Azure Synapse Analytics Tutorial

Let’s discuss a complete tutorial on Azure Synapse that includes what is Azure synapse Analytics?, its features, Azure synapse Analytics architecture, benefits and features of Azure synapse Analytics and the pricing details of Azure Synapse, etc.

Introduction to Azure synapse Analytics

Microsoft has introduced an excellent cloud-based data warehouse service that is known as Microsoft Azure synapse Analytics.

Earlier, it was known as Azure SQL Data Warehouse or, you can call it as the new version of Azure SQL Data warehouse. The better way of creating and managing your data is with the help of Azure synapse Analytics.

Azure synapse Analytics brings big data analytics and data warehousing together in one bucket.

Or, it is a single place or a one-stop destination where you can perform SQL in data warehousing (Synapse SQL), spark technologies used in Big Data (Apache Spark), Pipelines used for data integration, etc.

Basically, here, you will get a mixed environment with the combination of SQL, different data integration technologies, Apache Spark, etc.

Another interesting part is, Azure Synapse Analytics is well integrated with Power BI, Azure ML, Azure Cosmos DB, etc.

Azure Synapse Analytics Architecture

When you are thinking about the Azure Synapse Analytics Architecture, Note that mainly there are 4 main components that come into the picture. Those are as below.

Azure Synapse SQL
Azure Synapse Pipelines
Azure Synapse Studio
Apache Spark

Azure Synapse SQL

Azure Synapse SQL helps you provide SQL analytics. The SQL technologies that are used in the case of data warehousing Basically divided into two models.

Dedicated SQL Pool
Serverless SQL Pool

Dedicated SQL Pool

Here, if you will look closely at the below architectural diagram, you can notice, Control node is the main entry point for all your requests.

The requests or the queries are then submitted to the Massively Parallel Processing Engine, it process these queries and passes each of the queries to the compute nodes. Then the compute nodes are responsible to execute the queries and storing the user data on the Azure Storage.

The role of the DMS is to move the data across all the compute nodes during the query execution.

Serverless SQL Pool

In the case of Serverless SQL Pool, also the Control node is the main entry point and the central place. Here, the Distributed Query Processing (DQP) engine helps to optimize and converts the queries into smaller parts known as tasks and assign them to the compute nodes for execution.

Like the DMS in the case of a dedicated SQL Pool, the Distributed Query Processing (DQP) engine does the same type of work here.

Azure Synapse Pipelines

Responsible for creating and integrating with Pipelines. Pipelines are basically the grouping of different activities logically to perform a specific task or the pipelines are also called data-driven workflows.

Azure Synapse Studio

Apache Spark

Responsible for seamless integration with Apache Spark. You can easily create and configure Apache Spark pool in Azure with the help of Azure Synapse. It helps you to quickly process the data that you have stored in Azure.

Apache Spark basically helps you speed up the processing of your big data.

Related article: check out How to Create Azure Synapse Analytics.

Azure Synapse Analytics Features

Azure Synapse Analytics provides you with many wonderful features, Let’s discuss a few key features.

One-stop destination for SQL in data warehousing (Synapse SQL), spark technologies used in Big Data (Apache Spark), Pipelines used for data integration, etc. So at a single workspace you are getting all these services.
It has the capability to query a massive data without any issue.
Seamless integration with Azure Cosmos DB, Azure ML, Power BI, etc.
It supports a number of scripting language like SQL, .Net, Python, T-SQL, Java, etc.
It is highly secure. So nothing to worry about your data, your data is safe.

When to use Azure Synapse Analytics

Note that compared to earlier, Azure Synapse Analytics provides you with a bunch of new features. Based on the latest features of Azure Synapse Analytics, here we are presenting a list of Azure Synapse Analytics use cases.

As a developer, when you have the requirement to query the data lake, you can use the SQL on-demand pool (The latest feature of Azure Synapse Analytics).
For latest and enhanced reports, you can utilize the benefits of Power BI. Access Power BI from the Azure Synapse Analytics Studio itself.
All the requirement for the analysis and development of SQL technologies, blindly use Azure Synapse Analytics. Where you will get all the SQL features, T-SQL supports, Stored procedures and many more.
For the Data warehousing requirements, start using Azure Synapse Analytics.

Benefits of Azure Synapse Analytics

Below is a list of Azure Synapse Analytics Benefits.

It is simple and user friendly.
It is faster meaning process your data at faster pace.
You can save a lot as part of your cloud expense.
Can easily handles many concurrent requests at the same time.
It is well integrated with Power BI, Azure ML, Azure Cosmos DB, etc.

Azure Synapse Analytics Pricing

You can enjoy the pay-as-you-go pricing model and you will get some discounts on this model in the case of Azure Synapse Analytics.

Tier Details	Number of Synapse Commit Units	Percentage of Discounts	How much do you have to pay?
1	For 5,000 units	You will get a discount of 6%.	You need to pay 4,700$ only.
2	For 10,000 no of units	You will get a discount of 8%.	You need to pay 9,200$ only.
3	For 24,000 no of units	You will get a discount of 11%.	You need to pay 21,360$ only.
4	For 60,000 units	Get a discount of 16%.	Pay 50,400$ only.
5	For 1,50,000 no of units	Get a discount of 22%.	Pay 1,17,000$ only.
6	For 3,60,000 units	Get a discount of 28%.	Pay 2,59,200$ only.

For more information on pricing for Azure Synapse Analytics, you can check out here.

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Wrapping Up

Well, in this article, we have discussed a complete tutorial on Azure synapse Analytics. Hope you have got an idea on Azure Synapse Analytics by now.