In this article, you will learn about Azure Synapse Architecture and its different components.

To stay competitive, companies must collect, analyze, and utilize data effectively. This requires a robust data management strategy encompassing data quality, governance, and integration. 

  1. While traditional on-premise integration systems have been used for years, the trend is moving towards cloud-based solutions due to their flexibility and scalability.
  2. When transitioning to the cloud, companies must carefully consider various factors, including workload requirements, database platforms, storage systems, and data security standards, among others. 

Components of Azure Synapse Architecture

Synapse SQL

  • Azure Synapse SQL is a distributed SQL engine designed for large-scale data processing. It is based on Microsoft SQL Server and can handle petabyte-scale data workloads.
  • Azure Synapse SQL allows users to query data stored in various sources, including data lakes, data warehouses, and operational databases, using standard SQL syntax.
  • It also supports both serverless and dedicated options for processing queries, depending on the user’s needs.
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Dedicated SQL Pool

  • A dedicated SQL pool in Azure Synapse architecture is a fully managed, cloud-based, and optimized data warehouse.
  • It provides an enterprise-grade solution for managing and querying large datasets. A Dedicated SQL pool is a highly capable distributed query engine that employs Massively Parallel Processing (MPP) technology, making it ideal for handling large-scale data warehousing workloads.
  • The aim of utilizing Dedicated SQL Pools is to effectively store a large amount of data while being able to perform queries efficiently.
  • This is facilitated by storing the data in a columnar format and taking advantage of clustered columnstore indexing to enable swift retrieval.
  • Its performance is determined by the Data Warehouse Units (DWUs) we choose while creating the resource.
  • One of the benefits is that we can temporarily halt its resources to save on costs and start it again whenever required. However, you should note that storage costs still apply even when the Dedicated SQL Pool is paused.

Serverless SQL Pool

  • The Serverless SQL Pool is a new type of pool that enables users to run ad-hoc SQL queries. This is done against data stored in Azure Data Lake Storage (ADLS) and Azure Blob Storage without the need to manage any infrastructure.
  • The Serverless SQL Pool is a tool for querying data in your data lake. You can use it to access your data with T-SQL syntax, which lets you query the data without needing to copy or load it into a specific storage location.
  • You can also connect to the tool through the T-SQL interface and use various business intelligence and ad-hoc querying tools, including commonly used drivers.
  • In a Serverless SQL Pool, resources are automatically provisioned and scaled based on the workload requirements, and users are charged only for the queries they run.
  • This means that users can quickly and easily query large volumes of data without worrying about managing resources and only pay for the resources they use.
  • The Serverless SQL Pool also provides built-in security and compliance features such as column-level security, data masking, and auditing. Additionally, it integrates with other Azure services, such as Power BI and Azure Data Factory. This enables users to create end-to-end analytics solutions.


  1. Apache Spark is a framework that can process data in parallel and uses in-memory processing to increase the speed of big data analytics applications.
  2. Microsoft has its own version of Apache Spark in the cloud, called Apache Spark in Azure Synapse Analytics. With Azure Synapse, it’s simple to set up a serverless Apache Spark pool in Azure.
  3. These pools are compatible with both Azure Storage and Azure Data Lake Generation 2 Storage, which means that you can use them to process data that is stored in Azure.
  4. Apache Spark offers tools for performing distributed computing in-memory. Using Spark, a job can load and store data in memory and access it multiple times.
  5. This approach is significantly faster than using disk-based applications. Additionally, Spark has built-in support for multiple programming languages, enabling users to manipulate distributed data sets like they would with local collections without having to conform everything to map and reduce operations.
  6. Spark in Azure Synapse Analytics is fully managed and comes with pre-installed libraries for Python, R, and Scala.
  7. The Spark component in Azure Synapse Analytics is highly scalable, enabling users to process large amounts of data quickly and efficiently. It also integrates with other Azure services such as Azure Data Factory, Azure Stream Analytics, and Azure Event Hubs.

Synapse Pipelines

  • Azure Synapse Pipelines is a cloud-based data integration service that allows users to create, schedule, and manage data pipelines.
  • It is part of the Azure Synapse Analytics workspace and provides an intuitive graphical interface for building and managing data pipelines.
  • With Synapse Pipelines, users can connect to various data sources, transform and process data using a wide range of built-in transformations and custom code, and load the data into various destinations, including Azure Synapse Analytics, Azure Data Lake Storage, and Azure SQL Database.

Synapse Studio

  • Azure Synapse Studio is a web-based integrated development environment (IDE) that provides a unified experience for data integration, data warehousing, big data, and AI tasks.
  • It is a key component of the Azure Synapse Analytics architecture and provides a collaborative workspace for data engineers, data scientists, and business analysts.
  • Synapse Studio allows users to create and manage multiple Synapse workspaces from a single location.


  1. Now you know all about Azure Synapse architecture. Azure Synapse is an analytics solution that combines enterprise data warehousing and Big Data analytics.
  2. You have the choice to query data at scale utilizing serverless or dedicated resources as you see fit.  It helps financial services by guaranteeing data security. 

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Sharon Rithika
Content Writer, Hevo Data

Sharon is a data science enthusiast with a hands-on approach to data integration and infrastructure. She leverages her technical background in computer science and her experience as a Marketing Content Analyst at Hevo Data to create informative content that bridges the gap between technical concepts and practical applications. Sharon's passion lies in using data to solve real-world problems and empower others with data literacy.

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