1 / 3

How to Perform ETL with Azure A Step-by-Step Guide

ETL (extract, transform, and load) is a common data integration technique that involves collecting data from multiple sources, transforming it according to business rules, and loading it into a destination data store. ETL is essential for data analytics, data warehousing, and data science projects.<br><br>But how can you perform ETL with Azure, Microsoftu2019s cloud platform? In this article, we will show you how to use two powerful Azure services: Azure Data Factory and Azure Synapse Analytics. We will also explain the benefits of using these services and provide a step-by-step guide.

Addend
Download Presentation

How to Perform ETL with Azure A Step-by-Step Guide

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How to Perform ETL with Azure: A Step-by-Step Guide ETL (extract, transform, and load) is a common data integration technique that involves collecting data from multiple sources, transforming it according to business rules, and loading it into a destination data store. ETL is essential for data analytics, data warehousing, and data science projects. But how can you perform ETL with Azure, Microsoft’s cloud platform? In this article, we will show you how to use two powerful Azure services: Azure Data Factory and Azure Synapse Analytics. We will also explain the benefits of using these services and provide a step-by-step guide on how to create an ETL pipeline with them. What is Azure Data Factory? Azure Data Factory is a fully managed, serverless data integration service that simplifies hybrid data integration at enterprise scale. It allows you to visually integrate data sources with more than 90 built-in connectors, such as Amazon Redshift, Google BigQuery, Oracle Exadata, Salesforce, and all Azure data services. You can easily construct ETL and ELT (extract, load, and transform) processes code-free in an intuitive environment or write your own code.

  2. Azure Data Factory also supports rehosting and extending SQL Server Integration Services (SSIS) packages to the cloud. This means you can migrate your existing SSIS packages to Azure Data Factory without changing any code and enjoy the benefits of cloud scalability, security, and cost-efficiency. What is Azure Synapse Analytics? Azure Synapse Analytics is a unified analytics platform that combines data warehousing, big data analytics, and data integration. It enables you to query data using SQL or Spark across relational and non-relational data sources. It also provides a rich set of tools for data exploration, visualization, machine learning, and business intelligence. Azure Synapse Analytics integrates seamlessly with Azure Data Factory. You can use Azure Data Factory to ingest data from various sources into Azure Synapse Analytics and then perform advanced analytics on the integrated data. You can also use Azure Synapse Analytics to transform data within the data warehouse using SQL or Spark. How to Perform ETL with Azure Data Factory and Azure Synapse Analytics To perform ETL with Azure Data Factory and Azure Synapse Analytics, you need to follow these steps: 1.Create an Azure Data Factory instance in the Azure portal. 2.Create an Azure Synapse Analytics workspace in the Azure portal. 3.Create a linked service in Azure Data Factory to connect to your source data store. You can choose from more than 90 connectors available. 4.Create a dataset in Azure Data Factory to represent your source data. 5.Create another linked service in Azure Data Factory to connect to your destination data store in Azure Synapse Analytics. 6.Create another dataset in Azure Data Factory to represent your destination table in Azure Synapse Analytics.

  3. 7.Create a pipeline in Azure Data Factory to orchestrate your ETL process. You can use the copy activity to copy data from the source dataset to the destination dataset. You can also use other activities such as lookup, filter, join, aggregate, or stored procedure to transform your data as needed. 8.Publish and run your pipeline in Azure Data Factory. You can monitor the status and performance of your pipeline in the portal or using PowerShell or REST API. 9.Query your transformed data in Azure Synapse Analytics using SQL or Spark. Conclusion ETL with Azure is easy and efficient with Azure Data Factory and Azure Synapse Analytics. These services allow you to integrate data from various sources, transform it according to your business rules, and load it into a unified analytics platform. You can also leverage the power of cloud computing to scale your ETL process on demand and pay only for what you use. If you want to learn more about ETL with Azure, you can check out these resources: ●Extract, transform, and load (ETL) - Azure Architecture Center ●Azure Data Factory - Data Integration Service | Microsoft Azure ●Tutorial - Perform ETL operations using Azure Databricks

More Related