Getting Started with Azure Synapse Analytics
This article was published as a part of the Data Science Blogathon.
Azure Synapse Analytics is a cloud-based service that combines the capabilities of enterprise data warehousing, big data, data integration, data visualization and dashboarding. Azure Synapse empowers numerous organizations in decision-making with the help of prescriptive and predictive analytics capabilities using its integration with Azure Machine Learning Services, Apache Spark, Power BI and Stream Analytics. In this article, you will see an overview of Azure Synapse Analytics, how it works, features of Synapse Analytics, benefits and use cases of Synapse Analytics.
Azure Synapse Analytics – Overview
It is an integrated analytics service that gives you freedom to integrate, query and analyze data on your terms. Use Azure Synapse Studio to interact with the components that exists in the Azure Synapse Analytics service. Azure Synapse Studio is a low-code platform to perform a wide range of activities against your data.
Azure Synapse can be used in various industrial scenarios such as fraud detection, building a resilient supply chain, personalize recommendations, etc. It supports a number of languages such as .NET, Python, Java, Scala, SQL, etc. that are typically used by analytic workloads. Azure Synapse also provides security and monitoring related services to monitor data pipelines, analyze point of failures and prevent unauthorized access.
I believe by now you have got an overview of what Azure Synapse analytics is. Now let’s dive quickly into the working of Azure Synapse Analytics.
How Azure Synapse Analytics Works?
It analyses structured and semi-structured data across data warehouses, databases, data lakes and provide useful insights by performing data integration, querying and data visualization.
It does this by providing the following capabilities:
- Analytics capabilities are offered through Azure Synapse SQL either through dedicated or serverless SQL pool. Azure Synapse SQL enables the data engineers to easily implement data warehousing and data virtualization scenarios using standard T-SQL. Use dedicated SQL pools for predictable performance and cost as it will help to reserve the processing power. For ad-hoc workloads, always prefer to use serverless SQL endpoint.
- Development of big data and machine learning solutions are provided using Apache Spark for Azure Synapse. Apache spark provides support for various languages such as .NET, Python, etc. We can easily use SparkML algorithms and AzureML integrations for machine learning workloads.
- ETL and data integration capabilities are provided using Synapse pipelines which help the organizations to create data driven workflow to orchestrate and automate the data movement and data transformation. The reusable workflows are easy to adapt.
- Deliver real-time hybrid transactional and analytical processing using Synapse Link.
- Provide easy to use web-based UI Azure Synapse Studio for developing TSQL scripts, python notebook, building data pipelines, security, monitoring and managing the workloads in the service.
- Synapse Analytics can be easily integrated with other Azure data services such as Azure Databricks, Azure Data Lake Storage, Azure Machine Learning, Power BI, etc.
I hope that you got a good understanding of how Azure Synapse Analytics works. Let’s now look at Azure Synapse Analytics features.
Features of Synapse Analytics
- Automated restore points and backups: It ensures high data availability and fault tolerance in Synapse.
- Scalability: Synapse Analytics scale-up and scale-down resources as per the workload demands. It provides scalable storage and compute resources.
- Massively parallel processing & result-set caching: Synapse Analytics speed up the query process using parallel processing technique. It uses multiple computer processors in a coordinated manner to give fast results. Result-set caching delivers precomputed or cached results for a query.
- Unified analytics platform: Synapse Analytics provides a single environment for performing data integration, data processing, data exploration, big data and machine learning solutions and data visualizations.
- Enterprise data warehousing: Synapse Analytics provides enterprise data warehousing capabilities using dedicated SQL pool. Data Warehousing Units (DWU) determines the size of a dedicated SQL pool. Different types of data such as structured data, unstructured data, etc. is ingested into data warehouse from a variety of sources.
- Integrated AI and BI: Organizations can easily integrate the AI and BI capabilities with Synapse. Using synapse analytics, it is easy to create complex analytics with the integration of Azure Cognitive Services, Azure Storage, Azure Machine Learning and Power BI.
- Choice of Languages: A variety of language choice is provided including SQL, Scala, Python, .NET etc. whether you use serverless or dedicated resources.
- Security: It provides various security options such as data masking, data encryption and granular access controls.
- Easily Integrable with other Azure services: Synapse Analytics can be easily integrated with other Azure data services such as Azure Databricks, Azure Data Lake Storage, Azure Machine Learning, Power BI, etc.
Benefits of using Synapse Analytics
- Infrastructure cost reduction: Synapse Analytics provides pay-as-you-go pricing model and no infrastructure is required from customer end. Thus, it saves of infrastructure cost as Azure take care of the infrastructure.
- Accelerated data analytics and processing: Synapse Analytics has almost no downtimes because of workload variations due to instant scalability and flexibility. It becomes easy to develop complex data solutions using Synapse as it provides single environment for performing data integration, data processing, data exploration, big data and machine learning solutions and data visualizations. Thus, it helps to accelerate data analytics and processing.
- Security: It provides various security options such as data masking, data encryption and granular access controls. Granular access controls are provided using row-level security and column-level security.
- High availability & fault tolerance: The automated restore points and backups in Azure Synapse will help to ensure high availability.
The below are some use cases for Azure Synapse Analytics:
- Customer analytics: Azure Synapse Analytics can be used for customer segmentation and modeling. Machine learning capabilities of Azure Synapse will help to predict the customer buying pattern, provide personalized product recommendation and grouping product of similar interests and category.
- Fraud detection: For fraud detection while transferring cash and during claims, Synapse could be used to detect suspicious activities or transactions. It can also be used to avoid invalid user check-in and claim fraud.
- Manufacturing analytics: Supply chain visibility is enhanced in manufacturing industry which leads to increase resilience, better risk management and provide a competitive advantage using advanced analytics and machine learning. Synapse provides a better way of inventory management and product demand forecasting.
- Patient monitoring: Azure Synapse Analytics can be used for storing and monitoring patient data. Machine learning capabilities of Azure Synapse will help in identifying patterns in patient conditions and provide care plan recommendations.
In this article, we have seen an overview about Azure Synapse Analytics. Our focus area in this article was to learn the working of Azure Synapse Analytics, how it makes the development of complex data solution easy, provides single environment for performing data integration, data processing, data exploration, big data and machine learning solutions and data visualizations. Apart from this, we have seen the benefits and real-world use cases of Synapse Analytics in manufacturing, financial services and health. I hope this has given you good idea about on how to get started with the Azure Synapse Analytics. Please let me know your queries in the comments section below.
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