#Process of Data at Migration at Quadrant

[blog2258]

Comprehensive Guide to Data Migration Process at Quadrant

Data migration is the process at Quadrant of transferring data from one system or storage solution to another. This process is crucial for organizations that are upgrading systems, moving to the cloud, or consolidating data centers. A well-planned data migration process ensures that data is accurately and efficiently moved, minimizing downtime and maintaining data integrity. Here’s a comprehensive overview of the data migration process: 

1. Planning and Assessment 

Requirements Gathering: Understand the purpose of the migration, the scope, and the desired outcomes. 

Current State Analysis: Assess the current data environment, including data types, volume, sources, and quality. 

Target Environment: Define the target environment’s specifications and constraints. 

Risk Assessment: Identify potential risks and develop mitigation strategies. 

Budgeting and Resources: Determine the budget and resources (personnel, tools, time) required. 

2. Design 

Data Mapping: Map the data fields from the source to the target system. 

Migration Strategy: Decide on the migration approach (big bang, phased, parallel running, etc.). 

Data Governance: Establish policies and procedures for data handling, security, and compliance. 

Tools and Technologies: Select appropriate data migration tools and technologies. 

3. Development 

Infrastructure Setup: Set up the necessary hardware and software infrastructure for the migration. 

Data Extraction: Develop scripts or use tools to extract data from the source system. 

Data Transformation: Develop the transformation logic to convert data into the format required by the target system. 

Loading Process: Develop the process to load transformed data into the target system. 

4. Testing 

Unit Testing: Test individual components of the migration process (e.g., extraction, transformation). 

System Testing: Test the entire migration process in a controlled environment. 

Data Verification: Verify the data in the target system against the source to ensure accuracy and completeness. 

Performance Testing: Ensure the migration process can handle the data volume within the required timeframes. 

5. Execution 

Pilot Migration: Conduct a pilot migration with a subset of data to identify any issues. 

Full Migration: Execute the full data migration process. 

Monitoring: Continuously monitor the migration process for any errors or performance issues. 

Issue Resolution: Address any issues that arise during the migration process promptly. 

6. Post-Migration 

Validation: Perform thorough validation to ensure all data has been accurately and completely migrated. 

Performance Tuning: Optimize the performance of the target system post-migration. 

User Acceptance Testing (UAT): Allow end-users to test the new system and confirm that it meets their requirements. 

Training and Documentation: Provide training for users and document the new system and processes. 

7. Maintenance 

Ongoing Support: Provide support to resolve any post-migration issues. 

Data Quality Monitoring: Implement ongoing data quality checks and monitoring. 

System Updates: Keep the new system updated and perform regular maintenance. 

Tools and Best Practices 

Automation Tools: Use data migration tools like Talend, Informatica, or Microsoft Azure Data Factory to automate and streamline the process. 

Data Quality Tools: Utilize data quality tools to ensure the integrity and quality of the data during migration. 

Backup and Recovery: Always have a backup and recovery plan to revert changes if something goes wrong. 

Communication Plan: Keep all stakeholders informed throughout the migration process. 

Incremental Migration: Where possible, migrate data incrementally to minimize risk and downtime. 

A successful data migration requires meticulous planning, rigorous testing, and thorough validation to ensure that the data is accurately transferred and the new system operates as expected. 

[chloedecker0]

Cloud Database Management Systems: A Comparison of Leading Providers

Choosing the Right Cloud DBMS: Insights into Services, Security, and Scalability 

A cloud database is a database management service or system built on and accessible via a cloud computing platform. It is like traditional database administration services, but the cloud serves as a distinction. 

Cloud databases, as opposed to traditional databases, provide greater flexibility, scalability, and security due to their cloud computing architecture. With an increasing number of businesses migrating their databases to the cloud, the market is saturated with cloud Database Management System (DBMS) providers. 

But, before we get into the services and features given by the top Cloud DBMS providers, let's first define a cloud database management system and its major components. 

Download the sample report of Market Share: https://quadrant-solutions.com/download-form/market-research/market-forecast-cloud-database-management-system-2024-2028-worldwide--2352 

What is Cloud Database Management Systems (DBMS)? 

The Cloud database management system is the most recent advancement in database management. It is a strong technology that enables safe, scalable, and efficient data storage, retrieval, and management. Distributed computing offers seamless data access and performance enhancement. It also fosters consistency in data management and speeds up common activities with its tool suite. 

5 key components of cloud database management 

Cloud database platforms are made up of a variety of components that work together to handle and manage data. The following are the major components of a cloud database management system: 

Ensuring storage integrity

Ensuring storage integrity in cloud databases entails protecting stored data. Firewall protection analyzes both incoming and outgoing website traffic. Furthermore, stored data must be encrypted, transforming it into an unidentifiable code and limiting access to authorized individuals. This combination of methods strengthens data security and integrity in cloud storage systems. 

Managing data access

While access to information is critical for employees, it must be managed. Business owners set access levels depending on work needs, ensuring that only authorized personnel have access to sensitive data. Authentication processes verify users before allowing access, which prevents unauthorized extraction. 

Documenting data transactions makes audits easier, allowing you to follow changes and, if necessary, attribute them to specific people. Furthermore, installing access controls searches devices for viruses and malware, hence increasing overall system security. 

Securing data transmission

Securing data transmission in cloud database systems entails creating a secure and encrypted route between users and their devices. This precautionary measure protects against interception and unauthorized access to transmitted data. Use firewalls and virtual private networks (VPNs) to protect data in transit. 

Validating arriving data

Validating received data in cloud database systems is critical to ensuring its integrity and authenticity. When staff members receive data, they should execute data integrity checks to ensure its accuracy and legitimacy. This strategy reduces the danger of infections and data breaches, such as phishing attempts, in which bad actors try to trick recipients into providing personal information or installing malware via forged emails. 

Backup and data recovery strategies

Despite precautions, unanticipated mishaps and cyber dangers can occur. Create consistent data backup techniques and effective recovery procedures to recover lost data quickly and accurately. Maintain backups on several cloud accounts to improve data recovery possibilities. 

Download the sample report of Market Forecast: https://quadrant-solutions.com/download-form/market-research/market-forecast-cloud-database-management-system-2024-2028-worldwide--2352 

Best Cloud Database Management Systems 

Alibaba Cloud 

Alibaba Cloud provides a comprehensive suite of cloud goods and services, including databases, networking, security, analytics, and more. The services include elastic compute, data storage, relational databases, big data processing, and content delivery networks (CDN). It is China's largest cloud computing corporation, and it is expanding its operations into Europe and the Americas. It provides cloud databases that enable OLTP, NoSQL, and OLAP. 

Amazon Web Services 

Amazon Web Services is the elephant in the room. Amazon's cloud service revenues have regularly climbed by double digits (usually 20 to 35 percent) year on year. AWS provides a wide range of computing services. This offers a wide range of fully managed database services. AWS provides more than 15 purpose-built engines to serve a wide range of data formats, including relational, key-value, in-memory, graph, and time-series database services. Furthermore, the organization highlights its excellent availability and security. It accomplishes this by enabling multi-region, multi-primary replication and offering complete data governance with different degrees of protection.  

BigQuery 

BigQuery is a fully managed enterprise data warehouse on Google Cloud that helps manage and analyze data using built-in technologies such as machine learning, geographic analysis, and business intelligence. It provides significant flexibility by isolating the compute engine (which analyzes data) from the storage. Companies can use BigQuery to store and analyze data, as well as to review data wherever it is stored. To change and manage data, developers and data scientists can use client libraries built on known programming languages like as Python, Java, JavaScript, and Go, as well as BigQuery's REST API and RPC API. ODBC and JDBC drivers enable interaction with current programs, including third-party tools and utilities. 

Cloudera 

Cloudera provides a hybrid data platform that combines secure data management with portable cloud-native data analytics to turn complex data into actionable insights. The Cloudera Data Platform (CDP) combines data management, analytics, transactional, and data science services from public and private clouds. It comprises CDP Data Hub, CDP DataFlow, CDP Data Engineering, CDP Operational Database, CDP Data Warehouse, and CDP Machine Learning. Its operational database-as-a-service provides Apache HBase users with greater ease of use and flexibility. 

Oracle 

Oracle provides a range of database services and products that are both cost-effective and high-performance versions of its Oracle Database. on addition, Oracle Autonomous Database, which is accessible on-premises or on the Oracle Cloud Infrastructure, is a managed, completely automated database service. It achieves great performance, availability, and security for OLTP, analytics, batch, and Internet of Things (IoT) workloads by automating tuning, scaling, and patching using machine learning technology. It offers a slew of other products that bring the Oracle database to new platforms or enable it to integrate with existing systems. Some of these services include Oracle Database Service for Microsoft Azure, the fully managed Oracle MySQL HeatWave powered by an integrated HeatWave in-memory query accelerator, the cloud document database service Oracle Autonomous JSON Database for developing JSON-centric apps, and more. 

Making the Right Decision  

Quadrant Knowledge Solutions Cloud Database Management Systems (CDBMS) market research includes a detailed global analysis of major vendors. The research comprises of vendors’ product features and functionalities, as well as competitive differentiating factors. The research provides competitive landscape and vendor analysis to enable service vendors to enhance their understanding of the market and implement a growth-oriented technology roadmap. 

Cloud Database Management Systems Market Share, 2022’ and ‘Worldwide Market Forecast: Cloud Database Management Systems, 2022-2027’ reports on cloud computing database management system market give insight into the present status of the industry and what to expect in future which helps companies make decisions about their data storage strategies. The 2022 Cloud Database Management Systems Market Share report comprehensively analyses key players in the market, enabling business organizations to identify potential partners and competitors. It also shows how much bigger the market is compared to others as well as its growth rate thus indicating that it will grow.  

  

Talk To Analyst: https://quadrant-solutions.com/talk-to-analyst 

On the other hand, in terms of yearly forecast for 2022 up to 2027; this encompasses an elaborate view of trends that will ensue during this period and factors driving these changes. It gives a clue about factors that cause increase of markets such as scalable storage solutions at lower cost. However, it also provides insights into challenges that may hinder its growth including concerns over data security and meeting regulatory requirements.  

  

These reports can help companies gain an understanding of how the enterprise performance management cloud market works so they can make strategic choices tailored towards their data storage needs and aspirations. They can identify emerging trends and opportunities, assess the competitive landscape, and make informed decisions about their investments in cloud computing database management system market. These reports are essential tools for businesses looking to stay ahead in the rapidly evolving data storage market.  

Wrapping-up 

Cloud databases have various advantages over traditional databases. Aside from increased agility and scalability, they can also assist firms reduce database management expenses. 

The choice of cloud DBMS will be determined by the enterprise's or individual users' requirements. Some databases may be appropriate for analytical purposes, while others may be better suited to internet-scale applications. With different use cases, enterprises are transitioning to multimodel cloud databases, which provide multiple data models within a single database.  

[technology098]

Maximizing Data Potential: The Power of Data Preparation Tools

Data preparation tools play a pivotal role in the realm of big data, catering to structured, unstructured, and semi-structured data environments. These tools come equipped with pre-built functionalities that effectively automate and streamline recurring processes. With collaborative interfaces for report generation and model construction, they ensure seamless operations in data management. Their primary objective is to facilitate the migration of top-quality data for analysis while promptly flagging any instances of data duplication, empowering users to take necessary corrective measures.

Key vendors of data analysis tools offer a plethora of capabilities, ranging from consolidating diverse data sources into cohesive datasets to employing AI-driven mechanisms for data and field identification within multi-structured documents. Automated extraction and classification of data, along with quality assessment, data discovery, and data lineage functionalities, are integral features provided by these leading tools. Moreover, they excel in rectifying imbalanced datasets by amalgamating internal and external data sources, generating new data fields, and eliminating outliers. The evolution of data preparation software is evident in the expansion towards cloud-based solutions and the augmentation of their capabilities to align with DataOps principles, facilitating the automation of data pipeline construction for business intelligence (BI) and analytics purposes.

Quadrant Knowledge Solutions emphasizes the significance of data preparation tools in enabling organizations to identify, cleanse, and transform raw datasets from diverse sources. These tools empower data professionals to conduct comprehensive analysis and derive valuable insights using machine learning (ML) algorithms and analytics tools. By streamlining processes such as data cleansing, validation, and transformation without necessitating human intervention or coding expertise, these tools expedite decision-making processes for businesses. They enable users to devote more time to data mining and analysis, thereby enhancing overall operational efficiency.

Prominent vendors in the data preparation software market include Modak Analytics, Oracle, Precisely, Quest, SAP, SAS, Talend, Tamr, TIBCO, and Yellowfin. These industry leaders offer robust solutions that cater to the diverse needs of modern enterprises. Their continuous innovation and commitment to enhancing data management capabilities contribute significantly to driving efficiency and fostering data-driven decision-making across various sectors.

In conclusion, data preparation tools serve as indispensable assets in the realm of big data, offering a wide array of functionalities to streamline data management processes. Their role in facilitating data migration, cleansing, and transformation cannot be overstated, especially in today's data-driven landscape. With advancements in technology and a growing emphasis on automation, these tools are poised to play an even more significant role in empowering organizations to harness the full potential of their data assets.

[govindhtech]

Dynatrace’ s seamless integration in Microsoft’s marketplace

Dynatrace in Microsoft’s Marketplace

In the current digital landscape, the cloud has evolved into an essential component for more than ninety percent of organizations. As a result of this widespread adoption, cloud portfolios are becoming increasingly complicated. This is because businesses are navigating the challenges of migrating on-premises environments, managing hybrid workloads, and modernizing their cloud estate, all while operating under the current business imperatives of speed and scale. In order to overcome this challenge, organizations need to equip themselves with cutting-edge tools that not only offer insightful analytics but also streamline processes that are performed manually.

Customers are able to construct intelligent applications and solutions while seamlessly unifying technology to simplify platform management and deliver innovations in an efficient and secure manner. Microsoft Azure is the cloud that is considered to be the most trusted cloud services in the world. Microsoft collaborates with partners like Dynatrace to bolster its customers’ ability to derive the greatest possible benefit from their cloud portfolio. According to the 2023 Gartner Magic Quadrant for Application Performance Monitoring and Observability, Dynatrace, which is a platform for analytics and automation that is powered by causal artificial intelligence, was granted the title of Leader.

The combination of Azure and Dynatrace is superior

Customers can simplify their information technology ecosystems at scale with Dynatrace, which allows them to respond more quickly to changes in market conditions. Customers are able to click-to-deploy and adjust investments based on usage thanks to the Azure Native Dynatrace Service, which makes the process of migrating and optimizing their cloud estate easier for businesses. Additionally, the solution is available through the Microsoft commercial marketplace, which makes it accessible to potential customers.

The Dynatrace Grail Data Lakehouse is the most recent cloud management innovation for Dynatrace, which was just recently introduced to the public. This cutting-edge technology is a component of the Azure Native Dynatrace Service, which was developed to effectively manage vast quantities and types of data across diverse cloud environments, including hybrid, multicloud, and cloud-native environments. With Grail, observability, security, and business data are all brought together while the crucial context is preserved. This allows for the delivery of accurate and efficient analytics in an instant, while also minimizing costs.

The ability of Grail to process a wide variety of logs and events without the burdens of indexing, re-indexing, or schema management is one of the most notable features of this distributed database management system. Consequently, the process is significantly simplified as a result of the elimination of the preliminary requirements of developing individualized scripts, tags, and indexes prior to the loading of data.

Each and every one of Azure’s services, such as Azure Spring Cloud, Azure Kubernetes Service, Azure Cosmos DB, Azure Linux, and Azure Functions, are completely compatible with the Dynatrace platform. Not only does this compatibility improve its functionality, but it also provides capabilities for monitoring in close proximity to real time.

AI-focused innovation with Dynatrace as the platform

Artificial intelligence is at the heart of the Dynatrace platform, which employs predictive, causal, and generative AI in conjunction with one another to provide root-cause analysis and deliver precise answers, intelligent automation, accurate predictions, and increased productivity. Customers will be able to accelerate innovation and maximize automation if they have the ability to leverage artificial intelligence and machine learning operations as IT environments continue to evolve.

By utilizing the Microsoft commercial marketplace, the time-to-value ratio can be decreased

The Dynatrace offerings are easily accessible through the Microsoft commercial marketplace, which streamlines the process from the acquisition to the implementation of the solution. Businesses that are looking for the best cloud solutions can simplify their journey by using Marketplace, which provides a user-friendly platform for discovery, purchase, trial, and deployment of cloud solutions. In this one-stop shop, customers have the opportunity to investigate and choose from a wide range of solutions that have been thoroughly evaluated, such as Dynatrace, which can be easily integrated with their existing technology stacks.

As IT budgets grow and cloud use grows, customers want better value from their investments. The Azure consumption commitment gives customers discounts on their cloud infrastructure after meeting spending thresholds.  Microsoft assists its customers in accomplishing more by automatically counting eligible marketplace purchases toward their Azure consumption commitment.

A significant number of Microsoft’s clients are increasingly turning to the marketplace as their primary resource for managing their cloud portfolios in an effective and self-assured manner, while also enabling customers to make purchases in the manner of their choosing. Whether it’s a digital purchase, a private offer from a solution provider like Dynatrace, or a deal procured by their channel partner with a multiparty private offer, the marketplace is able to accommodate a wide variety of purchasing scenarios.

In a recent transaction, the information technology strategy firm Trace3 purchased Dynatrace’s solution on behalf of their client by means of a multiparty private offer that was made available through the marketplace. The purchase was counted toward the customer’s Azure consumption commitment, and the customer received the benefits of simplified purchasing while getting more value from their cloud dollars. This was made possible by the fact that Dynatrace’s solution is eligible for Azure benefits.

Begin your journey with Dynatrace and the marketplace

Customers are able to achieve maximum value while gaining access to the essential solutions, such as those offered by Dynatrace, that they require to propel their business forward when they centralize their cloud investments through the Microsoft commercial marketplace. By the end of this year, the Dynatrace Grail Data Lakehouse, which is an essential component of the Azure Native Dynatrace Service, will be made available for purchase through the marketplace.

Read more on Govindhtech.com

[quadrantresource3]

Looking for reliable and customizable data migration solutions? Look no further than Quadrant Resource. Our process-driven approach ensures safe and seamless database migration, customized to your unique needs. Contact us today to learn more about how we can help you with your migration needs. 

[techcarrot-dubai]

How techcarrot can be the correct Salesforce Partner for you

Salesforce is the global leader in CRM and has been ranked the #1 CRM provider by International Data Corporation (IDC) for the eighth consecutive year. It has a 19.8% share of the CRM market which is more than its 4 leading competitors combined. More than 150,000 businesses use Salesforce. Salesforce has been recognized as the leader in the Gartner CRM Customer Engagement Center Magic Quadrant for the 13th year in a row.

Salesforce provides many customizable features, tools, and applications and helps in streamlining a company’s business operations and enabling growth. Companies using Salesforce have reported an average of 56% faster deployment, 27% rise in sales revenues, 32% increase in lead conversion, and 34% enhancement in customer satisfaction.

Salesforce implementation is the process of deployment of the Salesforce platform and its adoption in the company. It is a multi-stage process that not only involves the technical part but also includes data cleansing, app integrations, and employee training among others.

A salesforce implementation partner is an external company that helps in selecting and implementing the right solution as per the specific business requirements. Salesforce implementation services comprise configuration, customization, integration, migration, etc.

How does a Salesforce partner help?

There are many reasons why taking the help of a Salesforce implementation partner is essential:

·        Swift and efficient Salesforce implementation can be challenging as there are multiple parts and sub-parts needing configuration.

·        The implementation company can provide the correct Salesforce implementation without the involvement of complexities and risks.

·        The partner has extensive knowledge of the best practices required for smooth deployment.

·        Continuous strategic guidance is required to cater to the unique needs of an organization and make the business process efficient.

·        The partner can provide customized training to the organization’s employees in properly adapting to the technology.

How to select the right implementation partner

The major factors that need to be considered while selecting an implementation partner include:

Hiring a Salesforce specialist: Salesforce implementation is an expensive investment for any company and involves huge costs and risks. Hence, the selected partner should be a Salesforce specialist having deep expertise in the specific industry of the company instead of offering generic services across industries and products.

Providing scalable solutions: A successful implementation can be gauged by its long-term results for which scalability is a crucial aspect. The partner should be able to ensure scalability for business growth with strategic planning and provide guidance to navigate the business challenges.

Providing long-term engagement: For most customer-centric businesses Salesforce implementations are business-critical. Hence it is essential to engage with a partner on a long-term basis who can support the organization as per the evolving needs.

Offering system support on an ongoing basis: The partner should be in a position to tackle the dynamic environment and continuous changes in a company’s business by providing ongoing support. The support should not be limited to just solutions but must be extendable to include advisory services also in the future.

Engaging with techcarrot

techcarrot is a leading Salesforce implementation services provider based in Dubai. We provide Salesforce consulting and implementation services as per the specific business requirements of our clients. Our services include configuration, customization, integration, migration, and support across industries. Our cost-effective Salesforce implementation services are ideal for businesses of all sizes in the Middle East and across the world.

We have established a Salesforce Center of Excellence in our company. Our extensive domain expertise includes specialized industry knowledge and focused industry solutions. Our solutions and accelerators comprise the Property Sales Solution, Property Leasing Solution, Reusable code, Integrators, and Lightning components.

The Property Sales solution spans end-to-end from Pre-Sales, Sales Operations and Post Sales, to Finance, Inventory and Dashboards. The Property Leasing solution includes Accounting, Document generation, Customer Engagement, Marketing, Contract Management, Unit Management, and Dashboards.

If you are looking for Salesforce implementation services for your business, contact us to know how techcarrot can be the right partner for you.

[blog2258]

Continuous Improvement and Optimization Data Migration process at Q-Migrator at Quadrant 

Data migration process at Q-Migrator is not a one-time event; it often serves as an opportunity to enhance and optimize your data management practices. This phase focuses on continuous improvement and optimization to ensure long-term success and efficiency: 

Post-Migration Review: Conduct a thorough review of the migration process to identify lessons learned and areas for improvement. This includes analyzing any issues encountered, user feedback, and overall system performance. 

Performance Tuning: Optimize the performance of the new system by fine-tuning configurations, indexing strategies, and query optimizations. Regularly monitor system performance and make necessary adjustments. 

Data Quality Monitoring: Implement ongoing data quality monitoring to maintain the integrity and accuracy of your data. Use automated tools to detect and address data quality issues proactively. 

Data Lifecycle Management: Develop and implement policies for data lifecycle management, including data retention, archiving, and disposal. This ensures that your data remains relevant, accessible, and secure over time. 

Scalability Planning: Plan for future scalability to accommodate growth in data volume and user demand. Ensure that your infrastructure and processes can handle increased load without compromising performance. 

Continuous Training and Support: Provide ongoing training and support to users to help them adapt to changes and fully utilize the new system. Regularly update training materials and support resources to reflect any system enhancements. 

Feedback Loops: Establish continuous feedback loops with users and stakeholders to gather insights and make iterative improvements. Use this feedback to refine processes, enhance user experience, and address emerging needs. 

Conclusion 

Continuous improvement and optimization are vital for maximizing the long-term benefits of your data migration project. By regularly reviewing and enhancing your processes, you can ensure sustained performance, data quality, and user satisfaction. Embrace a culture of continuous improvement to keep your data management practices aligned with evolving business needs and technological advancements. 

[chloedecker0]

Cloud Database Management Systems: Cost-Effective Solutions for Startups

Choosing the Right Cloud DBMS: Insights into Services, Security, and Scalability 

A cloud database is a database management service or system built on and accessible via a cloud computing platform. It is like traditional database administration services, but the cloud serves as a distinction. 

Cloud databases, as opposed to traditional databases, provide greater flexibility, scalability, and security due to their cloud computing architecture. With an increasing number of businesses migrating their databases to the cloud, the market is saturated with cloud Database Management System (DBMS) providers. 

But, before we get into the services and features given by the top Cloud DBMS providers, let's first define a cloud database management system and its major components. 

Download the sample report of Market Share: https://quadrant-solutions.com/download-form/market-research/market-forecast-cloud-database-managment-systems-2022-2027-worldwide-2156 

What is Cloud Database Management Systems (DBMS)? 

The Cloud database management system is the most recent advancement in database management. It is a strong technology that enables safe, scalable, and efficient data storage, retrieval, and management. Distributed computing offers seamless data access and performance enhancement. It also fosters consistency in data management and speeds up common activities with its tool suite. 

5 key components of cloud database management 

Cloud database platforms are made up of a variety of components that work together to handle and manage data. The following are the major components of a cloud database management system: 

Ensuring storage integrity

Ensuring storage integrity in cloud databases entails protecting stored data. Firewall protection analyzes both incoming and outgoing website traffic. Furthermore, stored data must be encrypted, transforming it into an unidentifiable code and limiting access to authorized individuals. This combination of methods strengthens data security and integrity in cloud storage systems. 

Managing data access

While access to information is critical for employees, it must be managed. Business owners set access levels depending on work needs, ensuring that only authorized personnel have access to sensitive data. Authentication processes verify users before allowing access, which prevents unauthorized extraction. 

Documenting data transactions makes audits easier, allowing you to follow changes and, if necessary, attribute them to specific people. Furthermore, installing access controls searches devices for viruses and malware, hence increasing overall system security. 

Securing data transmission

Securing data transmission in cloud database systems entails creating a secure and encrypted route between users and their devices. This precautionary measure protects against interception and unauthorized access to transmitted data. Use firewalls and virtual private networks (VPNs) to protect data in transit. 

Validating arriving data

Validating received data in cloud database systems is critical to ensuring its integrity and authenticity. When staff members receive data, they should execute data integrity checks to ensure its accuracy and legitimacy. This strategy reduces the danger of infections and data breaches, such as phishing attempts, in which bad actors try to trick recipients into providing personal information or installing malware via forged emails. 

Backup and data recovery strategies

Despite precautions, unanticipated mishaps and cyber dangers can occur. Create consistent data backup techniques and effective recovery procedures to recover lost data quickly and accurately. Maintain backups on several cloud accounts to improve data recovery possibilities. 

Download the sample report of Market Forecast: https://quadrant-solutions.com/download-form/market-research/market-forecast-cloud-database-managment-systems-2022-2027-worldwide-2156 

Best Cloud Database Management Systems 

Alibaba Cloud 

Alibaba Cloud provides a comprehensive suite of cloud goods and services, including databases, networking, security, analytics, and more. The services include elastic compute, data storage, relational databases, big data processing, and content delivery networks (CDN). It is China's largest cloud computing corporation, and it is expanding its operations into Europe and the Americas. It provides cloud databases that enable OLTP, NoSQL, and OLAP. 

Amazon Web Services 

Amazon Web Services is the elephant in the room. Amazon's cloud service revenues have regularly climbed by double digits (usually 20 to 35 percent) year on year. AWS provides a wide range of computing services. This offers a wide range of fully managed database services. AWS provides more than 15 purpose-built engines to serve a wide range of data formats, including relational, key-value, in-memory, graph, and time-series database services. Furthermore, the organization highlights its excellent availability and security. It accomplishes this by enabling multi-region, multi-primary replication and offering complete data governance with different degrees of protection.  

BigQuery 

BigQuery is a fully managed enterprise data warehouse on Google Cloud that helps manage and analyze data using built-in technologies such as machine learning, geographic analysis, and business intelligence. It provides significant flexibility by isolating the compute engine (which analyzes data) from the storage. Companies can use BigQuery to store and analyze data, as well as to review data wherever it is stored. To change and manage data, developers and data scientists can use client libraries built on known programming languages like as Python, Java, JavaScript, and Go, as well as BigQuery's REST API and RPC API. ODBC and JDBC drivers enable interaction with current programs, including third-party tools and utilities. 

Cloudera 

Cloudera provides a hybrid data platform that combines secure data management with portable cloud-native data analytics to turn complex data into actionable insights. The Cloudera Data Platform (CDP) combines data management, analytics, transactional, and data science services from public and private clouds. It comprises CDP Data Hub, CDP DataFlow, CDP Data Engineering, CDP Operational Database, CDP Data Warehouse, and CDP Machine Learning. Its operational database-as-a-service provides Apache HBase users with greater ease of use and flexibility. 

Oracle 

Oracle provides a range of database services and products that are both cost-effective and high-performance versions of its Oracle Database. on addition, Oracle Autonomous Database, which is accessible on-premises or on the Oracle Cloud Infrastructure, is a managed, completely automated database service. It achieves great performance, availability, and security for OLTP, analytics, batch, and Internet of Things (IoT) workloads by automating tuning, scaling, and patching using machine learning technology. It offers a slew of other products that bring the Oracle database to new platforms or enable it to integrate with existing systems. Some of these services include Oracle Database Service for Microsoft Azure, the fully managed Oracle MySQL HeatWave powered by an integrated HeatWave in-memory query accelerator, the cloud document database service Oracle Autonomous JSON Database for developing JSON-centric apps, and more. 

Making the Right Decision  

Quadrant Knowledge Solutions Cloud Database Management Systems (CDBMS) market research includes a detailed global analysis of major vendors. The research comprises of vendors’ product features and functionalities, as well as competitive differentiating factors. The research provides competitive landscape and vendor analysis to enable service vendors to enhance their understanding of the market and implement a growth-oriented technology roadmap. 

Cloud Database Management Systems Market Share, 2022’ and ‘Worldwide Market Forecast: Cloud Database Management Systems, 2022-2027’ reports on cloud computing database management system market give insight into the present status of the industry and what to expect in future which helps companies make decisions about their data storage strategies. The 2022 Cloud Database Management Systems Market Share report comprehensively analyses key players in the market, enabling business organizations to identify potential partners and competitors. It also shows how much bigger the market is compared to others as well as its growth rate thus indicating that it will grow.  

  

Talk To Analyst: https://quadrant-solutions.com/talk-to-analyst 

On the other hand, in terms of yearly forecast for 2022 up to 2027; this encompasses an elaborate view of trends that will ensue during this period and factors driving these changes. It gives a clue about factors that cause increase of markets such as scalable storage solutions at lower cost. However, it also provides insights into challenges that may hinder its growth including concerns over data security and meeting regulatory requirements.  

  

These reports can help companies gain an understanding of how the enterprise performance management cloud market works so they can make strategic choices tailored towards their data storage needs and aspirations. They can identify emerging trends and opportunities, assess the competitive landscape, and make informed decisions about their investments in cloud computing database management system market. These reports are essential tools for businesses looking to stay ahead in the rapidly evolving data storage market.  

Wrapping-up 

Cloud databases have various advantages over traditional databases. Aside from increased agility and scalability, they can also assist firms reduce database management expenses. 

The choice of cloud DBMS will be determined by the enterprise's or individual users' requirements. Some databases may be appropriate for analytical purposes, while others may be better suited to internet-scale applications. With different use cases, enterprises are transitioning to multimodel cloud databases, which provide multiple data models within a single database.  

[technology098]

Unlocking Data Potential: Mastering Business Insights with Master Data Management

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[annieboltonworld]

Juniper Publishers-Open Access Journal of Environmental Sciences & Natural Resources

Impact of Sustained Discharge of Treated Wastewater Effluent on Wetland Water Quality

Authored by Roger Saint Fort

Abstract

This study investigated the impact on water quality of sustained discharge of treated wastewater effluent on the wetland commonly known as Weed Lake. A field sampling program was conducted during the months of May through September. Various physical, chemical, and biological analyses were performed on retrieved water samples. EC and TDS values increased both spatially and temporally from the south to the north quadrant of the wetland. Similar patterns were also observed for HPC and coliforms CFU enumeration. Turbidity and TSS decreased from May to June and then surged slightly while typically remaining constant from June to September. COD and TKN were found to decrease from May to July and to increase marginally from August to September. Potentially mineralizable nitrogen and phosphorous were ascertained as indices of the wetland sediment capacity for mineralizing both nutrients. Batch isotherms of PO4-3 were conducted for interaction determination with the wetland sediments.

The average value and standard deviation of potentially mineralizable nitrogen for the sediment samples was 15±12.92 mg/kg. Potentially mineralizable P was estimated at 29.67±11.96 mg/kg. The nitrogen pool of NH3-N in the wetland ecosystem was double the NO3-N pool. Langmuir data indicated an average maximum sorbing capacity of 190mg of PO4-3per kg of substrate. The value constants of maximum sorbing capacity ranged from 100 to 294mg of PO4-3 per kg of substrate. Total phosphorus concentration increased significantly from June to July and then decreased in September, these concentration changes were in parallel with algal and plankton blooms. Mass balance analysis indicated that 40 to 80% of phosphorous in Weed Lake is in various complex forms and not readily bioavailable. Labile-P and soluble-P represent a smaller fraction of the total-P. It appears that uncontrollable natural factors will have episodic and direct influence from year to year on the speciation of phosphorous and nitrogen in the wetland and influence also the spatial-temporal relationship status of its water quality. As a natural dynamic ecosystem, implementation and evaluation of best management practices should be continued together with subsequent evaluation.

Keywords: Water quality; Wastewater; Nutrient; Isotherm; Mass balance; Algae

Introduction

Weed Lake is a 6 km2 surface area of historic wetland and highly valued as an important natural feature within the community. Hereafter referred to as Weed Lake or wetland, its orientation is a south-north direction. The subject site is located 20 km east of the City of Calgary and northeast of the Hamlet of Langdon within the Municipality District (MD) of Rocky View No. 44 (Figure 1). Glenmore Trail and TransCanada Highway represent important access roads along the southeastern corner and the northwestern boundary of the Hamlet. Weed Lake was previously a healthy functioning wetland ecosystem characterized by important waterfowl production, breeding habitat and staging area for a large number of shorebirds in transit during migration. The wetland was drained in 1971. However, because of a variety of soil fertility problems, the expected agricultural benefits of draining the original wetland were never achieved.

A Restoration and Rehabilitation Program were initiated in 2006 to restore Weed Lake as a dynamic and fully functioning wetland ecosystem. Currently, Weed Lake is a productive ecological aquatic environment that hosts a variety of aquatic plants, namely: emergent aquatic vegetation, floating leaved plants, submergent aquatic plants and free floating plants. The climate of the area is described as being largely sub-continental characterized by short and moderately warm summers, brief spring and fall seasons. The winter is rather long. The ambient temperature typically ranges between -350C and 250C. Average annual precipitation is approximately 55 cm. The surrounding land uses around the wetland is primarily agricultural comprising mainly of farmstead parcels and un-fragmented quarter sections [1].

The soils of Weed Lake were classified as Rego Gleysols and the major portion of the upland as Solonetznic soils [2]. These soils, while high in soluble salts and sodium, are predominantly heavy glacial tills to clay loam. Soils in the dipressional areas are classified as Rego Gleysols and were developed in fine textured, lacustrine sediments. Weed Lake area is generally flat consisting of sections sloping gently from the west toward the lake at an approximate rate of 0.25%. The upland landscape is gently undulating. Two major depressional areas exist in the center of each SE and NW portions of Weed Lake TP 23 R27 W4. In some areas, the banks of Weed Lake provide a sudden and discernible drop in surface topography. Weed Lake received a constant source of tertiary wastewater effluent influxes from the Hamlet's wastewater treatment facility during the spring, summer and fall seasons. The wetland shore line is relatively flat with the lake depth being less than 1.20 meter over much of its area. Typical annual water level varies from 76 to 55 cm.

Storm water from the Hamlet flows into the lake through a network of major and minor ditch systems. Influx of storm water from the Balzac area also flows into Weed Lake and is routed through the main drainage ditch through the Hamlet. The former may carry considerable amount of sediments and salts, especially in the spring season. Irrigation water is in some instances supplemented to stabilize the wetland water level during periods of drought. Flows out of the wetland are controlled by an outlet located east of the wetland and is mainly drained by Rosebud Creek. The main objectives of this study were to ascertain Weed Lake water quality through the months of May to September, and to gain insights into the relationship between basic physicochemical and biological processes. Hence, the impact on water quality of sustained discharge of treated effluent in Weed Lake can be scientifically evaluated.

Materials and Methods

Weed Lake Surface Water Sampling

The wetland was sampled once a month through the months of May to September. Representative grab samples were retrieved from five sampling locations and their respective global positioning system (GPS) coordinates recorded. Subsequent sampling of the wetland was achieved based on each sampling location precise and unique identifier. All water samples were collected in one liter sterilized glass bottle at depth of 12 cm below the surface water level. The process involved lowering the capped bottle into the water column at the desired depth. The cap was then removed to allow water collection. Once filled, the bottle was capped, brought to surface and appropriately labelled. The water samples were then transported in Calgary and kept refrigerated at 70C for subsequent chemical and physical analysis. Field measurements of water temperature, dissolved oxygen (DO), and water level were performed at each sampling location at sampling time.

The remaining physical, chemical, and biological parameters were performed in the laboratory. Water samples for bacteriological analyses were collected from each sampling location in labeled, 50 mL sterile containers and brought back to Calgary. The sterile containers were transferred into a cooler for transportation. The cooler temperature was maintained at 50C. The samples were then serially diluted and incubated within 3 hours after sampling.

Weed Lake Potentially Mineralizable -Nitrogen (PMN) and -Phosphorous (PMP)

The process involved placing 10 g of air-dry substrate into a 150 mL acid washed Pyrex flask to which 25mL of 0.01mL CaCl2 was added [3]. The suspension in the flask was gently mixed, weighed, capped with Al foil and placed in the autoclave overnight at 1210C and 1500 kPa. Then, the flask and its contents were allowed to cool to room temperature, weighed, and quantitatively adjusted with deionized water. The mixture was transferred in centrifuge tubes and spun for 4 minutes at 4500 RPM and the liquid filtered through Whatman No. 2 filter paper. The difference between the autoclave treatment and the NH3-N, NO3-N and PO43 initially present in the substrate indicates the NH3-N, NO3-N and PO4-3 produced by autoclaving.

Weed Lake Batch Isotherm of Orthophosphate

To carry out the isotherm study, six sediment samples were collected from the bottom of the wetland using a hand Dutch auger. The substrates were transferred in one liter wide mouth glass jar, accordingly labeled and stored in the fridge at 40C. Prior to initiate the batch isotherm study, the samples were allowed to equilibrate with the room temperature. Batch testing was used to obtain the equilibrium sorption capacity of a given sediment sample for a corresponding concentration of phosphorous (PO43). This was achieved by developing equilibrium isotherms that describe the sorption capacity of the sediment exposed to various concentrations of aqueous solutions of PO43. Batch studies were conducted by gently mixing a specific amount of the sediment substrate with a specific volume of PO4'3 solution. The latter was prepared with deionized water. Each sorbate/sorbent system was then allowed to equilibrate overnight at room temperature and 80C, respectively. Following equilibration, the suspension was centrifuged at 5000 RPM for 4 minutes.

The supernatant from each system was retrieved and analyzed for the construction of the isotherms. The extent of PO4'3 sorption was estimated as the difference between initial concentration of PO4'3 and final concentration at equilibrium. Corrections were made for PO43 present in the original substrate.

Weed Lake Material Balance Analysis

Several assumptions were made in assessing Weed Lake, being a dynamic system with finite capacity to cope with anthropogenic inputs which can affect its intrinsic quality. A steady state equilibrium conditions exists between precipitation and evaporation,

a) A steady state equilibrium conditions exists between upward capillary groundwater movement and seeping water,

b) Outgoing water released through the gate doesn’t represent a significant enough volume of water to disturb the overall equilibrium status of Weed Lake under current water management,

c) Incoming water from any tributary doesn’t represent a significant enough volume of water to disturb the overall equilibrium status of Weed Lake,

d) The rate of organic matter decay in relation to nutrient mineralization are in equilibrium interaction with uptake by the aquatic vegetation and growth dynamics,

e) Lateral averaging assumes longitudinal and vertical hydrodynamics variations in velocities, constituents, water density, and temperature are negligible,

f) Sediment oxygen demand (SOD) is coupled to the water column dissolved oxygen.

Water quality interactions are by necessity simplified for descriptions of most aquatic ecosystems that are intrinsically complex. Hence, Weed Lake can be scientifically analyzed with considerable flexibility as a steady-state conservative system without any potential technical limitations of great significance. Based on Weed lake geometry and boundary, the major perturbations affecting the Lake hydrodynamics and quality during the investigated months of May through September are created primarily by wind and temperature induced circulation. In that regard, to a lesser extent by hydraulically induced turbulence and circulation. Wind causes Weed lake water column to be mixed through circulatory motion that extends to the bottom. This echoes the technical sentiment that complete water mixing occurs in the wetland boundary within a timeframe of 10 days. As a result, temperature differential between surface and bottom heat exchange is therefore considered conservative.

Analytical Program

*Not applicable; “Chemical oxygen demand; bTotal suspended solids; cTotal dissolved solids; "Dissolved oxygen; eSodium absorption ratio = {[Na/([Ca + Mg]/2)1/2}

The analytical testing program along with the associated quality assurance and control (QA/QC) for the study are depicted in Table 1. Standard recommended holding times were followed for the samples. Bacteriological analyses were performed on water samples collected in sterile jars. Serial dilutions were accordingly performed on the water samples. Subsequently, the diluted samples were incubated using the recommended Millipore media pad for heterotrophic plate count (HPC) and Escherichia coli (E. Coli) & Coliform organisms. The number of colony forming units (CFU) were enumerated and recorded.

Results and Discussions

Weed Lake Potentially Mineralizable Nitrogen (PMN) and Phosphorous (PMP)

The results of PMN as an index of potential mineralization of organic nitrogen to inorganic nitrogen forms of NH3-N or NO3-N are reported in Table 2. Reactive P initial present in these substrates were also evaluated and are reported in Table 3. The data indicate that the substrate found at the bottom of Weed Lake has the potential to contribute to the inorganic pools of N that is present in the water column. Total Kjeldahl nitrogen (TKN) which refers to the combination of ammonia and organic nitrogen ranged from 30 to 1.97mg/L likely contributes significantly to the mineralizable N pool. The average value and standard deviation of PMN for the six samples were 15±12.92 mg/kg while potentially mineralizable P was estimated at 29.67± 11.96mg/kg. Ratio of NH3-N/NO3-N was calculated for each autoclaved substrate and was found to oscillate around a ratio value of 2.

NA=Not applicable.

Therefore, the potential reserve of NH3-N in the wetland pool of PMN and PMP will vary with temperature, plants density, ecosystem is twice more than NO3-N. The dynamic and fate ofthat dissolved oxygen level, water level. Degree of hydrodynamicmixing of mineralized N is expected to be primarily driven by wind conditions. The results further indicate the presence of a pool of PO4'3 in the bottom boundary of the wetland. That pool is relatively bio-available for uptake by the aquatic plants that comprise Weed Lake ecosystem.

Weed Lake Batch Isotherm Study of Orthophosphate

Sorption isotherms are normally obtained by measuring the amount of solute sorbed for a number of different concentrations of sorbate under specific conditions. They often can be described by the Freundlich or Langmuir equations. The Freundlich isotherm, a commonly used curvilinear model has no upper limit to the amount of sorbate that could be sorbed by a system. The Freundlich equation, Equation (1) can be written as:

where X/M is the quantity of PO4'3 sorbed per unit mass of sediment (mg/kg), Ceq is the equilibrium concentration of PO4'3 in the solution (mg/L) and where Kd and n are the constants. The logarithmic form of the Freundlich equation to plot the data is shown in Equation (2):

The logarithm of the concentration of the solute in the sorbate state, X/M in mg/kg, is plotted as a function of the logarithm of the residual solute concentration, Ceq in mg/L. Linear regression of the data points yields a best-fit line with a slope of 1/n and intercept of log [Kd]. The slope, 1/n, is a measure of sorption intensity, and the Kd value, which must be determined by taking the antilog of the intercept, is the partition coefficient, an indicator of the sorptive capacity of the system. A Langmuir plot models a system where there are a finite number sorption sites. The Langmuir equation, Equation (3) may be expressed as follows:

Where X/M and C are the same units as defined above, bis a constant related to the binding energy (L/kg), and a is the maximum amount of solute that can be sorbed by the sorbate, is the soil uptake quantity (mg/Kg). The Langmuir equation can be rewritten in the following linear form shown in Equation (4):

[Ceq]/[X/M] can be plotted as a function of Ceq. The linear regression of the data points yields a best-fit line with a slope of 1/a and 1/ab as the intercept. The maximum amount of PO4'3 that can be sorbed onto the samples can be calculated from the Langmuir linear equation.

The isotherm models coefficients for the representative equilibrium studies are summarized in Table 3. The regression equations and the corresponding coefficient of determination (R2) are given in Table 3. It appears that the P sorption data of solution could be best described mathematically by the Langmuir equation. In this current case, the Langmuir data indicate that the Weed Lake sediment has an average maximum sorbing capacity of 190 mg of PO43 per kg of substrate. The value constants of maximum sorbing capacity ranged from 100 to 294 of PO4'3 per kg of substrate which reflect the heterogeneity in the composition of the sediment substrate. Therefore, the sediment substrate serves as a significant sink for PO4'3 as long as the sorbing capacity is not exceeded nor the pH becomes basic. The latter case has not been observed throughout the duration of the monitoring program.

Weed Lake Water Monitoring

The trend in water quality status of the wetland was compiled in Table 4 in a comparative summary as average values for the parameters tested for years 2009 to 2016, respectively. Under the objectives of this research project, the parameters were compared to current Surface Water Quality Guidelines for Use in Alberta with respect to Freshwater Aquatic Life for long term [4]. The guidelines are numerical concentrations or narrative statements recommended to support and maintain a designated water body. The wetland was scientifically analyzed with flexibility as a steady-state conservative system with limited potential technical limitations of meaningful significance. Based on its geometry and boundary, it is deduced that the major perturbations affecting the hydrodynamics and water quality during the investigated months are created primarily by wind and temperature-induced circulation and to a lesser extent by hydraulically induced turbulence and circulation.

aTurbidity Guideline; bTSS Guideline; cDO Guideline; dLake Temperature Guideline.

Wind causes water column mixing through circulatory motion that likely extends to the lower boundary of the water column. Complete mixing of the wetland water is conceived to occur within a time frame of ten days. As a result, temperature differential between surface and bottom heat exchange is considered conservative as well as spatially throughout the water mass. Observed temperature difference, AT, is less than 20C, both in the water column and spatially, irrespective of the sampling month. Temperature differential between surface and bottom heat exchange is considered conservative [5]. As expected, the importance of hydraulic, thermally, and wind- induced circulation in active interactions and combinations in bringing about dynamic changes in Weed Lake water quality varies depending on the specific parameters. Analysis of water quality indicators show that some parameters are more affected in varying degree in their distribution pattern with respect to spatial and seasonal variations.

Data spatial variability within a sampling event appears not to be significantly different from one sampling point to the next. Annual recorded chemical and physical parameters for grab samples tend to marginally increase spatially and temporally with corresponding standard errors ranging from 1.3% to 15%. This supports the assumption that complete mixing of Weed Lake water column is enhanced through wind induced circulation patterns which negate the geometry effects in inducing intrinsic spatial variability. Biological parameters of HPC-total and E. coli & Coliform colony forming unit (CFU) enumeration tend to decrease from the south to the north quadrant of the wetland. The south quadrant is where the pipe discharges treated effluent from the wastewater plant into the wetland.

Enumeration of CFU for both biological parameters spatially appear to vary randomly, hence no discernible pattern could be denoted. However, the level of CFU enumeration tends to increase with temperature. The lowest level of bacteria and coliform organisms are typically denoted for the months of May and September. CEC and TDS values tend to increase both spatially and temporally from the south to the north quadrant. This could be best attributed to influxes of storm water diverted into the wetland. Turbidity and TSS generally decreased from May to June and then marginally increased to remain constant from June to September. COD and TKN typically decreased from May to July but slightly increased from August to September. The trend observed in relation to turbidity, TSS, COD and TKN clearly reflects the combining effects of hydrodynamic mixing with a high degree of photosynthesis and metabolic synergistic relationships between algae and bacteria.

Furthermore, when ascertaining the trend in the data for the average parameters from years 2009 to 2016 (Table 5), a pronounced difference can be observed. Both spatial and temporal variations can be best corroborated to operational changes, natural factors, and seasonal variations. These factors will interact to accordingly create new episodic dynamic equilibrium in the wetland functional systems. According to Table 5, the trend in values for pH, DO, and water temperature remained the most relatively stable as denoted by their respective standard deviation and standard error. The overall stability of the pH is also a good indication of the wetland buffering capacity. Functionally, the ability to resist change in pH can be attributed to the presence of CO2, CO3-2, HCO3- and organic matter whose origins are primarily of natural sources and from operational activities (i.e., wastewater effluents) to a lesser degree. Given the presence of decomposable organic materials at the bottom of the wetland, the following reduction processes may be occurring. They may naturally further sulfate-enrich the water chemistry and contributing to the buffering capacity as depicted in Equation (5) and Equation (6):

The presence of dissolved oxygen is of fundamental importance in maintaining aquatic life and the aesthetic quality of Weed Lake. Because of this importance, oxygen can be regarded as one of the most important water quality parameters.The impact is measured as oxygen demand, a parameter that can be interpreted as a gross measure of the concentration of oxidizable materials present in the water column and as a status of potential organic load. Through the metabolic action of bacteria, organic material (OM) in the water column is oxidized to its lowest energy state through the following mechanisms, Equation (7):

Dissolved oxygen profile in the water column tends to increase from the months of May to June (representative average 8.94 mg/L) and then drastically decrease during July and August (representative average 4.07 mg/L) to subsequently route to a marginal increase in September (representative average 6.42 mg/L). No stratification of dissolved oxygen was denoted in the water column during any of the monitoring programs. Dissolved oxygen at the bottom-water interface was totally lacking, therefore making it an anaerobic, organic matter rich boundary in the wetland. Above the anaerobic substrate layer, an intermediate transition zone was not identified which contained dissolved oxygen. Following episodic discharge of treated wastewater effluent, its mixing through the water column, and after some time has elapsed, several changes occurred.

The various inter and intra relationships between wind action, temperature, precipitation, and sunlight are germane to the wetland intrinsic stabilization processes. In that regard, these factors are natural and not controllable. Based on dissolved oxygen readings, stabilization reactions appear to be aerobically dominated. During the stabilization processes, much of the biodegradable organic matter pool is transformed by bacteria into living organic matter. As a by-product of their metabolism, they release through mineralization into the water column CO2, nitrates, phosphates, sulfates, and other mineral salts. A conceptual framework depicted in Figure 2 provides a fundamental representation of physicochemical and biological dynamics occurring in the wetland. Such conceptual model would aid in long-term nutrient mass balance monitoring and developing a better understanding of nutrient systems behavior.

Aerobic stabilization is highly influenced by the hydrodynamic mixing created by wind action, temperature, and sunlight. However, at lower temperature like during the months of April, May, and October, the overall biological activities and therefore, the stabilization processes in Weed Lake are slower. Consequently, the algae level remains relatively low. As temperature of the wetland water column increases, this triggers in response higher algae metabolic activity. Under the additional favorable natural conditions of peak solar radiation during the months of June and July, algae proliferate. They absorb light and use the mineralized by-products during this highly photosynthetic period. Their cellular material releases oxygen in the water column, often giving rise to supersaturated conditions in some instances. Furthermore, the combination of high temperature, dissolved oxygen and solar radiation as well as the quick stimulation of bacterial activity enhance oxygen consumption.

This therefore stimulates blue-green algae (Cyanophyceae) to grow exponentially relative to the more efficient green algae (Chlorophyceae). As a result, heavy algal blooms are observed in the wetland with thick green mats appearing on the water surface. From this point on, anaerobic conditions arise, the column water is typically turbid and objectionable anaerobic odours are produced and released. The month of August typically produces anaerobic conditions as evidenced by dissolved oxygen average concentration of 2.73 mg/L. Since algal oxygen production is a direct function of photosynthesis, a gradual decrease in water temperature will be accompanied by decreased algal activity and therefore lower oxygen production. Consequently, the floating green mats on the water surface impair light penetration. Consequently, decreased photosynthesis in deeper portions of the water column results in decreased oxygen production together with a decrease in algal activity.

Under these conditions, wind action doesn't mix the water mass sufficiently to transport oxygen from the surface to the lower layers. At this point, the algal mass starts dying off, decaying through induced microbial decomposition, which results in the settling of biomass to the bottom boundary. This leads to the development of an organic layer of substrate that works as an anaerobic digester. It is noteworthy that this chain of events represents the natural ecological order and stabilization processes in the wetland. The COD test was used to ascertain the organic carbon content. Unlike biological oxygen demand (BOD), the COD test does not differentiate between biologically oxidizable and inert organic matter. Based on the overall trend in the data analysis, COD values tend to decrease from May to June as well as July but the COD values then increase or decrease from June to July and tend to increase during the months of August to September.

Typically lower DO readings are observed in July and August because of the past presence of higher level of oxidizable materials in the water column. Minimal DO stratification was observed throughout the depth of Weed Lake indicating significant longitudinal and vertical hydrodynamics mixing in the water column. Anoxic characteristic of the wetland in May results in some degree of objectionable odor as a result of bacterial reduction of NO3-, SO42, NH3-N, and other products of bacterial growth. Algae growth was more abundant during the months of May to July but significantly decreased during the months of August and September. Depicted in Tables 6-10 is the ratio of total-P/NH3-N for the wetland water column and treated effluent as well as the ratio for PO43/NH3-N for selected representative years. The ratios can be used as an important determining factor in assessing on an ongoing basis the management of the water quality with respect to effluent influxes and episodic water release from Weed Lake.

Main Nutrients Budget Analysis

The wetland aquatic environment is the host for a variety of aquatic plants namely emergent aquatic vegetation, floating leaved plants, submergent aquatic plants and free floating plants. The months of May to September mark the periods of active microbial and aquatic growth in Weed Lake. The data indicate that in order to maintain proper water quality and managing algal bloom as well as potential offensive odor, particularly for the critical months of July and August, it would be prudent to maintain a treated effluent discharge with a ratio of total-P/ NH3-N around 0.55. However, an effluent ratio of total-P/ NH3-N between 0.55 and 1.45 should be effectively adequate in maintaining proper water quality of the wetland.

A significant improvement in the wastewater treated effluent quality has been observed from the data recorded between the years 2010 to 2014. The results indicate for the year 2014 a treated effluent ratio of total-P/NH3-N ranging from 0. 05 to 0.74 and a water column ratio of PO43/NH3-N varying from 0.14 to 0.23. With respect to year 2010, the treated effluent ratio of PO43/NH3-N oscillated between 0.13 and 1.22 while the water column ratio of PO4-3/NH3-N varying from 0.26 to 1.02.

Variations in those ratio values were the norm for the other comprising monitoring years. Consequently, the water column ratio of total-P/NH3-N and PO4-3/NH3-N significantly improved from 2010 to 2014. The water column ratio of total-P/NH3-N in 2014 was established to range from 0.30 to 1.88 while it was from 1.07 to 2.68 in 2010. Furthermore, in 2014, the water column ratio of PO43/NH3-N ranged from 0.14 to 0. 23 compared to year 2010 in which case it ranged from 0.26 to 1.02 (Table 7).

In a manner similar to nitrogen, phosphorous in the wetland is expected to cycle between organic and inorganic forms. However, phosphorous unlike nitrogen doesn't cycle as a gas. It has to be assumed that labile-P, the not so strongly sorbed intermediate form by the wetland sediment, represents a smaller fraction of total-P. It may enter in equilibrium with soluble-P to increase the level of orthophosphate in the wetland water column. Hence, the material balance constructed for phosphorous is orthophosphate and will be considered as the only form of phosphorus that is readily used by any aquatic plants and microorganisms in the wetland ecosystem. Therefore, significant amount of orthophosphate is removed through algal and macrophytes growth but more significantly during the months of May, June and July. As result keeping orthophosphate concentration relative low throughout the wetland ecosystem boundary.

Concurrently, conversion of dissolved forms of phosphorus into insoluble forms such as calcium phosphate Ca3(HPO4)2, magnesium phosphate Mg3(HPO4)2, and ferric phosphate FePO4 and AlPO4 complexes will contribute to the removal and control of PO43 concentration level as well as to controlling its buffering capacity. Additionally, phosphorous bioavailability may be significantly affected through organic complex formation with humic and fulvic acids. Given the water pH being greater than 8.50, assumptions must also be made regarding the activities and contribution of Al retention of PO43 onto the sediment as AlPO4 complex. The later complex will be closely related to the contents of acid-extractable Al+3 in the sediment. Total phosphorus (i.e., P-inorganic and P-organic) concentration increased significantly from June to July to decrease in September which parallels algal and plankton blooms (Table 8).

Weed Lake Material Balance Analysis

A simple water budget approach was used to analyze the water level dynamics in Weed Lake Equation (8):

A simple water budget approach was used to analyze the water level dynamics in Weed Lake Equation (8):

R = Volume of present in the wetland at any time,

Pr =Annual precipitation,

Inwt = Incoming water from the wastewater treatment plant, Tr = Tributaries feeding into the wetland,

Ca = Capillary groundwater feeding the wetland,

E = Evaporation of water from the wetland,

vap r '

Se = Seepage water from the wetland,

Out canal = Water released in the canal via the gate.

Therefore, the above water budget equation for the wetland can be simplified and rewritten as Equation (9):

Of special significance to the wetland water quality management program are nitrogen and phosphorous. The interrelationships that exist between these two nutrients are best manifested in their fundamental importance in eutrophication. A mass balance analysis was performed for phosphorous and nitrogen, both being the nutrients of greatest input from the wastewater effluent being regularly discharged into the wetland. The following assumptions were derived in formulating the mass balance analysis for nitrogen and phosphorous (Tables 9,10). Assumptions made for nitrogen:

i. Biological conversion of organic-N ---> NH3-N is not significant from November to April

ii. Mineralization of NH3-N ---> NO3-N is not significant from November to April

iii. Biological reduction of NH3-N ---> NH3 (g) is not significant from November to April and, assumptions for phosphorous:

iv. Biological conversion of organic-P --->PO43 is not significant from November to April

v. Formations of insoluble forms of Ca (HPO4)2 and Mg (HPO4)2 are nominal relative to the overall process of soluble PO4-3 removal by microorganisms, algae and plankton.

vi. Equilibrium reaction exists between soluble PO43 <---> sediment sorbed PO4'3

vii. The material balance equation for can be written Equation (10) for each nutrient of interest:

Total-P, PO4'3and NH3-N are expected to undergo significant biotic and abiotic interactions during the months of May to September. Their decay rate could be modeled as a first-order reaction Equation (11). That is

where K is the reaction rate coefficient with dimensions of 1/ time and C is the nutrient concentration. According to Equation (11), the rate of loss of any of these nutrients is proportional to the amount of available substance that is present in the water column. The integrated first order rate law yields Equation (12):

where C is the initial concentration of NO-N or PO'3 at first

The regression equations for rate constant, K, determination of NO3-N and PO43 for selected years were derived for the wetland most biologically active period (Table 11). Assuming that both nutrients are uniformly distributed throughout the wetland water column, hence the total amount of a nutrient is calculated as CV according to Equation (14). Thus,

where C represents the concentration of nutrient and it is uniformly distributed throughout the volume, V, of the wetland water column. The material balance becomes Equation (15):

Monthly phosphorous and nitrogen load (MLP or N) as a result of effluent discharges can be calculated Equation (16):

Where, EmV represents monthly volume of effluent (L) and CE, effluent concentration (mg/L). Therefore, average volume of water (LV) in the wetland at sampling time Equation (17):

The mass balance analysis reveals a decreasing trend in nutrients level in the wetland. In 2012 the water matrix consisted of 45% of PO4-3 which translated into a load of 1.32 x 106 g with a corresponding load of 8017g of NH3-N meaning a ratio of PO4-3/NH3-N of 165:1. Concurrently, 55% of phosphorus can be assumed to be in the forms of condensed-phosphates and organically bound-phosphates which represent a load of 1.63 x 106g. With respect to 2013, the wetland water matrix was made up of 35% PO43. Which translated into a load of 1.34 x 106 g with a corresponding load of 1.6 x 105 NH3-N. Hence 65% of phosphorous in the water mass can be assumed to be in complex forms. For year 2014, it was determined that 82% of phosphorous status was in various complex forms. The direct impact of various uncontrollable natural factors will influence the speciation of phosphorous and nitrogen in Weed lake from year to year.

Nonetheless, if too much nitrogen enters the wetland through effluent discharge to offset the current nutrient ratio PO4-3/NH3-N, the result will likely be rapid and significant growth of aquatic vegetation in nuisance quantities and eventually lowering DO content due to the death and decay of the aquatic vegetation. As a natural dynamic system, implementation and evaluation of best management practices of Weed Lake should be continued and accordingly evaluated in order to strive to achieve and maintain a healthy wetland ecosystem.

Conclusion

The results of the Weed Lake water quality monitoring program study indicate spatial and temporal variations in the concentrations of the parameters tested. EC and TDS values tend to increase both spatially and temporally from the south to the north quadrant. Similar trend has been observed as it relates to HPC and coliforms CFU enumeration. Mass balance analysis indicates that 40 to 80% of phosphorous in the wetland water column is in various complex forms. Uncontrollable natural factors will have a direct influence from year to year on the speciation of phosphorous and nitrogen in the wetland ecosystem. Any surges or significant departure from the current nitrate inputs will have the potential to adversely affect the water quality and health of the wetland.

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Global Data Backup and Recovery Market Size & Growth Analysis Report 2027

Global Data Backup and Recovery Market is growing at a significant CAGR of 10.1% during forecast (2021-2027) and is anticipated to reach around $22.5 billion in 2027.According to the organization in 2017, more than 3 billion people are connected to the internet compared to just 2.3 million in 1990. These 3 billion people are generating data in every second, which has led to the growth of big data. Several researchers have discovered a potential connection between robust data management strategy and companies’ financial performance. This enables businesses to reach the market faster with products and services that are efficiently associated with customer needs. Higher recovery time of lost data is leading to lost revenue and customer relation problems as well as can reduce the possibility of reaching the lost data. It is difficult to retrieve data from harsh physical damages in the data storage devices. In addition, damages on the magnetic area of storage devices complicate the process of data loss recovery. It is required not to make any blunders while retrieving the data which can complicate the process of data recovery. Therefore, technological professionals are employed by some organizations to perform the task and avoid such complications in data recovery.

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Growing Demand for Data Backup and Recovery Market

Blockchain is an emerging technology that is anticipated to strengthen data integration, security, and authenticity. The technology can advance the way data is stored and managed by making it tamper-proof. It can offer various advantages in data protection, including transparency, reliability, and tamper-proof data security. Blockchain network is visible to all participants and ensures trust and easy auditing. It is a reliable source as and when one node fails, the user can still access the data as it is saved on all other nodes. Tampering or deleting data is highly difficult and will leave a trace. Blockchain platform ensures that data is encrypted which signifies that alterations in data are a difficult task. It allows users to save cryptographic signature of a file or document on the blockchain. This would provide users with a way to ensure a file is un-tampered without the need to save the entire file on the blockchain. Therefore, integration of blockchain technology may significantly improve data security and efficiently backup the files and documents.

A Full Report of Data Backup and Recovery Market is Available at: https://www.omrglobal.com/industry-reports/data-backup-and-recovery-market

Scope of the Global Data Backup and Recovery Market

Market Coverage

Market number available - 2020-2027

Base year- 2020

Forecast period- 2021-2027

Segment Covered- By Deployment and By Application

Regions Covered- North America (US and Canada), Europe (UK, Germany, France, Italy, Spain, and Rest of Europe), Asia-Pacific (China, Japan, India, and Rest of Asia-Pacific), and Rest of World

Competitive Landscape- Amazon Web Services, Inc., Hewlett Packard Enterprise Co., IBM Corp., Microsoft Corp., Oracle Corp.

Recent Strategic Initiatives in the Global Data Backup and Recovery Market

In July 2019, the company announced that it has been named a Visionary in the 2019 Gartner Magic Quadrant for Security Awareness Computer-Based Training for PhishLine. In its independent analysis of security-awareness training vendors, Gartner recognized Barracuda for completeness of vision and ability to execute.

In August 2018, Amazon Web Services and DXC Technology, an American multinational company offering IT solution, announced a multi-year global agreement to build a new multi-billion-dollar DXC - AWS Integrated Practice that will deliver IT migration, application transformation, and business innovation to global Fortune 1000 clients.

Global Data Backup and Recovery Market -Segmentation

By Deployment

On-Premises

Cloud-Based

By Application

Media Storage

Email Storage

Application Storage

Global Data Backup and Recovery Market– Segment by Country

North America

US

Canada

Europe

UK

Germany

France

Italy

Spain

Rest of Europe

Asia-Pacific

China

Japan

India

Rest of Asia-Pacific

Rest of the World

Company Profiles

Acronis International GmbH

Actifio Inc.

Amazon Web Services, Inc.

Asigra Inc.

Barracuda Networks, Inc.

Broadcom Inc.

Carbonite, Inc.

Commvault Systems Inc.

Dell Inc.

FalconStor Software Inc.

Hewlett Packard Enterprise Co.

IBM Corp.

Microsoft Corp.

NetApp Inc.

Oracle Corp.

R3 Data Recovery Ltd.

Reasons to Buying From us –

We cover more than 15 major industries, further segmented into more than 90 sectors.

More than 120 countries are for analysis.

Over 100+ paid data sources mined for investigation.

Our expert research analysts answer all your questions before and after purchasing your report.

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Media Contact: Company Name: Orion Market Research Contact Person: Mr. Anurag Tiwari Email: anurag@omrglobal.com Contact no: +91 780-304-0404

[digital-roadmap-management]

Top 5 Use Cases for Business Capabilities to Transform an Organization

Business capabilities take over the role as primary concept to manage alignments and gain transparency of an organization. In this article, I present five major use cases that business capabilities can enable and what is needed to achieve the desired benefits.

 1. Provide IT Landscape Transparency on All Enterprise Architecture Layer

The first and most common use case of applying business capabilities is to provide a generic, easy to understand, and holistic view of an organization that can be used to map IT components, such as applications, data, or technology to it. Understanding the As-Is and To-Be landscape of the application layer, the data layer, and the technology layer is at the heart of Enterprise Architecture Management. Business capabilities can be the linking element, located in the business layer, that IT can map components to, and that business is able to understand easily. Before the start of a landscape assessment, have a clear picture of what you want to do with the data after you gathered it (and hence what you actually want to assess in detail), how you want to store it, share it, and how it should be governed and updated over time.

 2. Prioritize Projects Based on the Strategic Importance of the Underlying Business Capabilities

This use case requires that projects identify the business capabilities that they support and that the result is centrally collected before the beginning of the demand and portfolio process. This also requires that there is a business capability map in place for the whole organization and that there is an indication of the strategic relevance for every capability. This can be done by breaking down existing business strategies and understanding what those strategies actually imply.

Consider this example: If your company wants to increase digital sales, your e-commerce capability would probably be of high strategic relevance. If your organization collects the data for this use case, it would be able to show the strategic importance of business capabilities based on the underlying projects – depending on which capabilities they enable. The resulting analysis could help to decide whether a project should be funded or not.

 3. Capabilities-Based Demand Management

A very popular use case is to support the demand management process of your organization. Unfortunately, it is also one of the hardest to implement. In order to add value to the demand management process with the help of capabilities, you need to have a detailed capability map in place, as well as a very good As-Is transparency of your landscape mapped to it. Your As-Is landscape might consist of applications and their functionalities, of systems and supported technologies, or even of ready-to-use solution bundles.

If you gather all this information, you could map incoming demands (e.g. a business-drawn user journey) to business capabilities and identify whether you already have that capability in your map or not. If it already exists, you can analyze the IT components mapped to it. You can also evaluate whether they might be suitable to meet the described demand or if you really need to develop something new. The result of this exercise would most likely be a reduced set of capabilities and hence functionalities that need to be developed from scratch or purchased, while you can maximize the re-usage of existing IT components and therefore reduce costs and required resources, enhance stability through tested components, and reduce time-to-market.

Yet, this approach often stays theoretic, because it cannot be applied in areas in large applications cannot be separated into its single capabilities. Therefore, they do not allow to use single parts of it for new demands. Also, the As-Is landscape is often not described in enough detail to allow for such an approach, so that there is a lot of upfront effort required.

 4. Application Lifecycle Optimization

Some organizations have thousands of applications. Applying business capabilities to cluster them according to the business abilities they enable makes it much easier to optimize the application landscape. The goal is to have such a granular business capability map that no more than 5 to 10 applications are mapped to one business capability.

This allows to analyze the applications per independent from each other per business capability cluster. This can for instance be done by applying a TIME analysis, which assesses the business fit and the IT fit of each application and allocates it on a matrix. The business fit could be the result of business value added, business criticality, number of users, departments, countries using the application, or allocated revenues. The IT fit could be the result of the support of the underlying technology, the application security, availability of the source code, response times, issues etc. There are many indicators that you could think of and you should evaluate which ones your organization can assess and are helpful for your goal.

If you put the business fit on the x-axis and the IT fit on the y-axis, you will create the following quadrants:

Top Left, Tolerate: Those applications have a low business fit, but their IT fit is high. You can therefore keep them in your organization as they do no harm.

Top Right, Invest: Those applications have a high business fit and a high IT fit. You should further strengthen them and further invest in them, as they are the best category of your applications.

Bottom Right, Migrate: Those applications have a high business fit, but a low IT fit. You probably need their functionalities, but the underlying technology is not optimal. You should consider changing the provider, migrate to a new server or do something else to enhance their IT fit.

Bottom Left, Eliminate: Those applications have a low business fit and a low IT fit. You do not need those applications and they have no suitable IT foundation. The best option for them is to be eliminated to reduce the number of applications and to drive down costs.

Doing this analysis on a capability basis ensures that you always have transparency on whether the capability is still supported via an application although you decide to eliminate another.

 5. Capabilities-Based IT Post Merger Integration Approach

The key benefit of using business capabilities in a post-merger integration is their generic nature that makes them understandable across organizations. While processes are often specific to an organization and hence differ in terms of their scope or wording, capabilities aim at providing the same foundation for all.

Especially during the merger of two different companies, this is extremely important, as those companies have different backgrounds, different cultures, and need to be clear when communicating with each other prior to Day 1. Business capabilities provide that foundation and can be used to map all kinds of IT components from both companies to them, so that the further analysis can be based on those capability clusters.

[blog2258]

Comprehensive Guide to Data Migration Process at Q-Migrator

Data migration is the process at Q- Migrator  of transferring data from one system or storage solution to another. This process is crucial for organizations that are upgrading systems, moving to the cloud, or consolidating data centers. A well-planned data migration process ensures that data is accurately and efficiently moved, minimizing downtime and maintaining data integrity. Here’s a comprehensive overview of the data migration process: 

1. Planning and Assessment 

Requirements Gathering: Understand the purpose of the migration, the scope, and the desired outcomes. 

Current State Analysis: Assess the current data environment, including data types, volume, sources, and quality. 

Target Environment: Define the target environment’s specifications and constraints. 

Risk Assessment: Identify potential risks and develop mitigation strategies. 

Budgeting and Resources: Determine the budget and resources (personnel, tools, time) required. 

2. Design 

Data Mapping: Map the data fields from the source to the target system. 

Migration Strategy: Decide on the migration approach (big bang, phased, parallel running, etc.). 

Data Governance: Establish policies and procedures for data handling, security, and compliance. 

Tools and Technologies: Select appropriate data migration tools and technologies. 

3. Development 

Infrastructure Setup: Set up the necessary hardware and software infrastructure for the migration. 

Data Extraction: Develop scripts or use tools to extract data from the source system. 

Data Transformation: Develop the transformation logic to convert data into the format required by the target system. 

Loading Process: Develop the process to load transformed data into the target system. 

4. Testing 

Unit Testing: Test individual components of the migration process (e.g., extraction, transformation). 

System Testing: Test the entire migration process in a controlled environment. 

Data Verification: Verify the data in the target system against the source to ensure accuracy and completeness. 

Performance Testing: Ensure the migration process can handle the data volume within the required timeframes. 

5. Execution 

Pilot Migration: Conduct a pilot migration with a subset of data to identify any issues. 

Full Migration: Execute the full data migration process. 

Monitoring: Continuously monitor the migration process for any errors or performance issues. 

Issue Resolution: Address any issues that arise during the migration process promptly. 

6. Post-Migration 

Validation: Perform thorough validation to ensure all data has been accurately and completely migrated. 

Performance Tuning: Optimize the performance of the target system post-migration. 

User Acceptance Testing (UAT): Allow end-users to test the new system and confirm that it meets their requirements. 

Training and Documentation: Provide training for users and document the new system and processes. 

7. Maintenance 

Ongoing Support: Provide support to resolve any post-migration issues. 

Data Quality Monitoring: Implement ongoing data quality checks and monitoring. 

System Updates: Keep the new system updated and perform regular maintenance. 

Tools and Best Practices 

Automation Tools: Use data migration tools like Talend, Informatica, or Microsoft Azure Data Factory to automate and streamline the process. 

Data Quality Tools: Utilize data quality tools to ensure the integrity and quality of the data during migration. 

Backup and Recovery: Always have a backup and recovery plan to revert changes if something goes wrong. 

Communication Plan: Keep all stakeholders informed throughout the migration process. 

Incremental Migration: Where possible, migrate data incrementally to minimize risk and downtime. 

A successful data migration requires meticulous planning, rigorous testing, and thorough validation to ensure that the data is accurately transferred and the new system operates as expected. 

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AWS named a Leader in new 2020 Gartner Magic Quadrant for Cloud Database Management Systems

Industry analyst firm Gartner has published a new report, the Magic Quadrant for Cloud Database Management Systems, naming AWS as a Leader and placing AWS highest among the 16 vendors evaluated for “Ability to Execute.” We’re proud to be recognized by Gartner as a Leader for several consecutive years in both the database and analytics market segments, but there’s more to the story of what makes this report special. We’re sharing it here because we believe it’s critical to enterprises making cloud-based data management decisions that could impact the future of their business. This report is Gartner’s first database and analytics report exclusively evaluating cloud-based services, doubling down on their assertion in a June 2019 report that The Future of the DBMS Market is Cloud. We couldn’t agree more – eleven years after AWS introduced its first cloud database service, Amazon Relational Database Service (Amazon RDS) supporting MySQL in 2009, our customers have embraced the cloud wholeheartedly. In fact, we recently shared in an October 15, 2020, blog post that 300,000 customers have used AWS Database Migration Service (AWS DMS) to bring their databases to AWS. Similarly, 8 years after AWS introduced Amazon Redshift as the first fully managed data warehouse in production in the cloud, the service has grown to be the most popular cloud data warehouse on the market, with tens of thousands of customers deploying workloads on it today. Our customers have told us that they don’t want the “one-size-fits-all” approach that old guard providers deliver, so we offer 15 database services, including relational, key-value, document, in-memory, graph, time series, wide-column, and ledger databases. All those databases have been rapidly adopted by our customers. But don’t take our word for it – Gartner’s most recent market share report for Database Platform as a Service (Market Share: Enterprise Public Cloud Services, Worldwide, 2019) shows that AWS market share of 55% is over 10% more than the combined market share of the other 15 participants in the Magic Quadrant for Cloud Databases. For analytics, customers share that they want more value from their data even as it is growing exponentially, coming from new and increasingly diverse data sources, used by many people, and analyzed by many applications. Our portfolio of analytics services spans data lakes, data warehouse modernization, big data processing, real-time streaming analytics, operational analytics, and self-service business intelligence. It gives customers the easiest way to build data lakes and run analytics workloads on the most secure infrastructure, with a comprehensive set of analytics services that are open, scalable, and cost-effective. When it comes to analytics, breadth and depth matters – Gartner recently recognized AWS analytics leadership in another detailed evaluation, the 2020 Scorecard for AWS Cloud Analytical Data Stores (August 2020), where AWS scored the highest of any vendor evaluated against the criteria (92%) and a perfect 100% for required criteria. If you’d like to learn more about our databases and analytics services, see Databases on AWS and Data Lakes and Analytics on AWS. You can also read the complete Gartner report. About the Author Herain Oberoi leads Product Marketing for AWS’s Databases, Analytics, BI, and Blockchain services. His team is responsible for helping customers learn about, adopt, and successfully use AWS services. Prior to AWS, he held various product management and marketing leadership roles at Microsoft and a successful startup that was later acquired by BEA Systems. When he’s not working, he enjoys spending time with his family, gardening, and exercising.       Gartner, Magic Quadrant for Cloud Database Management Systems, Donald Feinberg, Merv Adrian, Adam Ronthal, Henry Cook and Rick Greenwald, November 2020. The Future of the DBMS Market Is Cloud, Donald Feinberg, Merv Adrian, Adam Ronthal, June 2019. Market Share: Enterprise Public Cloud Services, Worldwide, 2019, Colleen Graham, Terilyn Palanca, Neha Gupta, Fabrizio Biscotti, Bindi Bhullar, Julian Poulter, Craig Roth, Alys Woodward, Jim Hare, July 2020. Solution Scorecard for AWS Cloud Analytical Data Stores, Ramke Ramakrishnan, Darryl Jamieson, August 2020. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from AWS. https://aws.amazon.com/blogs/database/aws-named-a-leader-in-new-gartner-magic-quadrant-report-evaluating-cloud-database-and-analytics-services/