This article is based on reporting originally published at Hindustan Times (December 2019) covering Microsoft’s announcement of Azure Synapse Analytics.
What Is Azure Synapse Analytics?
Microsoft announced Azure Synapse Analytics as a new cloud service designed to help large enterprises consolidate, query, and act on the massive volumes of data stored across their corporate systems. The service was unveiled at a Microsoft event in Florida and sits within the company’s Azure cloud computing portfolio.
According to Rohan Kumar, Corporate Vice President of Azure Data at Microsoft, the platform was built specifically to address two persistent pain points that organizations face when building enterprise-scale analytics pipelines.
Problem 1: Handling Both Structured and Unstructured Data
Traditional enterprise analytics stacks require separate toolchains to handle different data shapes. Structured data — such as customer records stored in relational databases, where information sits in clearly defined rows and columns — is handled by one set of tools. Semi-structured or unstructured data — such as the streams of clickstream events, timestamps, and behavioral signals generated by web monitoring platforms — typically requires a completely different set of tools and infrastructure.
Azure Synapse is designed to handle both data types within a single service, removing the need for engineering teams to maintain separate processing pipelines for relational and non-relational workloads.
Problem 2: Reducing Engineering Overhead Through Automation
Building an end-to-end data analytics system traditionally requires significant developer and data engineer involvement — ingesting data, transforming it, provisioning compute resources, writing queries, and wiring together visualization layers. Azure Synapse aims to automate a substantial portion of this orchestration work, reducing the manual programming effort required to stand up a functioning analytics environment.
Kumar stated that projects which previously took multiple months for customers to complete were being finished within a single day using the Synapse platform.
“What we’re seeing with our customers is these projects that would typically take multiple months are now getting completed within a day.”
Rohan Kumar, Corporate Vice President of Azure Data, Microsoft
Early Adopters and Use Cases
At the time of the announcement, Microsoft named Deutsche Bank and Unilever among the organizations testing Azure Synapse in early access. These enterprises represent two distinct industry verticals — financial services and fast-moving consumer goods — suggesting that the platform’s unified analytics approach is applicable across a range of data-intensive business contexts.
A representative example use case cited by Microsoft is measuring whether a digital marketing campaign is generating measurable increases in traffic to physical stores and e-commerce sites — a problem that inherently requires joining structured transactional data with unstructured behavioral signals from web analytics platforms.
Azure Synapse in the Context of Azure’s Data Platform
Azure Synapse builds on the foundation of Azure SQL Data Warehouse and integrates tightly with other Azure services, including Azure Data Lake Storage, Azure Machine Learning, and Power BI. This positions it as a central hub in Microsoft’s broader cloud data architecture rather than a standalone product.
For DevOps and data engineering teams already operating within the Azure ecosystem, Azure Synapse represents an opportunity to consolidate tooling, reduce inter-service data movement, and apply unified access controls and monitoring across both batch and real-time analytics workloads.
Key Takeaways for Data Engineering Teams
- Unified data handling: One service for both relational (structured) and non-relational (semi-structured/unstructured) data sources.
- Reduced time-to-insight: Automated orchestration and provisioning reduce the engineering effort required to build production analytics pipelines.
- Enterprise validation: Early adoption by organizations such as Deutsche Bank and Unilever indicates suitability for high-volume, compliance-sensitive environments.
- Azure ecosystem integration: Deep integration with Azure Data Lake, Azure ML, and Power BI enables end-to-end data workflows without leaving the Azure platform.
Azure Synapse Analytics has continued to evolve since this announcement. Teams evaluating the platform should consult the current Microsoft Azure documentation for up-to-date feature availability, pricing, and service limits.