Azure Synapse Analytics is a single solution for all data needs like ingesting, processing, and serving the data. It delivers the unified experience of data integration, data warehousing, and big data analytics in a single workspace environment. Azure Synapse analytics can be easily integrated with other services provided by Azure like Azure Machine Learning, CosmosDB, and PowerBI. Microsoft rebranded SQL Data Warehousing into Azure Synapse Analytics with many new functionalities and features apart from integrating most of the services into it.
Before Azure Synapse Analytics, there were multiple tools that must be used for big data processing in the Azure cloud. Some of them are listed below which needs individually worked upon step by step to get the desired result.
- Azure data factory
- Azure data lake
- Azure databricks
- SQL data warehousing
- PowerBI
After the introduction of azure synapse analytics, you can perform all these in a single service. All you need is to create an azure data lake storage and then create an azure synapse analytics workspace on top of it to get started with your ETL operations, Big data processing, and PowerBI reporting integration -yes, PowerBI can be directly connected to synapse analytics to generate reports.
The main advantage in synapse over other individual services is the freedom to query data on your own terms using serverless or dedicated resources at scale.
It brings together the 3 main components under a single entity…
- SQL technology used in data warehousing as synapse SQL
- Apache spark processing
- ADF pipelines for ETL/ELT as synapse pipelines
![What does Azure Synapse Analytics do?]()
The above image explains the best on what are the tools that must be dealt with big data processing earlier and how azure synapse analytics simplified the entire ecosystem now.
As with any other azure data service, Microsoft provides us with a rich UI to work on called azure synapse studio. Using this not only can create pipelines but also, we can use it to manage, monitor, and secure all our workflows. Currently, Azure Synapse offers two analytics runtimes namely SQL and Apache spark which we can use as per our needs. The DataLake storage that synapse can connect directly is Azure DataLake gen2 apart from this it supports 90+ data sources to ingest from.
Summary
This is a primer or starting step to know what azure synapse analytics is. I have planned a series of blogs on how to implement a synapse analytics project in real-time with the demo.
Reference
Microsoft official docs