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If you’re anything like us—and if you’re reading this, you probably are—you’ve found yourself in a position where you’ve created 60+ interdependent (600+ line) data transformations defined in SQL as a core step of your ETL. You may even be sitting there thinking how did I get here, and what do I do now?
All of your queries are running, and your SQL-defined charts are returning results, but when it’s maintenance time, you don’t have a clear picture of the cascade of views, tables, and CSV files that your analysis depends on. At least it’s lucky that we’re good analysts, and we don’t make mistakes like circular references … right. How many of you ran straight for the Git repo?
You can sit down and start coding directed acyclical graphs (DAGs) in something like Airflow, but that’s a laborious task that involves reading through tons of SQL files and recording dependencies.
To solve this Sisense for Cloud Data Teams has released dependency mapping, enabling you to trace back your ETL process across your analytics stack, from ingestion to final analysis. This is an incredibly powerful feature that will help you optimize, eliminate or simply understand the ETL processes driving your companies analytics!
Check it out in our docs!
To solve this Sisense for Cloud Data Teams has released dependency mapping, enabling you to trace back your ETL process across your analytics stack, from ingestion to final analysis. This is an incredibly powerful feature that will help you optimize, eliminate or simply understand the ETL processes driving your companies analytics!
Check it out in our docs!
