#data architecture

[thedatachannel]

Data Modelling Master Class-Series | Introduction -Topic 1

https://youtu.be/L1x_BM9wWdQ

#theDataChannel @thedatachannel @datamodelling

[atozofsoftwareengineering]

Demystifying Data Engineering: The Backbone of Modern Analytics

Hey friends! Check out this in-depth blog on #DataEngineering that explores its role in building robust data pipelines, ensuring data quality, and optimizing performance. Discover emerging trends like #cloudcomputing, #realtimeprocessing, and #DataOps

In the era of big data, data engineering has emerged as a critical discipline that underpins the success of data-driven organizations. Data engineering encompasses the design, construction, and maintenance of the infrastructure and systems required to extract, transform, and load (ETL) data, making it accessible and usable for analytics and decision-making. This blog aims to provide an in-depth…

[dvnzook]

Strange World

[jonah-miles-smith]

Data Engineering Services Explained: What Lies Ahead for the Industry

In an era where data shapes every aspect of business decision-making, organizations are turning to data engineering to harness its full potential. As data volumes and complexities escalate, the demand for specialized data engineering services has surged. This article delves into the core components of data engineering services and offers insights into the evolving future of this critical field.

What are Data Engineering Services?

Data engineering involves the design, construction, and maintenance of systems and infrastructure that allow for the collection, storage, processing, and analysis of data. Data engineering services encompass a variety of tasks and functions that ensure data is accessible, reliable, and usable for data scientists, analysts, and business stakeholders. Key components of data engineering services include:

1. Data Architecture

Data engineers are responsible for designing data architectures that define how data is collected, stored, and accessed. This includes selecting appropriate databases, data lakes, and data warehouses to optimize performance and scalability.

2. Data Integration

Data often comes from multiple sources, including transactional systems, external APIs, and sensor data. Data engineering services involve creating ETL (Extract, Transform, Load) processes that integrate data from these various sources into a unified format.

3. Data Quality and Governance

Ensuring data quality is critical for accurate analysis. Data engineers implement data validation and cleansing processes to identify and rectify errors. They also establish governance frameworks to maintain data integrity and compliance with regulations.

4. Data Pipeline Development

Data pipelines automate the flow of data from its source to storage and processing systems. Data engineering services focus on building efficient pipelines that can handle large volumes of data while ensuring minimal latency.

5. Performance Optimization

As organizations scale, performance becomes a crucial consideration. Data engineers optimize databases and pipelines for speed and efficiency, enabling faster data retrieval and processing.

6. Collaboration with Data Teams

Data engineers work closely with data scientists, analysts, and other stakeholders to understand their data needs. This collaboration ensures that the data infrastructure supports analytical initiatives effectively.

The Future of Data Engineering

As the field of data engineering evolves, several trends are shaping its future:

1. Increased Automation

Automation is set to revolutionize data engineering. Tools and platforms are emerging that automate repetitive tasks such as data cleansing, pipeline management, and monitoring. This will allow data engineers to focus on more strategic initiatives rather than manual processes.

2. Real-time Data Processing

With the rise of IoT devices and streaming applications, the demand for real-time data processing is growing. Future data engineering services will increasingly incorporate technologies like Apache Kafka and Apache Flink to facilitate real-time data ingestion and analytics.

3. Cloud-based Solutions

Cloud computing is becoming the norm for data storage and processing. Data engineering services will continue to leverage cloud platforms like AWS, Google Cloud, and Azure, offering greater scalability, flexibility, and cost-effectiveness.

4. DataOps

DataOps is an emerging discipline that applies agile methodologies to data management. It emphasizes collaboration, continuous integration, and automation in data pipelines. As organizations adopt DataOps, the role of data engineers will shift toward ensuring seamless collaboration across data teams.

5. Focus on Data Security and Privacy

With growing concerns about data security and privacy, data engineers will play a vital role in implementing security measures and ensuring compliance with regulations like GDPR and CCPA. Future services will prioritize data protection as a foundational element of data architecture.

6. Integration of AI and Machine Learning

Data engineering will increasingly intersect with artificial intelligence and machine learning. Data engineers will need to build infrastructures that support machine learning models, ensuring they have access to clean, structured data for training and inference.

Conclusion

Data engineering services are essential for organizations seeking to harness the power of data. As technology continues to advance, the future of data engineering promises to be dynamic and transformative. With a focus on automation, real-time processing, cloud solutions, and security, data engineers will be at the forefront of driving data strategy and innovation. Embracing these trends will enable organizations to make informed decisions, optimize operations, and ultimately gain a competitive edge in their respective industries.

[sbscglobal]

Welcome to the digital era, where data reigns as the new currency.

In modern information technology, the term “Big Data” has surged to the forefront, embodying the exponential growth and availability of data in today’s digital age. This influx of data encompasses vast volumes, generated at unprecedented speeds and with diverse varieties, presenting both challenges and opportunities across industries worldwide.

To unlock the true potential of big data, businesses need to address several critical areas like #BigDataCollection and #DataIntegration, #DataStorage and Management, #DataAnalysis and #DataAnalytics, #DataPrivacy and #DataSecurity, Innovation and Product Development, Operational Efficiency and Cost Optimization. Here at SBSC we recognize the transformative power of #bigdata and empower businesses to unlock its potential through a comprehensive suite of services: #DataStrategy and #Consultation: SBSC’s Tailored advisory services help businesses define their Big Data goals, develop a roadmap, and align data initiatives with strategic objectives.

#DataArchitecture and #DataIntegration: We Design and implementation of scalable, robust data architectures that support data ingestion, storage, and integration from diverse sources. #DataWarehousing and Management: SBSC provides Solutions for setting up data warehouses or data lakes, including management of structured and unstructured data, ensuring accessibility and security. Data Analytics and Business Intelligence: Advanced analytics capabilities leveraging machine learning, AI algorithms, and statistical models to derive actionable insights and support decision-making.

#DataVisualization and Reporting: Creation of intuitive dashboards and reports that visualize key insights and performance metrics, enabling stakeholders to interpret data effectively. #CloudServices and Infrastructure: Leveraging #cloudplatforms for scalability, flexibility, and cost-effectiveness in managing Big Data environments, including migration and optimization services Continuous Improvement and Adaptation: Establishment of feedback loops and metrics to measure the impact of Big Data initiatives, fostering a culture of continuous improvement and adaptation.

By offering a comprehensive suite of services in these areas, SBSC helps businesses to harness the power of Big Data to drive innovation, improve operational efficiency, enhance customer experiences, and achieve sustainable growth in today’s competitive landscape

Contact SBSC to know the right services you need for your Business

Email: info@sbsc.com Website:https://www.sbsc.com

[all-software-updates]

Data Management Solution - How It is Beneficial for Successful Business

What Is Data Management?

The process of gathering, arranging, safeguarding, and preserving an organization's data so that it can be examined for business choices is known as data management. Data management solutions are crucial for making sense of the enormous amounts of data that enterprises are creating and consuming at previously unheard-of speeds. The most advanced data management software available today makes sure that trustworthy, current data is always used to inform choices. The program facilitates the speedy discovery of the data required for analysis by helping with data preparation, cataloguing, search, governance, and more.

The Significance of Data Management

In order to use efficient data analysis at scale and gain valuable insights that benefit both your consumers and your business, data management is an essential first step. People from all throughout a company can locate and access reliable data for their inquiries with the help of efficient data management.

An Efficient Data Management Solution Has the Following Benefits:

·   Visibility- Data management can improve the visibility of your company's data assets, enabling staff members to locate the necessary data more easily and confidently. It can also help individuals become more organized and productive.

·   Dependability- Data management reduces the possibility of errors by defining usage procedures and guidelines. Businesses can react to changes in the market and client needs more quickly when they have access to accurate and current data.

·   Safety & Security- Through the use of authentication and encryption technologies, data management shields your company and its personnel from data breaches, thefts, and losses. Robust data security guarantees that crucial firm data is preserved and retrieved in the event that the primary source is unavailable.

·   Scalability- With repeatable procedures to maintain data and metadata current and minimize needless duplicate expenses, data management enables enterprises to increase data and usage situations efficiently.

T/DG Data Management Solution has the latest tools and platform capabilities- like from organizing the data with best governance practices to storing, processing, integrating and cleansing the data and final consumption. The Digital Group Data Management includes Data Architecture, Data Governance, and Data Quality.

T/DG provides a holistic Data Architecture as a Service.

T/DG’s data management solution expertise in handling and refining enormous amounts of data and are the industry experts in Data Governance.

The ultimate goal of T/DG's Data Quality management software is not only improve the quality of data but to create the desired business outcome leading to success.

[nitor-infotech]

Understanding Database Sharding

Imagine you have a library with thousands of books, and finding a specific one becomes time-consuming. To speed things up, you decide to split the collection into smaller sections based on genres, making it quicker to locate any book.  

Similarly, Database Sharding divides a large database into smaller, more manageable pieces to improve performance and scalability. 

Fig: Database Sharding Banner Image 

What is a Sharding Database 

Database sharding is like storing a huge database across several machines. Imagine one server trying to handle all the data—it can only do so much before it starts slowing down. By splitting the data into smaller chunks, or shards, and storing these across multiple servers, we can manage and process large amounts of data more efficiently. 

As an application grows, more users and data can turn the database into a bottleneck, slowing everything down and frustrating users. Sharding also helps by allowing parallel processing of these smaller datasets, keeping things running smoothly even as demand increases. 

Scaling Techniques in Database Sharding 

Scaling database sharding involves several techniques to ensure efficient management and distribution of data. Here are some key methods: 

Horizontal Partitioning 

This technique involves splitting the data across multiple servers based on a sharding key, such as user ID or geographic region. Each server, or shard, contains a subset of the overall data. This approach allows the system to scale out by adding more servers, thereby distributing the load and improving performance and reliability. 

Vertical Partitioning 

This technique divides the database into smaller tables, each stored on different servers. Each server handles a specific aspect of the application, such as user profiles, transactions, or product details. By separating the data based on functionality, vertical partitioning can improve query performance and make it easier to manage and scale specific parts of the application independently. 

Range-Based Sharding 

Distributes data management based on a continuous range of values. For example, user IDs 1-1000 on one shard, and 1001-2000 on another. 

Hash-Based Sharding 

Uses a hash function on the sharding key to evenly distribute data across shards. This helps avoid uneven data distribution. 

Directory-Based Sharding 

Maintains a lookup table or directory that maps each data item to its corresponding shard. This allows flexible and dynamic distribution of data. 

Each technique has its advantages and is chosen based on the specific needs and growth patterns of the application. 

Benefits of Database Sharding 

Database sharding offers several benefits: 

Improved Performance: By distributing the data across multiple servers, each server handles a smaller subset of the data, reducing the load and improving query response times. 

Scalability: Sharding allows horizontal scaling, meaning you can add more servers to handle the increased load, making it easier to scale the database as the application grows. 

Increased Availability: With data distributed across multiple servers, the system can continue to operate even if one shard fails. This redundancy enhances the overall availability and reliability of the application. 

Efficient Resource Utilization: Different shards can be optimized for specific workloads, allowing better use of hardware resources. For instance, high-traffic shards can be allocated more resources, while less busy shards use fewer resources. 

Reduced Maintenance: Smaller databases are easier to back up, restore, and maintain. Sharding breaks down the database into more manageable pieces, simplifying administrative tasks. 

Factors to consider before Sharding 

Before deciding to shard your database, consider the following factors: 

Database Size: Sharding is typically suitable for large databases that have outgrown the capacity of a single server. 

Traffic Patterns: If your database experiences uneven traffic patterns, sharding can help balance the load. 

Growth Projections: If significant future scaling is anticipated, sharding can be a beneficial strategy. 

Complexity: Sharding introduces additional complexity to your database architecture and requires careful planning and ongoing maintenance. 

Cost: Sharding can be costly due to the need for extra hardware resources and infrastructure to support multiple servers. 

So, database sharding offers both advantages and challenges, and it is important to determine if it aligns with your application’s requirements. 

To know more about database management, reach out to us at Nitor Infotech. 

[intellidatalabs]

Building the Foundation for Data-driven Success: IntelliData Labs' Expert Data Architecture Services

IntelliData Labs offers expert data architecture services designed to lay the foundation for data-driven success. Our experienced architects work closely with organizations to design robust and scalable data architectures that align with business objectives and support future growth. From data modeling to infrastructure design, we provide comprehensive solutions tailored to your unique requirements, enabling you to effectively manage, analyze, and derive insights from your data assets.

[azilentech]

Data Fabric vs Data Lake: Selecting the appropriate one

Have you ever experienced confusion whirling around your data? At once everywhere and nowhere, structured and unstructured? In this data environment, there exist two concealed strongholds: the data lake and the data fabric.

But what's the difference, and which one helps you conquer your data kingdom?

The Data Lake: A Wild Reservoir of Potential

Imagine a vast lake teeming with raw, unfiltered data - text, logs, sensor readings, the whole shebang!

It's a flexible friend, happy to store anything you throw in.

Need to do some exploratory analysis and unearth hidden gems? The data lake is your playground!

But beware, adventurers! Without a map (data schema), it can be hard to find what you're looking for.

The Data Fabric: The Organized Architect

Think of the data fabric as a sophisticated network that connects all your data sources, like rivers feeding a grand canal.

It provides a unified view of your data kingdom, no matter where it resides.

Need real-time insights for critical decisions? The data fabric delivers them at lightning speed.

But building this network takes planning, like designing a grand canal.

So, which one if for you? Read the blog : Data Fabric vs. Data Lake [25 FAQs answered], to know which one is suitable for you.

[rajaniesh]

Scaling Your Data Mesh Architecture for maximum efficiency and interoperability

[ashutosh4]

Here is list of Four Pillars OF TOGAF

“Unlock Your Career Potential: Enroll Now with Certera for Accelerated Growth!”

Visit our website for more information or to explore the courses.

Follow us on LinkedIn, X, YouTube and Instagram.

[garymdm]

Understanding the Role of Information Architecture in Enterprise Architecture

In today’s rapidly evolving digital landscape, organizations, whether large or mid-sized, need to navigate a complex web of data and technology. To effectively manage this complexity and ensure that their operations run smoothly, they often rely on robust Enterprise Architecture (EA) practices. Within the realm of EA, one crucial aspect that plays a pivotal role is Information Architecture (IA).…

[bugendaitechgerman]

Enhancing Enrollment Processes with Salesforce: Streamlining Student Admissions

A well-known provider of CRM systems, Salesforce, has tailored its selection of products to address the needs of the education sector specifically. Educational institutions now have access to a comprehensive platform that enables effective monitoring of student contacts, streamlines admissions procedures, and raises general engagement levels thanks to services like EDA, Education Cloud, and Service Cloud. Salesforce's specialized solutions are intended to efficiently answer the education industry's unique needs.  

Education Cloud:  

The Salesforce Education Cloud solutions comprise various items and technologies intended to simplify the processes for students, teachers, and other members of the educational ecosystem. The objective is to improve personalization in learning paths, student experiences, and other areas. One of Education Cloud's most impressive aspects is how it helps students get in touch with professors, potential employers, tutors, and other students.  

Education Cloud provides a 360-degree view of student and institutional data, which means a comprehensive picture of student and institutional data is provided by Education Cloud. As a result, staff members can access a student's academic record, including what and where they study the personnel with whom they regularly work.

The Education Data Architecture (EDA) is the Education Cloud's foundation.  

A pre-built data model explicitly created for the education industry is the Education Data Architecture (EDA). The collaboration between education partners and the customer community is built on this. 

How does EDA support the Enrollment process? 

Centralized Data Management:   

EDA enables educational institutions to consolidate student data from various sources and systems into one centralized platform. This guarantees that all pertinent information regarding applicants and enrolled students can be easily accessed, accurate, and kept up to date.  

EDA facilitates a seamless connection between each student's account and an administrative account, ensuring a comprehensive record of student addresses, relationships, and affiliations.    

Automated Workflows:   

EDA can automate several enrollment-related procedures, minimizing human error and requiring less manual involvement. For instance, EDA can start automated answers, acknowledgments, and follow-up actions when a candidate submits an online application. Throughout the enrollment process, this automation helps keep applicants informed and interested.   

Integration with Education Cloud:   

Although EDA is not an independent product like Education Cloud, it frequently integrates with Education Cloud to offer a complete solution. Education Cloud provides specialized tools for admissions, student success, and engagement. When combined, EDA and Education Cloud form a strong ecosystem supporting multiple student enrollment and management aspects. 

Admission Connect:

A fresh offering from Education Cloud, Admissions Connect streamlines the application review process, boosts applicant engagement, and improves the student experience while modernizing the recruitment and admissions process for applicants and admissions personnel.   

Both applicants and admissions teams enjoy a consistent experience thanks to Admissions Connect. Staff members can monitor applicants' journeys in a single view, communicate with them meaningfully, and identify which application steps have been completed and which ones are still pending. Additionally, candidates can receive prompt assistance at the appropriate time through chatbot help, personalized action plans, and dynamic checklists. 

Salesforce Service Cloud Support Enrollment Process with Effective Reporting by:  

Selecting a Standard Report Type and Custom Report Type   

The Contacts with Relationships report type is useful when the admissions team has to compile a list of parents for the parent meeting before first-year orientation. 

Dashboard Basics -  

You can make informed decisions based on data by utilizing reports and dashboards to facilitate conversations regarding the information gathered in Salesforce. This supports both potential and incoming students and your recruitment and admissions team. Reports and dashboards provide insight into your data, revealing its narrative. 

Using Service Cloud, assist admitted students -  

With Service Cloud, you allow your students to interact with your organization via any channel, including chat, email, social media, online communities, and more. These exchanges are converted into cases by Service Cloud, which is the Service Cloud object for a student issue, request, or help ticket.  

The one-stop method assists your institution in smoothly and effectively managing admissions and onboarding for students and employees.  

Different Scenarios for Creating Cases:  

Case scenarios can occur through various channels, such as web-to-case, message-to-case, email-to-case, social-to-case, and phone-to-case.  

In a web-to-case scenario, a prospective student visits the Admissions webpage and utilizes the 'Contact Us' button to reach out for information.  

For message-to-case situations, a newly admitted student contacts Cloudy College via Facebook messenger with inquiries about dual credit courses.  

In an email-to-case scenario, a prospective student writes an email to admissionsinfo@cloudy.edu seeking clarification on the application process.  

Social media platforms also play a role in case creation. In social media interactions like social-to-case scenarios, parents may tweet about difficulty finding useful financial aid information while mentioning Cloudy College's account.  

Lastly, in phone conversations leading to case creation (phone-to-cases), newly admitted students call the Admissions office with questions regarding on-campus housing. The Admissions office staff then generates a case for Office of Residence Life staff members to respond accordingly.  

For the support of accepted and prospective students, Service Cloud offers a variety of case management tools, including Queues, Chat, Einstein bots, and Knowledge Base.  

Conclusion 

Salesforce can be a powerful tool for streamlining student admissions. By automating tasks, centralizing data, and providing insights, Salesforce can help schools improve the efficiency and effectiveness of their enrollment processes. As a result, schools can save time and money, improve student satisfaction, and increase enrollment rates.

[ezrasf]

I am putting this senior data systems architect position out into the aether for selfish reasons.

I'm not the hiring manager, but I'm hoping this gets filled so I can stop worrying about the work getting shifted onto my unqualified plate. #dataarchitect

[commercepulse]

Enterprise Data Services

CommercePulse is a Leading provider of business analytics software and enterprise data services. We design, and optimize your data architecture. Contact us now!

For more information visit our website: https://www.commercepulse.co.uk/

[ravinskiy]

Brilliant technique for handling database queries literally saved Discord

It helped them store trillions of messages and fetch them without bringing their DB cluster to its knees.

The technique is called Request Coalescing.

And it’s too good to ignore.

But what’s so special about it?

If multiple users are requesting the same row at the same time, why not query the database only once?

This is exactly what Request Coalescing helps us achieve.

Here’s what happens under the hood:

- The first user that makes a request causes a worker task to spin up in the data service

- Subsequent requests for the same data will check for the existence of that task and subscribe to it

- Once the initial worker task queries the database and gets the result, it will return the row to all subscribers at the same time.

There are several pros to using Request Coalescing:

- Efficient utilization of database resources

- Ability to handle more concurrent requests without creating hot partitions

- Reduce latency

But there are some cons as well:

- Implementation can be complex with regards to getting a fair distributed reader-write lock. Basically, multiple readers need to access the data simultaneously while preventing conflicting writes

- Overall latency may go down, but certain requests will take more time

Of course, this technique is NOT needed normally. But at a certain scale, it can actually save your business.

Credits to Saurabh Dashora