Want to learn Azure Synapse Analytics? You are in the perfect place. In this Azure article, we will learn a complete tutorial on Azure Synapse Analytics, earlier known as Azure SQL Data Warehouse.
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Azure Synapse Analytics Tutorial
Let’s discuss a complete Azure Synapse tutorial that includes what Azure Synapse Analytics?, its features, Azure Synapse Analytics architecture, benefits and features of Azure Synapse Analytics is, and the pricing details of Azure Synapse, etc.
Introduction to Azure Synapse Analytics
Microsoft has introduced an excellent cloud-based data warehouse service known as Microsoft Azure Synapse Analytics.
Earlier, it was known as Azure SQL Data Warehouse or, you can call it, 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.
Here, you will get a mixed environment with the combination of SQL, different data integration technologies, Apache Spark, etc.
Another exciting part is Azure Synapse Analytics is well integrated with Power BI, Azure ML, Azure Cosmos DB, etc.
Azure Synapse Analytics Architecture
When thinking about the Azure Synapse Analytics Architecture, Note that 4 main components come into the picture. Those are as follows.
- 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 look closely at the architectural diagram below, you can notice the 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, which processes these queries and passes each of the questions to the compute nodes. Then, the compute nodes execute the queries and store 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 a Serverless SQL Pool, the Control node is the main entry point and the central place. Here, the Distributed Query Processing (DQP) engine helps to optimize and convert 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 the grouping of different activities logically to perform a specific task, or the pipelines are also called data-driven workflows.
Responsible for seamless integration with Apache Spark. You can easily create and configure the Apache Spark pool in Azure with the help of Azure Synapse. It helps you quickly process the data you have stored in Azure.
Apache Spark 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 many beautiful features. Let’s discuss a few key elements.
- 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 can query massive amounts of data without any issues.
- Seamless integration with Azure Cosmos DB, Azure ML, Power BI, etc.
- It supports many scripting languages like SQL, .Net, Python, T-SQL, Java, etc.
- It is highly secure. So, there is nothing to worry about your data. Your data is safe.
When to use Azure Synapse Analytics
Note that Azure Synapse Analytics provides you with many new features compared to earlier. 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 the latest and enhanced reports, you can utilize the benefits of Power BI Access Power BI from the Azure Synapse Analytics Studio.
- All the requirements for analyzing and developing 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 it processes your data at a quicker pace.
- You can save a lot as part of your cloud expense.
- It can easily handle 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.
|Number of Synapse Commit Units
|Percentage of Discounts
|How much do you have to pay?
|For 5,000 units
|You will get a discount of 6%.
|You need to pay only 9,200$.
|For 10,000 no of units
|You will get a discount of 8%.
|You need to pay 9,200$ only.
|For 24,000 no of units
|You will get a discount of 11%.
|You need to pay 21,360$ only.
|For 60,000 units
|Get a discount of 16%.
|Pay 50,400$ only.
|For 1,50,000 no of units
|Get a discount of 22%.
|Pay 1,17,000$ only.
|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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In this article, we have discussed a complete Synapse tutorial. I hope you have got an idea of Azure Synapse Analytics by now.
I am Rajkishore, and I have over 14 years of experience in Microsoft Azure and AWS, with good experience in Azure Functions, Storage, Virtual Machine, Logic Apps, PowerShell Commands, CLI Commands, Machine Learning, AI, Azure Cognitive Services, DevOps, etc. Not only that, I do have good real-time experience in designing and developing cloud-native data integrations on Azure or AWS, etc. I hope you will learn from these practical Azure tutorials. Read more.