Data Processing Art with Azure Synapse Analytics

Azure Synapse Analytics has made a name for itself! By consolidating all data operations such as big data analysis, data merging, visualization, and storage in one place, it offers users the ability to quickly and easily access their data and obtain predictions. Its features, which provide serverless and private resource models for data engineers, analysts, and scientists, such as Azure Synapse SQL, make it possible to work with descriptive and explanatory analytic scenarios. In summary, Azure Synapse Analytics is a game-changing platform for data analysis!

With its features, data storage options, performance, and scalability, Azure Synapse Analytics makes jobs faster and easier. This article will discuss what Azure Synapse Analytics does, how it works, and when it is used.

We will follow certain subjects from Abdullah Kise’s course “DP-203T00: Data Engineering on Microsoft Azure” in this series. Additionally, I want to thank Abdullah Kise for his insightful advice.

What is Azure Synapse Analytics used for?

Azure Synapse Analytics is an analysis platform that consolidates big data analysis, data merging, data visualization, and data storage operations in one place. Users with all the capabilities can quickly access all their data and easily obtain predictions. Thanks to its innovative performance and scalability, it can make jobs faster and easier.

How does Azure Synapse Analytics work?

If you don’t have an analytical environment, Azure Synapse Analytics can act as a one-stop-shop in an integrated environment to meet all your analytical needs. It provides the following capabilities:

Analytic capabilities through SQL pools or serverless SQL pools

• Azure Synapse SQL is a distributed query system that enables data engineers to implement data warehousing and data virtualization scenarios using standard T-SQL experiences. Synapse SQL offers serverless and private resource models to work with descriptive and explanatory analytic scenarios.

Storage considerations when using Azure Synapse serverless SQL pools

Organizations deploying on-premises applications in performance-sensitive clouds should be aware of the importance of having cost-effective data storage options with different levels of performance. Azure Blob Storage offers two different performance tiers:

Premium: Optimized for high transaction rates and single-digit consistent storage latency.

Standard: Optimized for high capacity and efficiency.

Azure Data Lake Storage Gen2 provides a price advantage in object storage scale and file system performance, thanks to its hierarchical namespace feature. This feature allows objects/files in an account to be organized in a hierarchical directory and nested subdirectory structure just like your file system on your computer.

Note: If you’re looking for the best performance for your Serverless SQL pool, I recommend using the premium tier of Azure Data Lake Storage Gen2. However, keep in mind that this option is one of the most expensive options.

When to use Azure Synapse Analytics?

The widespread use cases for Azure Synapse Analytics in all organizations and industries are determined based on the following needs:

Modern Data Warehouse: Ability to analyze all data for analytical and reporting purposes independent of location and structure.

Advanced Analytics: Ability to perform predictive analytics using Azure Synapse Analytics’ native features and other technologies.

Data Discovery: Serverless SQL pool function enables data analysts, engineers, and scientists to discover and analyze data in the data estate.

Real-time Analytics: Azure Synapse Analytics allows for capturing, storing, and analyzing data in real-time or near-real-time.

Data Integration: Azure Synapse Pipelines enables the preparation, modeling, and presentation of data for downstream systems to use.

It can also interact with existing Azure services you may already have for your existing analytics solutions.

Integrated analytics
Bringing services together harmoniously can be a complex process because of the need to apply different analytical methods on the data in your Azure Synapse Analytics service. However, Azure Synapse Analytics removes this complexity by consolidating the entire analytics environment into a single service. This way, you can spend more time working with data to benefit your business, rather than spending time procuring and maintaining multiple systems to achieve the same results.

Creating Azure Synapse Analytics Service

Create
Click the “Create a resource” button in the Azure portal and select the “Azure Synapse Analytics” option.

Security
At this stage, it is mentioned that the SQL server to be created will create a SQL pool in the background. Therefore, credentials such as the name and password of the virtual server are determined.

Review and Create
All information needs to be reviewed as a new Synapse Analytics service will be created. After the verification processes are completed, the creation process is completed by clicking the “Create” button.

Deployment
Once the Azure Synapse Analytics service is created, it must be configured according to the requirements and aligned with the applications.

Resource Group
The Synapse Analytics service must be included in a resource group. This is a structure used to group and manage resources in Azure.

Synapse WorkSpace
Finally, a Synapse Workspace must be created in order to use the Synapse Analytics service. This is a workspace where Synapse Analytics projects are managed.

Azure Synapse Analytics is widely used for a variety of needs such as modern data warehousing, advanced analytics, data discovery, real-time analytics, and data integration. It can also interact with existing Azure services you may already have for your analytics solutions. Azure Synapse Analytics simplifies the process of bringing services together in a compatible way to apply different analytical methods to data. As a result, it allows users to consolidate their big data analytics processes on a single platform and access all of their data easily.