Tools & Technology

Azure Data Factory

When I started working with ETL tools, it was mostly on-premises with SSIS or TimeXtender, but I wanted to transition to a full cloud solution so Azure Data Factory was the native choice in the Microsoft ecosystem supporting great ingestion capabilities combined with state-of-the-art orchestration and high-level data transformation introducing – for me – the idea of lakehouses.

Azure Synapse Analytics

Need to add better transformation capabilities and data warehousing (for me especially the serverless options did the trick for a lot of use-cases!) to Azure Data Factory stack – then Azure Synapse Analytics should be the stack to chose. Easy to spin up Spark cluster to run notebooks that can be used to transform data in a scalable way.

Azure Databricks

Great user interface and user experience paired with single or multiple node clusters to share are a great way to do transformations in Spark. That’s how got into Databricks and I really like the Data Warehousing possibilities and unity catalog options Databricks offers: great value for money lakehouse potential and scheduling, paired with great performance and user/access management.

Power BI

During my career I saw a lot of BI tools like QlikView, QlikSense, Tableau or MicroStrategy – all great choices, but Microsoft Power BI really convinced me from the beginning. It has a great community with a lot of resources and is constantly developed by keeping backwards compatibility in place. The Power BI team is doing a great job and it integrates like no other BI tool in Microsoft environments following best practices.

Microsoft Fabric

Finally, Microsoft is setting the industry standard creating a fully integrated data engineering and analytics platform in one place. And the best part: all based on already existing concepts like Azure Data Factory, Azure Synapse Analytics and Power BI.