Azure Synapse Analytics

What is Azure Synapse Analytics?

Azure Synapse is an unlimited analytics tool that helps in bringing together an organization's both big data analytics and data warehouse. The technology is a progression of widely used Azure SQL Data Warehouse. It processes and stores a large amount of data in the Azure cloud system. It offers various cloud solutions. Users can query information as per the requirements thereby facilitating modern data warehouse management. In a nutshell, it brings together the two worlds of data warehouse and big analytics by unifying the experience of ingesting, preparing, managing, and serving the data for rapid machine learning and business intelligence needs.

How Azure Synapse Analytics Work?

Azure Synapse Analytics is a one-stop solution for analytics that helps the user with the following capabilities

• Synapse SQL is the pool of SQL servers that serve as the backbone for the complete analytics of data storage. This helps with the infrastructure for the implementation of the data warehouse. It also allows users to unlock insights from the data stores without the formal process of having a data warehouse using data virtualization as it empowers the serverless model for Adhoc and unplanned workloads.
• Data integration and ETL capabilities from different sources use Synapse pipelines to help organizations to analyze data efficiently. Pipeline orchestration and the capability of reusable workflows are features that can be easily adapted.
• With the help of Apache Spark, organizations can empower the development solutions of machine learning and workloads of big data for Azure Synapse. This is possible with the help of computer resources that deliver scalable high performance.
• Delivering real-time operational analytics with the help of data sources from operations using the Synapse link.

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Set up and Configure the Azure Synapse Analytics workspace

Follow the steps mentioned below

Load and Analyze data using Spark

• Navigate to the data hub tab and browse both the covid-tracking and gallery datasets.
• Go to the new notebook of spark by using the dataset as seen in the below image

• The notebook will contain the default code which can be further analyzed with the help of the Spark backbone framework.

Analyze the Data in Serverless SQL Pool

• Using the data hub again load the dataset sample and then create a SQL Script which is new

• You can use Native T-SQL queries for analysis of the data sets with the help of freehand scripts.

Setup and Integrate a Pipeline

• From the Synapse Analytics studio hub navigate to the Integrate hub and select the option Pipeline

• After initiating the pipeline you will be able to add the workflow activities as per your requirements.

• Trigger conditions can be added for responding to any event or you can also manually execute the workflow of the pipeline. To monitor the progress of the pipeline execution navigate to the monitor hub and select the pipelines to run.

Integrated Linked services

• You can use Azure Synapse analytics studio to both integrate and also enable services that are linked for example Power BI
• You can also use the Manage Hub to find the linked existing services.
• Then, to leverage the data visualization and also the reporting a link should be created to the PowerBI ( Business Intelligence)

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The Architecture of Azure Synapse

Poor Architecture of Azure Synapse

“Suggested Synapse SQL” terminology indicates the capability of the Synapse Enterprise for executing analytics with the help of T-SQL. Below mentioned are the two pools

1.Dedicated SQL Pool : A workspace might consist of a limitless number of SQL pools that are dedicated and used as dedicated models.

2.Serverless SQL Pool : Each workspace has a minimum of a single serverless SQL Pool used for serverless models.

Apache Spark for Azure Synapse Analytics

It uses Apache Spark which is serverless, created is used in the synapse workspace for spark analytics. It has the following parts

1. Notebook
2. Apache spark application spark pool
3. Job definition of spark
4. Spark for synapse with Apache

Synapse Pipeline

The Synapse pipeline has the following characteristics 

1. Trigger
2. Pipeline
3. Combined data set
4. Activity
5. Data stream
6. Integration of Data

Synapse Studio

The architecture of Synapse is secured. The collaboration boundaries are trustworthy for performing cloud-based analytics for Azure. The deployment of it is also very easy in specific regions. Moreover, for non-permanent storing of data, it has collaboration with the ADLS Gen2 account and the file system.

Azure Synapse Service for Industries

Financial Services: It ensures that the data is secured with industry-leading features. It helps in deploying a modern strategy to facilitate personalized customer experience, handling data warehousing, and big data, and for strict adherence to compliance and all the government procedures to protect consumer data.

Manufacturing services: Azure Synapse analytics is utilized for gaining scalable real-time insights. With the help of a combination of operational and also all analytical technologies, Industry 4.0 helps users with real-time access to old and new data.

Retail Services: With the help of end-to-end analytics. Data can be combined from various channels and obtain insights from time to time. This will help you to understand the consumers and also build a safe, reliable, and genuine supply chain.

Healthcare Services: Legislative restrictions, Secure patient data, delivering customized treatment, lack of care workers, and patient expectations are some of the pressures of the Azure Synapse analytics for healthcare.

Real-Life Applications of Azure Synapse

• Data Warehouse- It helps in interacting seamlessly with numerous platforms and data providers.
• Exploratory Analysis- Exploration of data and figuring the data using SQL queries as opposed to a synapse database.
• Data visualization- For making an informed decision at a faster pace collaborate with Excel or Tableau.
• Real-time Analytics- Consolidation of different operational sources for deploying exploratory solutions that are real-time with the aid of Business Intelligence tools such as Tableau and PowerBI
• Step-up Analysis- Utilize Azure Databricks and get insights from the data. This will help to improve the business outcome and results that were drawn using tableau and PowerBI.

Characteristics of Azure Synapse

• Azure synapse can help organizations with a variety of services concerning analytics.
• Processing of real-time data from millions of IoT devices.
• Azure synapse helps in securing the data by using cutting-edge privacy features and security.
• Application of machine learning algorithms to all smart applications.
• Removal of data barriers and performing analytics on the data from both the business applications and operations.
• It helps in broadening the insights of the data.

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 Conclusion

Azure Synapse is the best tool for data engineers to have the entire data pipeline in one place. It can handle business insights in significantly less time, excluding the need for additional resources. Hope you have understood the architecture, characteristics, and how the technology can be leveraged for several analytical purposes. If you have any queries, let us know via the comment section.

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