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.

The post Modernization Secrets for your SQL Server Data Warehouse appeared first on Bitwise.

Challenges of migrating PL/SQL to cloud

There are a number of options for converting PL/SQL code to cloud-native systems. Tools like SnowConvert from Snowflake and AWS Schema Conversion tool can apply for certain scenarios and there are manual conversion and other third-party tool options.

Even with these tools, migrating PL/SQL code to Synapse or Snowflake can be a challenging and time-consuming process. Challenges include:

• Understanding the legacy code – PL/SQL code can be complex and difficult to understand, especially for code that was written many years ago.
• Reproducing the functionality – The goal of the migration is to reproduce the same functionality as the legacy code in the new environment. This can be difficult to do, especially if the code is not well-documented.
• Testing the migrated code – Once the code has been migrated, it needs to be thoroughly tested to ensure that it is working correctly. This can be a time-consuming and error-prone process.

Using AI to overcome challenges and accelerate data modernization

When harnessed properly, artificial intelligence (AI) can help overcome a lot of the complexity that causes challenges when migrating to the cloud. Key areas where you can use AI to modernize your PL/SQL code in Synapse or Snowflake include:

• Analyze the legacy code – AI can help identify patterns, dependencies, other important information that can be used to make the code easier to understand and accelerate migration.
• Generate new code – using AI to replicate the functionality of the legacy PL/SQL code can save a significant amount of time and effort when converting to Synapse or Snowflake.
• Test the migrated code – testing the migrated code and identifying any errors or defects is a critically important and difficult step in the modernization process, which can be assisted with AI to ensure that the migrated code is working correctly.

Generative AI approach to PL/SQL code conversion

Generative AI opens new doors for confronting the issues of tedious code conversion and optimization to accelerate your data modernization journey. With our advanced knowledge of PL/SQL code and deep experience modernizing data in Synapse and Snowflake, Bitwise has created powerful modules for transforming and validating code, including:

• Code Converter – provides effortless conversion that automates code migration and modernization utilizing Gen AI. Its batch processing feature allows increased efficiency for automated PL/SQL conversion.
• Code Optimizer – assesses code with Gen AI and suggests optimization designed to your specific goals. Code optimizer reduces space complexity, time and improves error handling assuring fine-tuned performance.
• Code Documenter – automates the commenting and documentation process allowing a clear, comprehensible code base and can delve into variables, functions and defined objects. This not only enhances code readability but also adding to long-term maintainability.
• Migrated Code Validation Utility – provides support for heterogeneous data sources and file types using a unique approach of in-memory comparison without moving the data across data stores. The utility generates comprehensive comparison and summary reports with synopsis of mismatched data and overall comparison stats to pinpoint any potential errors.

Conclusion

AI-powered PL/SQL code conversion to Synapse and Snowflake can be a challenging task, but it is a necessary step for enterprises that are looking to modernize their data and move to the cloud. AI can be used to overcome the challenges of migration and accelerate data modernization initiatives.

While using AI can be a game-changer for modernizing your PL/SQL code in Synapse or Snowflake, building the right AI competencies and using optimal prompt engineering with Generative AI comes with its own set of challenges. Our team has gone through extensive trial and error to perfect the steps needed to effectively harness AI to successfully convert PL/SQL code. Explore our Data Migration and Modernization solutions to see how we accelerate PL/SQL code conversion.

The post How to use AI to modernize your PL/SQL code in Synapse or Snowflake appeared first on Bitwise.

The top five business factors that will make moving your data warehouse to the cloud a wise decision in 2023 will be discussed in this blog post.

1. Scalability and Flexibility:

Scalability is one of the main advantages of migrating your data warehouse to the cloud. With the use of a data warehouse, your company may simply scale up or down its IT needs as needed. For long-term success, businesses are experimenting with a variety of data modeling techniques. Cloud computing once again proves its mettle by being able to grow on demand and adapt to changing requirements because there is no one size fits all answer. Data warehouse modernization offers businesses an infrastructure that meets the purpose as and when necessary without integrating or optimizing difficulties thanks to autonomous scaling or de-scaling of servers, storage, and network bandwidth to manage massive volumes with unprecedented efficiency.

Conclusion:

In 2023, moving your data warehouse to the cloud will offer a variety of business benefits that will transform your company’s data capabilities. The cloud offers a complete solution to unlock the full potential of your data assets, from scalability and cost-effectiveness to improved performance, advanced analytics, and strong security. Businesses may maintain their agility, take quicker, data-driven choices, and gain new insights for innovation and expansion by using the cloud. To maximize the benefits and overcome any potential obstacles, make sure the migration is well-planned and executed with a smooth transition process.

Getting Started

While the benefits are numerous, and the technology matures, there can be many pitfalls on the path to migrating your data warehouse to a cloud environment. Understanding which platform and strategy can best help you achieve your business goals is a crucial first step. An experienced solutions provider should be able to help you conduct your cloud strategy and assessment to develop an implementation roadmap.

The post 5 Business Drivers for migrating your data warehouse to Cloud in 2025 appeared first on Bitwise.

1) Does Migrating the Data Integration or ETL Platform to Something Different Add Value?

When looking at migrating your Data Integration or ETL platform to another, you need to really look at the WHY behind it.

It’s not just the platform that adds or takes away value, it’s the environment it operates in that influences the platform largely.

It could be that the custom legacy code to update four different databases is insufficient to handle your increased data warehouse (DW) & business intelligence (BI) needs. Or you might be missing new platform feature sets like Data Profiling, Data Quality, Advanced Scheduling Features, etc. For example, the value for one Fortune 500 client was derived from being able to Eliminate out the business rules from the operational batch which were tightly coupled and thereby less adaptable to changing the business strategy.

The vendor licensing strategy for the platform and associated features it provides may be less than adequate over the course of time which forces you to reevaluate the impact on your departmental budget and productivity.

In any case, migrating your ETL system to a different platform is like running a marathon. In the short term, there is a distinct possibility that the migration itself costs more in terms of time and effort than the license agreement with the existing vendor for another year but over the long term would be more than worth cost borne.

The platform you pick needs careful consideration of not only the licensing costs but the Developer learning curve and other feature considerations like Data Profiling, Data Lineage, Data Quality, Scheduling, Source Control, connectors to Big Data platforms, etc. From an administration standpoint, vendor support, Ease of Maintainability and Ease of Administration Eliminate can the ability to scale up in terms of supporting the increasing demands in quantum of processing without any performance degrades. And having a good debate with your architect on open source and proprietary would come in handy.

2) How to derive the most value?

There was a classic comment on the Windows XP migration blog, “If you are thinking about moving your enterprise off Windows XP NOW- you are already too late.” This was April 2013 when there was a year still left for MS to support XP.

Very often, licensing agreements run till the end of the calendar year which coincides with the reporting and real-time information needs of the business. If the operations or technology people cannot guarantee the up-time of the warehouse and the relevance of its data, the business folks cannot depend on its predictive analysis and risk assessment of marketing or sales strategies.

Timing your migration with a view and in cooperation with the business team is critical to ensuring the success of your migration.

Moving from one ETL platform to another is a time-consuming process that needs thorough planning and execution. At Bitwise, some of the critical migrations we have been involved with have ranged from 18 man-months to 240 man-months. What can help reduce the effort is building platform reassessment into the life cycles of the applications using the tools and planning moving across vendors based on these reassessments.

Adding value by cutting costs (and not corners) is the way forward for today’s lean enterprises. This is challenging the set paradigms for design and architecture to offer solutions that don’t necessarily reinvent the wheel – but do away with it altogether.

It is forcing stakeholders like analysts, modelers, and design and development teams to leave the silos they work in and come up with an integrated approach. Like the separation of critical business SLAs on proprietary products and leaving non-critical functions on open source. The integration of the two then takes on its own criticality and needs to be treated with the same respect the SLA-linked business functions get.

3) How Do I Curb My Costs?

Before jumping on any new platform it is essential to run a scaled-down version of your enterprise application in a Proof of Concept.

Furthermore, it would be a good idea to break down the application into segments that need to be migrated vs. elements that can easily or seamlessly be ported over to the new platform. Scripts that are used by the Enterprise-authorized batch scheduler and XML Metadata that can be parsed to generate Source to Target mappings are a few of the examples of elements that are easily ported. However, custom scripts that are specific to administration or managing the execution of the product in your environment do not port easily and are better redone from scratch.

Metadata management and Data Lineage tracking is a key elements of any enterprise Data Integration/ETL solution and the most appropriate platform for your enterprise should be including this as a focused offering. Besides the help it provides the Analysts, having effective data lineage in place reduces the time required for setting up test data for the migration team.

As is with every critical system, budget for extensive testing and data validation while also allowing parallel execution of the old and the new systems with the same input data sets for Production validation.

To efficiently get this done, it may be a good idea to seek out tools/services that can automate such migration and associated testing from source to The target platform. Having a data validation suite in place that can be used by both the migration team as well as the end business users helps reduce turnaround time for the UAT.This would save the efforts of manual conversion and bring in efficiencies both from a cost and Time-to-market perspectives. Bitwise’s ETL Converter is a good candidate for such an automated migration service offering which can serve as your starting point.

And, with a view of assessing the platform you are migrating to in a couple of years down the line, ensure the thought process behind a particular course of action is as well documented as the action itself.

To Summarize

Migrating your Data Integration/ETL platform to another can always add value to your enterprise if assessed, planned, and executed.

A successful data migration project needs the team armed with a methodology, a proven migration automation tool/service with migration test automation. Connect with us if you want ballpark estimates to get you started on your migration project.

The post 3 Essentials For Migrating Your Data Integration (ETL) Platform appeared first on Bitwise.

1. BI Strategy

Organizations need to have a vision before they set themselves on a BI journey. Laying down a BI strategy with answers to below questions would help bringing clarity to this vision:

• What are the expectations from the BI implementation initiative?
• How are these expectations going to be achieved?
• Who will be the stakeholders?

Along with these answers, BI strategy needs to identify metrics and KPI’s that align with the corporate strategy and objectives (e.g. Increase Customer base, Increase Customer Satisfaction, 360⁰ Customer view, etc.). It should also contain a comprehensive approach describing the current and future behaviour of processes, technology, people and other components due to this implementation. The BI strategy should be treated as a living artefact and needs to be constantly tuned and adjusted to reflect the needs of the business.

Conclusion

BI Implementations can be costly and unpredictable sans experience of implementation. It is the experience which brings in a pragmatic roadmap that caters to business and technology keeping the 5 keys always in focus for a solution to work as well on day 100 as it did on day 1. Bitwise has worked on very large to medium scale BI deployments building some of its most complex solutions which it brings in as proven Reference Designs that help projects fall into a quadrant of success.

Do start a conversation with us if you are looking at any initiative in BI within your organization, we would be more than glad to bring in our experience.

The post 5 Keys To Nailing a BI Implementation appeared first on Bitwise.