The Evolution of SQL Server from Database into an Analytical Platform

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Azure SQL Data Warehouse (SQL DW) is Microsoft’s cloud-based Platform-as-a-Service (PaaS) for massive, structured relational databases. SQL DW offers petabyte-scale due to its distributed architecture and use of parallel processing. A fully managed cloud service offering independently-scaled compute and storage, SQL DW is an excellent choice when looking to store big relational data on cloud with highly performant queries, and performs better than competitor offerings including Amazon Redshift, Google BigQuery and Snowflake DW.

In terms of both feature set as well as its shift from premise to the cloud, SQL DW is, to a certain extent the “big brother” of SQL Server, albeit with a slightly different purpose. To better appreciate SQL DW, I thought, therefore, to review the evolution of SQL Server and how its innovation has enabled new personas of data consumers.


In today’s increasingly data-driven business world, the requirement for an analytics and data engineering platform which provides capabilities with minimum disruption and smooth upgrade is both technically critical and strategically necessary. Microsoft SQL Server enables and activates these capabilities for data engineers, data scientists, and business analysts by enhancing those features with each release. This allows these personas to adopt these new features without transitioning their skills to analyze data within SQL Server.


These features are integrated within the SQL Server engine, which results in easy adoption without replicating the dataset. The insight derived from the analytical workload can be directly stored and consumed as an insight from same location reducing the additional hop to store the outcome persistently. This also allows reporting to be more performant by making the storage the single source of truth.