We’ve created reusable frameworks for ELT and ETL paradigms which allow us to quickly achieve consistent naming conventions, auditable processes, and easily-understood lineage for the ingestion pipeline.

Taking advantage of modern tools on the market, we standardize transformation processes so that they can be easily understood at a high level. We believe the most important part of transformation isn’t transformation itself; it’s creating a process that can be replicated, interrupted, iterated on, and understood.

As part of our data integration consulting best practices, our processes will first try to dynamically adjust and compensate for any structural changes in the data. If this cannot be achieved dynamically, our processes will highlight the culprits that could cause data integration to fail (and alert you to the situation!), allowing you to quickly remedy the problem.

Data scientists and analysts no longer have to spend the majority of their time cleansing data for analysis. In a modern data stack, data cleansing is handled programmatically within the stack so that data scientists and analysts can spend their time solving complex business problems and uncovering opportunities.

There are costs associated with the movement, transformation, and integration of big data; data storage; and the upfront and ongoing costs of data stewardship. For these reasons, you may not actually want to integrate all of your data. We have best practices and tools that help prioritize which data to integrate – and which data to NOT integrate – so you can optimize your data integration processes and reduce cost.