Factors to Consider: Data Warehouse Modernization

A successful data warehouse migration or modernization journey needs a great deal of planning. As part of the planning, you will have to choose between various products/end points and the path they will have to offer you. 

Below are some of the factors that may help you to consider the right path forward.

Platform

Choosing the right provider carefully is the first and most critical part of your journey. Evaluate their service offerings (IaaS/SaaS/PaaS), cost and pricing structure, scalability, and performance factors based on your needs. Security and compliance are major deciding factors and ensure you bring your security teams early in the decision-making process. Choosing the right availability zone and DR capabilities ensures that you do not hit performance issues later. 

Endpoints

While modernizing it is also equally important to look at decommissioning your legacy systems and databases which might become performance and scalability bottlenecks as you start dwelling into AI models that need massive amounts of data and faster data retrievals. Choose an equivalent cloud solution or database and this can also play a key role in deciding your cloud vendor. The closer the database semantics and syntax the easier the migration is going to be.

ETL Product Suite

Understand the scale of your current ETL or integration code, and patterns of the code and evaluate vendors that offer strong integration capabilities around the new areas of cloud solution or database systems to which you are inclining.

Choose an ETL vendor that offers cloud services for your code runtime capabilities, and that has vast support for cloud use cases and AI capabilities embedded. This will enable you to completely modernize and scale your solution with no dependencies on any on-premise aspects.

Code Conversion Aspects

A thorough initial way of assessment of the code will go a long way in the conversion of code. Identifying patterns is equally important and is essential for easy conversion of the code. Consider code conversion utilities that support repointing to the new databases, maximum percentage of automated conversion capabilities with these endpoints, reconciliation methods, pattern-based conversion, code optimization, and bulk tuning capabilities post-conversion. 

Beyond ETL

Look at all the other aspects like external scripts, schedulers, and custom code in the ETL flows that control the overall orchestration of the code and plan to modernize them as well. Decommission anything that might still need on-premise components and the effort that is involved in rewriting some of this will go a long way in your modernization journey. Invest now rather than this becoming a performance bottleneck later.

Phased Approach

This journey and overall effort may be massive but planning it phase-wise will allow you to focus on each of the parts of this modernization and achieve the goals step by step in a planned and organized way. Identify the dependencies, mark priorities, and always start with less business-critical flows to embark on your learnings.

AI-Powered Capabilities

The struggles of your existing team learning and implementing using a new platform are real. Instead of rehiring or depending solely on the expert partners it's worthwhile to consider AI-powered platforms/vendors. While choosing and planning for the Cloud/Db/ETL/Conversion vendors incline towards the ones that support AI capabilities for Training, Support, or Monitoring at the minimum. This will help your team to easily adapt to the new world and learn and implement quickly. If the vendor also supports building your use cases or system in an automated way that is an added benefit.

Research into each of these areas is very critical and involving the right team of experts who understand your current implementations to think through the above factors will help you decide the right path for modernization.