This article was originally written by Avanade alumn Luke Pritchard.

Let’s face it – we all want things simpler, whether at work or at home. And simple for data engineering means simplifying the ability to work across different data sources, languages, and methods through a consistent interface. For organizations looking to accelerate their digital transformations, scaling the use of data, analytics and AI across their enterprise comes at a premium. Empowering data engineering to use the skills and knowledge they already have, to accelerate business outcomes is a critical component of moving fast and at scale.

At this year’s Ignite, Microsoft introduced Azure Synapse Analytics with data engineering squarely in mind. Azure Synapse Analytics provides a single tool for data engineering to support self-service data provisioning, reporting and data analytics. In an average Azure data pipeline, data engineers may interact with many different tools (e.g. ADF, Azure Data Explorer, Azure Databricks, Azure SQL, Azure Analysis Services, Power BI) each with their own interface and intricacies. Azure synapse analytics simplifies this experience drastically by offering the capability to build end-to-end data pipelines through “a single pane of glass.” This move by Microsoft effectively brings together data warehousing and big data analytics into one unified experience.

Database administrators and citizen data engineers and data scientists can now also benefit from Azure Synapse connecting data and insights to the business process data and logical business acumen to drive insight from vast amounts of data. Azure Synapse Analytics native integration with the rest of the Azure platform drives unparalleled security and the ability to test to find value quickly and then effectively scale across a business process within one tool.

For organizations looking to scale use cases with an end-to-end pipeline that integrates from Power BI and to SQL tools, leverage Azure Cloud-native services, full T-SQL support, Spark notebooks and Azure Data Flows, this is a great one-stop shop approach. For those looking for additional scale, Databricks provides fast data transfers between data services and support for data streaming in addition to their Mapping Data Flows features. The good news is that both Azure Synapse and Azure Databricks can run analytics on the same data in Azure Data Lake Storage. Azure Synapse compliments the Databricks story in that it offers a data engineering, visualization, and next-generation data warehousing. We see Azure Synapse and Azure Databricks as the two core services supported by the broader Azure platform that will enable companies to drive data analytics and AI at scale within their organizations to truly transform their enterprises.