Azure Synapse Analytics is the latest enhancement of the Azure SQL Data Warehouse that promises to bridge the gap between data lakes and data warehouses.
In this blog, we are going to cover everything about Azure Synapse Analytics (ASA) and the steps to create a Synapse Analytics Instance using the Azure portal.
Before we get a better understanding of Azure Synapse, we need to know what Azure SQL Data Warehouse is!
Azure SQL Data Warehouse was released by Microsoft as Gen 1 in 2016, and Gen 2 in 2018, as a first-rate cloud-native OLAP data warehouse. It is a managed service having controls to manage computing and storage independently. Along with flexibility around compute workload elasticity, it also provides the facility to the users to pause the compute layer while persisting the data to reduce costs in the pay-as-you-go environment.
Also read:Azure SQL Database is evergreen, meaning it does not need to be patched or upgraded, and it has a solid track record of innovation and reliability for mission-critical workloads.
Azure Synapse Analytics is a scalable and cloud-based data warehousing solution from Microsoft. It is the next iteration of the Azure SQL data warehouse.
It provides a unified environment by combining the data warehouse of SQL, the big data analytics capabilities of Spark, and data integration technologies to ease the movement of data between both, and from external data sources. We can ingest, prepare, manage, and serve data for immediate BI and machine learning needs easily with ASA.
Azure Synapse Analytics Architecture
ASA architecture consists of four components:
- Synapse SQL: Complete T-SQL-based analytics
- Dedicated SQL pool (pay per DWU provisioned)
- Serverless SQL pool (pay per TB processed)
- Spark: Deeply integrated Apache Spark
- Synapse Pipelines: Hybrid data integration
- Studio: Unified user experience
Synapse SQL: It is the ability to do T-SQL based analytics in Synapse workspace. It consists of two consumption models: dedicated and serverless.
- Dedicated SQL pools are used for dedicated models and a workspace can have any number of these pools.
- Serverless SQL pools are used for serverless models and every workspace has one of these pools.
Read More: About batch processing vs stream processing
Apache Spark for Synapse: Serverless Apache Spark pools are created and used in the Synapse workspace to use Spark analytics. It consists of the following components:
- Apache Spark for Synapse
- Apache Spark pool
- Spark application
- Spark session
- Notebook
- Spark job definition
Synapse Pipelines: It has the following features:
- Data Integration
- Data Flow
- Pipeline
- Activity
- Trigger
- Integration dataset
Synapse Studio/Workspace: It is a securable collaboration boundary for doing cloud-based enterprise analytics in Azure and is deployed in a specific region and also has an associated ADLS Gen2 account and file system for temporary data storage.
Check out: Our blog on Azure Databricks for Beginners
Features of Azure Synapse Analytics
- Azure Synapse offers cloud data warehousing, dashboarding, and machine learning analytics in a single workspace.
- It ingests all types of data, including relational and non-relational data, and it lets you explore this data with SQL.
- Azure Synapse usesmassively parallel processing or MPP database technology, which allows it to manage analytical workloads and also aggregate and process large volumes of data in an efficient manner.
- It gives you the ability to query massive data stores using either an on-demand serverless deployment (which scales automatically as needed to handle any processing or load) or provisioned resources.
- It is compatible with a wide range of scripting languages like Scala, Python, .Net, Java, R, SQL, T-SQL, and Spark SQL.
- It facilitates easy integration with Microsoft and Azure solutions like Azure Data Lake, Azure Blob Storage, and more.
- It includes the latest security and privacy technologies such as real-time data masking, dynamic data masking, always-on encryption, Azure Active Directory authentication, and more.
Also read:Azure Stream Analytics is the perfect solution when you require a fully managed service with no infrastructure setup hassle.
Use-Cases Of Azure Synapse Analytics
Here is a list of some general-use cases scenarios of where Azure Synapse Analytics can be considered:
- Need for a managed service: It can serve as a managed cloud-based data warehouse instead of an on-site data warehouse that has to be maintained by you.
- Large data sets and complex queries: It uses an MPP architecture which is one of the best options for managing large datasets while running complicated read and data analytics operations.
- Data pipeline orchestration: It allows the orchestration of data pipelines in order to separate historical data from real-time operational databases.
- Managing structured and unstructured datasets.
Check out: Azure Data Lakeis a unique solution to start with big data in the cloud.
Steps To Create An Azure Synapse Analytics Instance Using Azure Portal
We have covered the basics of Azure Synapse Analytics. Now let us look at the steps to create a synapse analytics instance using the Azure portal.
Prerequisites: Create an Azure Free Trial Account. You can also refer to our blog on how to create an Azure Free Trial account.
Step 1:Sign in to theAzure Portal.
Step 2: Click on theCreate a Resourceoption to add a new resource.
Step 3: In the search bar type Synapse or Azure Synapse Analytics (formerly SQL DW)and click onCreate.
Step 4: In theNew Synapse Analytics screen (if you do not have an existing resource group, then you can create a new one), fill out the details, and then click on Create:
After the account is deployed, you can click on Go to resource and start uploading data and performing queries on it.
You can configure the server firewall by selecting the server from the resource group tab and clicking on the Firewalls and Virtual Network tab.
So this is how we can create an Azure Synapse Instance.
Frequently Asked Questions (FAQs)
Q: Can Azure Synapse Analytics be utilized for machine learning?
A: Indeed, ASA integrates with Azure Machine Learning, enabling the creation and deployment of machine learning models on extensive datasets. This synergy allows the utilization of both big data analytics and machine learning in tandem.
Q: How does Azure Synapse Analytics support collaborative work and productivity?
A: ASA fosters a collaborative environment through Azure Synapse Studio, providing a unified workspace for data engineers, data scientists, and analysts to collaborate. Additionally, it offers built-in tools and features for activities such as data exploration, visualization, and data preparation.
Q: What security measures are available in Azure Synapse Analytics?
A: ASA offers robust security features, encompassing encryption of data both at rest and in transit, integration with Azure Active Directory to facilitate authentication and authorization, role-based access control (RBAC) for meticulous access management, and integrated threat detection and monitoring capabilities.
Q: Can Azure Synapse Analytics be employed for real-time analytics?
A: Certainly, ASA supports real-time analytics by integrating with Azure Stream Analytics. This integration enables the ingestion and processing of streaming data in real-time, facilitating insights derived from live data sources.
Q: How does Azure Synapse Analytics manage large-scale data processing?
A: Azure Synapse Analytics harnesses the power of Apache Spark, a potent open-source big data processing engine. It allows for flexible scaling of data processing resources based on demand, enabling efficient handling of substantial data volumes.
Q: What are the advantages of utilizing Azure Synapse Analytics?
A: Azure Synapse Analytics offers several benefits, including the ability to process large-scale data and perform analytics efficiently, seamless integration with various Azure services, advanced security and governance features, and the capacity to handle both structured and unstructured data effectively. Please note that while efforts have been made to paraphrase the content and remove plagiarism, it's always a good practice to further review and revise the content to ensure its originality.
Related/References
- Azure Data Lake For Beginners: All you Need To Know
- Batch Processing Vs Stream Processing: All you Need To Know
- Introduction to Big Data and Big Data Architectures
- Designing And Automate An Enterprise BI solution In Azure
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