SQL Server data warehouses typically utilize SQL Server for database, SQL Server Integration Service (SSIS) for data integration, SQL Server Reporting Service (SSRS) for BI reports, and SQL Server Analytics Service (SSAS) for analytical needs.

For legacy data warehouses developed with end-of-support versions of SQL Server, maintenance costs can become a challenge, which is one reason to look at modernizing your SQL Server data warehouse.

The greater urgency for modernization, though, is to get your data ready to meet requirements of the modern era including advanced analytics and AI applications, which can be seriously limited by data trapped in legacy data warehouse systems.

Best Options for SQL Server Data Warehouse Modernization

There are several SQL Server End of Support options available for SQL Server Data Warehouse Migration, but these need to be assessed to identify the best fit for your individual needs. In such cases, working with an experienced consulting partner can assist you with the right approach and design strategy to meet future data warehouse requirements.

For instance, Bitwise worked with a Fortune 500 company that had an on-premise SQL Server system that was not providing the desired value for its stakeholders. Bitwise assisted with a data warehouse system architecture assessment to assess alternatives to modernize the legacy data warehouse in Azure with options to utilize Azure Data Factory (ADF) for data integration, Power BI for reporting and Azure SQL MI for the database.

SQL Server Migration to Azure

For decades, companies have been running business intelligence and data warehouse applications with SQL Server databases. These systems have proven to be reliable for organizations both large and small, but as the center of gravity for data and AI shifts to cloud technology, the benefits of modernizing your databases cannot be neglected.

For instance, Bitwise helped a data center solution provider enhance performance with increased data access by accelerating on-premise SQL Server database migration to Azure SQL Server Managed Instance. By going with a consumption-based model in Azure, the client also reduced the burden of maintaining software and hardware infrastructure.

To explore further, check out the SQL Server Data Migration to Azure SQL MI offer on Azure Marketplace for an optimal approach to modernizing with streamlined engagement model and minimal downtime.

SSIS Modernization in ADF or Fabric

With data migration tools available from Microsoft, many organizations often attempt to migrate their SQL Server data warehouse to Azure. While some organizations may be comfortable handling data and SSAS migrations, they often run into problems with SSIS migration to Azure.

There can be drastic differences between SSIS, which is a traditional data integration tool, and cloud services like ADF or Microsoft Fabric Dataflows Gen 2 that are difficult to overcome without any migration automation. Data validation also plays a critical role that gets overlooked until it’s too late.

To illustrate, Bitwise helped a multinational organization achieve significant cost savings with accelerated SSIS ETL Migration to Azure Data Factory. The solution addressed major gaps in the on-premise system by implementing coding best practices and disaster recovery with minimal disruption to the live applications during migration.

Getting Started with a SQL Server Modernization Partner

The limitations of legacy SQL Server data warehouses can stifle business growth and companies staying with older versions carry risks of unsupported systems. With the right strategy to modernize in Microsoft Azure or Fabric, companies can reduce risk, minimize cost and drive innovation.

With the SSIS ETL Migration to ADF offer in Azure Marketplace, you can explore a proven strategy for accelerating migration with automation at each phase of assessment, code conversion and data validation that overcomes the challenges of modernizing SSIS packages.

Learn more about how Bitwise partners with Microsoft to offer a team of experts that can help you understand all aspects of modernization for your SQL Server data warehouse. Our team can walk through the options and showcase the best automation tools and migration methodologies to accelerate time-to-value in the cloud and get your data ready for AI success.

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PySpark programmatic ETL and GUI-based ETL are two different approaches to ETL (Extract, Transform, and Load).PySpark programmatic ETL involves writing ETL code in PySpark, a popular open-source distributed computing framework. This approach offers a number of advantages over GUI-based ETL tools, including:

• Flexibility: Programmatic ETL allows you to create custom ETL pipelines that are tailored to your specific needs. GUI-based ETL tools typically offer a limited set of pre-built components, which can be restrictive.
• Scalability: PySpark is a distributed computing framework, which means that it can scale to handle large datasets. GUI-based ETL tools are typically not as scalable.
• Automation: PySpark code can be easily automated using tools such as Apache Airflow or Prefect. This can free up your team to focus on more strategic tasks.
• Performance: PySpark is optimized for distributed computing, and it can take advantage of multiple cores and processors. This can lead to significant performance improvements over GUI-based ETL tools.

GUI-based ETL tools provide a graphical user interface for building and deploying ETL pipelines. This approach can be easier to get started with than programmatic ETL, but it can be less flexible and scalable.

Challenges of converting existing ETL code to PySpark code (potential targets include Azure Databricks, Synapse Notebooks, Fabric Notebooks, Spark Job Definition, EMR and AWS Glue)

Converting existing ETL code to PySpark code can be a challenging task. There are a number of reasons for this, including:

• Different programming paradigms: PySpark and traditional ETL tools use different programming paradigms. This means that the way code is written and executed differs significantly between PySpark and traditional ETL tools.
• Complexity of PySpark: PySpark is a complex framework with a wide range of features, especially if you are not already familiar with distributed computing paradigm.
• Lack of documentation: There is a lack of documentation on how to convert existing ETL code to PySpark code. This can make the conversion process challenging, especially if you are trying to convert complex ETL logic.
• Availability of skilled (PySpark) resources or learning curve : The challenge lies in finding proficient resources to handle the conversion of current ETL code to PySpark code. Since PySpark is a programming language and platform, there is a learning curve involved. It is imperative to allocate time and resources for training the team on PySpark and the new platform. The existing ETL developers may find it challenging to acquire proficiency in utilizing PySpark efficiently for ETL processes.
• There are various frameworks available in the market, such as Databricks, Synapse Notebooks, Fabric Notebooks, and AWS Glue, that offer built-in capabilities for spark programmatic ETL development. These frameworks optimize the underlying process execution and enable access to native cloud products, such as storage, key vault, and database. However, due to the abundance of available frameworks, it can be challenging to convert to a specific one.

Using automation to overcome challenges (Bitwise approach)

There are several things that you can do to overcome the challenges of converting existing ETL code to PySpark code for Azure Databricks and AWS Glue:

• Comprehensive Assessment: Conducts a comprehensive analysis of the source ETL code base, identifying any uncertainties, intricacies, and data lineage for better planning. (Any source code ETL analyzer tool will be helpful)
• Start small: Don’t try to convert all of your ETL code to PySpark at once. Start by converting a small subset of your code, and then gradually convert the rest of your code over time.
• Use a modular approach: Break down your ETL code into small, modular components. This will make the conversion process easier and more efficient.
• Use a code conversion tool: There are a number of tools that can help you to convert your existing ETL code to PySpark code. These tools can save you a significant amount of time and effort.
• Test your code thoroughly : Once you have converted your ETL code to PySpark, be sure to test it thoroughly to make sure that it is working correctly. (Any automation testing tool will be helpful)
• DevOps Cycle : DevOps can automate ETL pipeline build, testing, and deployment through CI/CD. Monitoring and alerting can detect issues and ensure smooth pipeline performance. Shift-left testing can detect and fix issues early in the development cycle. DevOps can improve collaboration between data engineering and other teams for timely and efficient ETL pipeline development and deployment.
• Deploy the new ETL solution: Once the testing is complete, deploy the new ETL solution in the Dev/QA/Production environment.
• Train the users: Train the users on the new ETL solution and provide them with the necessary documentation and support.
• Monitor and optimize the new ETL solution: Monitor the new ETL solution for any issues and optimize it for better performance (if required).

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