Azure Synapse Analytics is a cloud-based analytics service of Azure. It combines big data and data warehousing capabilities into a single unified platform. Synapse Analytics integrates various data processing and analytics components, enabling organizations to ingest, prepare, manage, and serve data for business intelligence, machine learning, and real-time analytics purposes.
Introduction
Today, we will look into a big data handling, processing, and presenting service from Microsoft Azure. This is called Azure Synapse Analytics. If you google this term, you will certainly find many articles on it. However, most of these are difficult to understand, making the service quite complex. The truth is really contrary to this. This is a very simple service to understand and implement. I will explain the main components of this service using a simple diagram.
What we had before
In the past, we had some ETL tools. This stands for Extract, Transform, and Load. These included tools like SSIS. In these tools, we simply loaded data from different sources, did some transformations on the data, and then loaded it into another destination store. From here, we could generate reports, etc. One good example is the SSRS tool. This data could also be loaded into a data warehouse structure and then processed using SSAS. Time went on, and we moved into the cloud and got a service in Azure called Azure Data Factory to load and transform data and store it in some destination store. From here, we could run an analysis of the data and showcase it using Power BI reports. Again, Power BI reports can be seen as a platform to present the data in a friendly way, and the main strength of it was that these reports could be created with low or no code required. Hence, a great tool for business users with no programming experience.
Azure Synapse Analytics
As you can see in the above paragraph, we needed a number of tools to complete the process of collecting data from different sources, processing it, storing it, and finally presenting it for consumption. Hence, we were given Azure Synapse Analytics, which covers all these things inside one tool.
![Azure Synapse Analytics]()
In the above diagram, we see that we feed data from different sources into Azure Synapse Analytics. This is stored in an Azure Data Lake. From here, we create Data pools which are nothing but stores of our transformed data. Hence, we have the data factory, which imports and stores the data, and the data warehouse, which transforms and stores the data into a SQL database in the case of SQL pools. We also have Spark pools for Apache Spark data. I will not cover this topic here and focus on the SQL data. Finally, we can use Power BI tools to connect to the SQL warehouse tables/pools and use it to create business reports and dashboards.
Key features of Azure Synapse Analytics
Here are the key features of Azure Synapse Analytics:
Data integration: It offers built-in connectors and data integration capabilities to ingest data from various sources such as Azure Data Lake Storage, Azure Blob Storage, Azure SQL Database, and more.
Data warehousing: Synapse Analytics provides a dedicated SQL-based engine called "SQL pools, " enabling users to perform powerful, high-performance analytics on large datasets. It supports both relational and non-relational data models.
Big Data processing: The service incorporates Apache Spark and Apache Hadoop frameworks to process large-scale data workloads. Users can leverage familiar tools like Python, Scala, and .NET for data transformations, machine learning, and advanced analytics.
Data exploration and visualization: Synapse Analytics integrates with Azure Synapse Studio, a collaborative workspace that allows data engineers, data scientists, and analysts to explore and visualize data using notebooks, dashboards, and drag-and-drop interfaces.
Advanced analytics and machine learning: The service provides integration with Azure Machine Learning, allowing users to build, deploy, and manage machine learning models at scale. It supports the use of popular frameworks like TensorFlow, PyTorch, and scikit-learn.
Security and governance: Azure Synapse Analytics offers robust security features, including encryption at rest and in transit, Azure Active Directory integration for authentication and authorization, and fine-grained access controls. It also supports data classification, data masking, and auditing for regulatory compliance.
Summary
This is Azure Synapse Analytics in a nutshell. A simple to use but very powerful tool. All your data processing needs are in one place. In addition, it comes with a very friendly tool called Azure Synapse Studio, which lets you do all the steps from the browser.