Introduction

In Azure Synapse Analytics, you can combine data integration, data exploration, data warehouse, and big data analytics into an unlimited analytics service. Using one platform, users can combine their data engineering, data science, and machine learning needs without having to manage separate tools and processes.

What Capabilities Azure Synapse holds?

Utilizing the familiar SQL language, Azure Synapse allows users to query both relational and non-relational data. The data analysis and exploration can be performed either using serverless on-demand queries for ad hoc data analysis and exploration or using provisioned resources (dedicated SQL pool) for predictable and demanding data warehouse needs.

A serverless SQL pool provides access to external files stored in Azure storage without requiring data to be copied or loaded to another location using the T-SQL dialect. Synapse workspaces come with this service by default, so users can use it as soon as their workspace is created. With this approach, there is no infrastructure to maintain, and there is no cost associated with keeping services running. The service is priced on a pay-per-use basis, so costs are based only on the data processed by queries. Data budget limits (TB) can be used to control costs for data used in a day, week, or month.

An enterprise data warehouse can benefit from a dedicated SQL pool. The data is stored in tables with columnar storage, which improves performance and reduces costs. A massively parallel processing architecture is also used to run queries. This feature is not enabled by default in Azure Synapse Analytics. It is necessary to create a pool and select relevant performance levels, which can later be changed. The cost of a dedicated pool is determined per hour, but the cost can be controlled by scaling the service up and down when necessary. Pools can also be paused when not in use.

What are some features of Azure Synapse Analytics?

Apart from all these core capabilities, Azure Synapse Analytics also provides the following features:

Exploration of data lakes

It was not always easy to analyze data for some file formats or required additional tools. A Parquet file, for instance, is great to store but cumbersome to read since it is highly compressed. Using Synapse, we can right-click on a file and open it with a SQL script.

Language choice

Users may choose from T-SQL, Python, Scala, Spark SQL, or .Net for serverless or dedicated resources based on their preferences.

Managing and monitoring resources

Offering industry-leading compliance and security features. Single sign-on with Azure Active Directory integration.

Support for Delta Lake

This platform is compatible with Delta Lake from the Linux Foundation. An open-source storage layer that provides ACID (atomicity, consistency, isolation, durability) transactions to Apache Spark workloads and big data workloads. In addition, it includes time travel (data versioning) and handles scalable metadata.

Synaptic pathway for Azure

This tool simplifies and speeds up the migration of on-premises and cloud data warehouses to Azure Synapse analytics. By connecting to the source system, it inspects details about database objects and provides an assessment report.

Azure Synapse Analytics platform enables an accelerated time to insights with a unified analytical experience that allows for cost-saving analysis. It is a cost-effective service due to its intelligent architecture, which separates storage from computing resources. This gives businesses a great deal of flexibility. It is possible to scale up from small proof of concept project environment to a production environment. Likewise, if the need arises, resources can be paused to limit costs. All tools required by data engineers and data scientists are available in one place in this unified environment.

What is Azure Synapse Analytics? (Its Capabilities and Features)