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Best Practices in Cloud Data Migration

Enterprises are increasingly migrating to the cloud to benefit from the productivity and data access capabilities of modern data architectures. With close to 60% of Indian firms planning on leveraging the cloud for greater agility, there is significant momentum across industries to scale up cloud adoption.

 To truly benefit from comprehensive digital transformation, businesses need to outline a robust data migration strategy. The strategy needs to incorporate best practices in cloud data migration while ensuring cost-effectiveness, accessibility, and cyber security at scale. Leveraging best practices also ensure that the cloud data migration initiative is ROI-focused and does not lead to overruns or costly re-migration processes. Here’s how it is done:

 Incorporating robust data governance protocols 

While spending on public cloud services is set to grow to 29.4% in 2021 in India, businesses need to have robust data governance policies to manage the rapid generation of real-time data. Data governance policies ensure effective input-control and data source mapping to prevent the formation of a data swamp.

 Data governance policies also establish critical controls to analyze meta-data, perform data analytics initiatives, and update key databases. Firms can also establish different rules for handling different data types to align the modern cloud architecture better to business needs. Modern database management is strengthened significantly with the tools that data governance protocols provide.

 Leveraging automation to expedite data transformation

Automation is a critical tool to enhance the accuracy and speed of cloud migration. Through automated protocols, firms can correctly assess the right data migration candidates while prioritizing databases for effective migration. Automated tools also transform the data to cloud equivalents and optimize schema to ensure compatibility to the new data platform without manual inputs.

 An essential benefit that automated transformation tools provide is validating the translated data sets to ensure quality control. Reconciliation scripts are automatically generated with granular-level analysis of the transformed data. Firms can also perform semantic and syntactical validation of the converted logic to ensure minimal errors.

 Ensuring effective cyber security measures

Cyber security is a key area of focus throughout the lifespan of cloud data management. Complex deployments that are not protected from common threats can be prone to zero-day attacks, phishing/spoofing, ransomware, crypto-jacking, etc. Misconfigurations and unencrypted data sets can lead to significant issues with cyber security, especially as cloud adoption scales up to enterprise-wide.

 Firms need to focus on cyber security from a policy and technology perspective. While most cloud platforms have built-in security tools, businesses need to incorporate regular pen-testing, phishing tests, and audits to ensure that they are ahead of industry trends.

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Modernizing the Enterprise Data Warehouse – 5 Key Considerations

Data warehouse modernization is a complex initiative involving multiple stakeholders to implement successfully. Enterprises need to formulate the right modernization approach by incorporating key requirements, transformation goals, and cost-effectiveness parameters. By balancing distinct aspects of data modernization, organizations can prevent budgetary overruns while ensuring seamless modern data warehouse implementation. Below-mentioned are a few important things to consider while modernizing the enterprise data warehouse:

 Selecting the right data platform 

Effective data warehouse modernization relies on platforms that are flexible and can enhance the productivity of users at-scale. That is why selecting the right target platform is one of the most important areas to consider for effective data warehouse implementation. Compatibility, reliability, up-time, and connectivity are key factors that will play a vital role in enabling managers to decide which platform to opt for.

 Focus on a strategic modernization roadmap

Organizations can map key requirements from a business standpoint and develop a strategic roadmap for data warehouse modernization. The roadmap can capture the essential modernization approach (hybrid, cloud, etc.), the data management protocols, and sunsetting the legacy systems. The roadmap should also capture essential strategies to update enterprise data warehouses, as well as cover any training required to leverage modern data warehouses for business intelligence and data analytics initiatives.

 Optimizing for cost-effectiveness and scale 

Cost-effectiveness and scale are critical areas of consideration when it comes to data warehouse modernization. Enterprises need to ensure that they can run business applications seamlessly when leveraging a modern data warehouse platform. Workloads need to run effectively without giving rise to significant cost burdens while ensuring end-to-end connectivity for diverse data sources. Enterprises should ensure that there are no reinvestments required to facilitate modernization. 

 Leveraging automation for greater agility

Automation eliminates error-prone processes by streamlining assessment of the data warehouse inventory. The leading data migration service providers leverage automated protocols for data migration, as it expedites modernization reducing time to launch for modern data warehouses. Data validation at the minutest level can be performed to review the effectiveness of the modernization initiative, along with automated execution of transformed scripts.

 Ensure compliance with industry regulations

EDW modernization needs to be compliant with relevant industry guidelines to ensure the proper handling of sensitive data. Establishing robust data governance protocols can streamline compliance management while empowering managers with the right tools to manage the modernization lifecycle. By establishing clear standards for data quality, modeling, architecture, semantics, and development methods, organizations can better control data warehouse modernization.

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Strengthening Data Governance in Data Lakes – 4 Key Strategies

Data governance is a key strategic initiative that all enterprises must undertake to ensure complete protection of data lakes and data warehouses. Firms also need to follow industry-based regulations, such as GDPR, PCI DSS, CCPA, and HIPAA to comply with data protection norms and prevent unauthorized parties from gaining access. By leveraging the following data governance strategies, organizations can remain agile in protecting sensitive information within their modern data architecture.

 Tier-based access for stakeholders

Effective data governance relies on the comprehensive implementation of key policies, technologies, and controls. Chief among them is the identity and access management (IAM) system,  a critical protocol to implement to ensure holistic data protection. Based on the enterprise’s needs, robust role-based access control should be instituted along with the metadata analysis of logged user-sessions. Tier-based access also prevents hackers and third-party applications from gaining access to sensitive information.

 Dynamic data masking for sensitive materials

When leveraging a robust data lake, a critical objective of enterprises is implementing data encryption. Encryption for data in motion and data at rest is critical to establish to prevent unauthorized administrative control over sensitive information. Dynamic data masking is a key strategic initiative that helps enforce enterprise data governance policies. Column-level masking allows specific data users to see information as is, and others in an encrypted or hidden format.

 Routine data maintenance and cleansing

Employees across business areas may spend close to 30% of their time cleansing data due to poor data quality and limited visibility. To eliminate data ambiguity from your data lakes, it is important to perform routine data maintenance to remove inactive input sources, unused datasets, and bulky legacy data. Enterprises with a robust Hadoop data lake maintenance protocol can also leverage automation-driven cleansing initiatives for scale-driven data lake architectures.

 Continuous iteration and innovation within policies

Data lakes are only as effective as the data governance policies implemented to protect sensitive information. Every organization must adopt a unique approach to data governance, and continuously iterate across business domains to apply the right level of controls. Larger enterprises may require distinct data lake governance policies to process specific business intelligence jobs and analytics initiatives. The data governance framework needs to seamlessly integrate with the needs of the enterprise and not create organizational silos or bottlenecks due to excessive restrictions.

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All You Need to Know About Data Lake Management

 A data lake is a centralized repository that can store a range of structured and unstructured data. It allows organizations to store data as it is, without the need to structure it. Data lake systems thus help organizations save time that would have otherwise been wasted in the data processing. This is because these systems mitigate the need to define, structure, and curate data.   

 Data Lake Architecture:   

  Compared to traditional business intelligence architecture, the architecture of a data lake differs so that the data sources are more varied, and the data can either be structured or unstructured. All this data integrates just like a raw data store. When structured data enter raw data storage, it either enters the analytical sandbox or the batch-processing engine. From here, it enters the processed data stores where reports are generated. The unstructured data may enter raw data storage and get transferred to the batch-processing engine, or might enter through a real-time processing engine. It then enters the processed data stores where reports are generated. 

  Essential Elements of a Data lake: 

  The key capabilities that must be considered by any organization that plans to build data lakes and ensure smooth data lake management are as follows:

  - Data movement: Data lakes help in importing any amount of data. The data is collected from multiple sources and then moved to the data lake.  

  -Securely store the data: Data lakes also help in storing relational data (operational databases) and non-relational data (IoT devices, mobile apps, and social media. It also helps in understanding data through cataloging, crawling, and indexing. 

  - Analytics: With the help of data lakes, data developers and data scientists can access data using analytic tools and frameworks. It allows them to run analytics without moving the data to a separate analytics system. 

  -Machine Learning: Data lakes also help organizations in ETL offload and gaining data insights. It allows machine learning where different models are built to suggest actions that will help in achieving the best results.  

 The most important benefit of a ‘data lake’ is that it helps in harnessing more data from many sources in less time. This, in turn, helps in better collaboration and faster decision making for the organizations.

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Ensuring Limited Downtime When Performing a Netezza Migration

Performing a comprehensive Netezza migration to the cloud is a complex and resource-intensive activity, which is why it is crucial to minimize risk and downtime throughout the transition. Firms leveraging data-intensive applications may experience costly delays because of slower workload migration to the cloud. Whether firms opt for a hybrid approach or a complete data transformation initiative, it is important to limit downtime across all transformation stages. This is how it can be done:

Profiling all existing workloads

Data managers need to profile all workloads within the enterprise to analyze their transformation readiness from Netezza. Workload migration is an intensive process, that is streamlined when firms understand the relationship between different tables, roles, applications, and users. The utility of the existing data can also be analyzed to help filter them prior to migration.

Firms can then prioritize workloads as part of a robust migration framework to transform critical workloads first. This expedites the process significantly, resulting in cost and resource savings. It also creates a phase-wise approach for the transformation process, giving managers greater control over the entire process.

Leveraging automation to streamline transformation

Netezza workloads can be transformed into big data architecture seamlessly through automated transformation technologies. Impetus Technologies, a leading partner in enterprise workload migration, leverage automated tools to ensure high-quality data transformation to the big cloud architecture.

Post-transformation, validation can also be easily performed through automated meta-data, data, and schema analysis for the entire workload. This ensures that all the Netezza data files have been successfully transformed into the big data environment without significant downtime. Automation also ensures that all business logic is also maintained post-transformation so that the enterprise can execute business intelligence initiatives easily.  

Performing ongoing data loading and manual conversion

As a part of the overall data transformation activity, Netezza workloads may require manual conversion on-site on an ongoing basis. This is critical to expediting the transformation process, as it ensures that there are no lapses within data quality or leftover data files. This also helps sunset the existing workload, while all applications run on the new cloud platform.

Manual conversion is also required in some instances where the Netezza migration is complex with third-party applications, business logic, and unstructured data present. Manual conversion helps ensure 100% data transformation completion so that enterprises can lower potential maintenance costs and reduce the need for re-transformation activities for workloads.

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Characteristics of a Modern Data Architecture

The rise in data and technological advancement has forced enterprises that rely on data for decision making to remodel its data architecture.  How organizations compete and serve their customers have changed drastically in the past decade, resulting in a treasure trove of new techniques, software, and technologies. The modern data-driven enterprises envision business needs and market shifts to optimize their business strategies and outcomes. This helps them remain ahead of the competition and be aware of the possible hurdles arriving in business.

What is data architecture?

A data architect designs a blueprint of the data ecosystem that falls in line with the short and long-term goals of an organization, its cultural and contextual requirements. To put it simply, data architecture defines a set of products and tools used by an organization to manage its data. However, it does much more than this. Data architecture also identifies the audience that consumes the data and their requirements.

Most of the data warehouses require an army of individuals to build and change, resulting in a low return to investments. In the past, the data architectures were relatively static and IT-driven, also known as data warehouses. In contrast,  modern data architecture provides a much more flexible, adaptable, and agile data warehouse. These data warehouses are just one of the many components of modern data architecture. Let’s look at some of the characteristics of these modern data warehouses.

1.           Adaptable: Modern data warehouses allow the flow of data from the source systems to business users seamlessly. The architecture ensures a smooth flow by constructing a series of interconnected and bidirectional data services that serve several business requirements. Base data objects are used to build these pipelines and serve as building blocks. They are regularly reused, replenished, and repurposed to ensure a steady flow of data.

 2.           Customer-centric: Modern data architecture starts with business users and their needs rather than the technology and data required to extract, transform, ingest, and present the information. The customers’ requirements may vary in terms of their role or department and might be internal or external to the organization.

  3.      Automated: To create a modern data architecture that is adaptable and has a smooth flow of data, designers need to automate everything. The data must be tagged and profiled as it is ingested into the system and then mapped to the existing data set and attributes. This process is known as metadata injection, a key function of data catalogs. Similarly, the changes in the source schema should be detected and their impact on the downstream objects and applications should be identified. Working in a real-time environment, the modern data architectures should be able to detect the irregularities and trigger alerts in the dashboards.

Modern data architecture has various other characteristics that help build a strong data platform for modern data analytics. If you are looking to shift your organization’s data to modern architecture, you can get in touch with Impetus Technologies. They specialize in the field of data analytics, ETL, and big data and are a one-stop solution for all your big data consulting services.