In my last blog post (From Warehouse To Lakehouse – ELT/ETL Design Patterns With Azure Data Services) I talked about the different patterns that exist

Lakehouses, Data Warehousing, Big Data and Advanced Analytics
In my last blog post (From Warehouse To Lakehouse – ELT/ETL Design Patterns With Azure Data Services) I talked about the different patterns that exist
I like a good pattern. I also like a good framework. From a data perspective, nothing lends itself better to having both of these as
In a previous post I blogged about “Building the Datawarehouse-less Datewarehouse“, which is pattern I’ve always liked wherein we can build a logical star schema
Note – whilst I use the term Azure Data Factory in this article, the approach is supported by both Azure Data Factory AND Synapse Pipelines
This is just a short post, but it’s a prelude to a larger one I’m writing on record-linkage using Synapse Spark Pools with this step
Azure Automated Machine Learning is an awesome tool. It significantly lowers the bar of entry for data science by allowing folk to submit their datasets