AWS Redshift vs Azure Synapse Analytics – A Comparison (Part 1)

Learn the top differences between AWS Redshift and Azure Synapse Analytics

The 21st century has witnessed rapid developments and advancements in the world of IT and technology. The introduction of cloud technology lies at the core of these developments. Data is critical to the smooth functioning and progress of any business and to store this data securely, businesses now opt for on-demand cloud services that can store large data sets securely in a single place.

Cloud services such as Data Warehouses have played a critical role in storing structured, semi-structured, and unstructured data at a very low cost. A data warehouse is a data management system that is used for the storage, reporting, and analysis of organizational data. Amazon Redshift, Azure Synapse, Snowflake, Google BigQuery, etc are some of the most popular Data Warehouses that are used by businesses.

These data warehouses provide storage and analytics services that enable businesses to make important and informed decisions. In this article, we will explore the key differences, similarities, features, and working of the two most widely used data warehouses i.e. AWS Redshift and Azure Synapse Analytics.

Let us begin by understanding the key features offered by both these data warehousing solutions.

Amazon Redshift

  • Amazon Redshift can process large data sets which lie in the range of exabytes. Many multi-billion dollar companies such as Delivery Hero, Nubank, Lyft, Figma, etc use Amazon Redshift for storing, processing, and analysis of their data.

AWS Redshift works by using Massively Parallel Processing Technology (MPT) which enables the warehousing solution to analyze and process large chunks of data at a very high speed. It not only provides a high processing speed but ensures the security and privacy of data as well, thanks to the excellent data encryption services it offers.

  • Data is at the disposal of users with just a few clicks. It offers multiple useful features such as Automated Table Design, Query Editor, Fault tolerance, and Network Restriction which allow the smooth and secure processing of data.

Key Features of Amazon Redshift

Following are some noteworthy features of AWS Redshift:

  • AWS Redshift has amazing automation capabilities which save users from repeating mundane tasks such as generating reports. The system performs these tasks automatically.
  • Using the same parameters, AWS Redshift can query large sets of data in several different ways. This intelligent optimization allows the user to query data with different sets of tools that are needed for each scenario.
  • AWS Redshift is built on PostgreSQL. This makes Redshift an SQLFriendly data warehousing solution.

Azure Synapse

azure synapse analyticsAzure Synapse is a modern Data Warehousing service that combines data integration, data warehousing, and Big Data analytics. Azure Synapse enables the users to query data using different tools. The services are scalable and available on an on-demand basis.

Azure Synpase comprises the following four major components:

  • SQL Pool and SQL On-demand: it is a collection of analytic resources that are applicable in data warehousing.
  • Apache Spark: Applicable for Big Data
  • Synapse Pipelines: An integrated platform service that combines data integrations, ETL pipelines, analytics tools & services, visualization & dashboards.
  • Synapse Studio: An important tool that is used for the administration and operation of different features of Azure SQL Analytics.

Key Features of Azure Synapse

Following are some noteworthy features of Azure Synapse:

  • It operates on Massively Parallel Processing (MPT) Technology which enables the warehousing solution to process big data sets in a fraction of a second.
  • Due to the implementation of Hybrid Transactional and Analytical Processing (HTAP) technology, real-time integration between Azure Synapse and Azure Databases is possible in the system.
  • Synapse can pair up with Azure Data Share which enables the sharing of Data Lake and Data Warehouse internally and externally.

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