Introduction to Azure Data Factory
Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation. ADF enables you to process on-premises data like SQL Server, together with cloud data sources such as Azure SQL Database, Blob storage, and Cosmos DB. You can also use ADF to transform data for analysis in Azure HDInsight Hadoop, Spark, and Azure Data Lake Store.
ADF provides a visual authoring tool that enables you to compose data storage, transformation, and movement services into manageable data pipelines. You can monitor the status of your data pipelines from the Azure portal, and you can also set up alerts to get notifications when your jobs fail or succeed.
what is azure data factory
Azure Data Factory can be used for a variety of data integration scenarios. Common uses cases include:
– Copying data from on-premises or cloud data sources for further analysis in Azure HDInsight Hadoop, Spark, and Azure Data Lake Store.
– Cleaning and transforming data from different sources before loading it into a data warehouse for reporting and analytics.
– Extracting data from multiple data sources, transforming the data based on business rules, and then loading the data into a destination data store.
– Migrating data from one data store to another. For example, you can migrate data from an on-premises SQL Server database to Azure SQL Database.
– Loading data into multiple destinations based on business rules. For example, you might want to load data into Azure SQL Database for reporting and analytics, and also load the same data into blob storage for archival purposes.
What is Azure Data Factory and what are its key features?
Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation. ADF enables you to process on-premises data like SQL Server, together with cloud data sources such as Azure SQL Database, Blob storage, and Cosmos DB. You can also use ADF to transform data for analysis in Azure HDInsight Hadoop, Spark, and Azure Data Lake Store.
ADF provides a visual authoring tool that enables you to compose data storage, transformation, and movement services into manageable data pipelines. You can monitor the status of your data pipelines from the Azure portal, and you can also set up alerts to get notifications when your jobs fail or succeed.
How to create a data factory
Creating a data factory is a four-step process:
1. Provision Azure resources
2. Configure the data factory
3. Create linked services
4. Create datasets
5. Create pipelines
6. Monitor and manage your data factory
How to use pipelines in data factories
Pipelines are the core objects in Azure Data Factory. A pipeline is a logical grouping of activities that together perform a task. For example, you might have a pipeline that copies data from one data store to another data store. You can think of a pipeline as a workflow for your data: it defines what actions need to be performed, and in what order.
Pipelines can be triggered on a schedule, or they can be triggered manually. You can also trigger pipelines based on an event, such as when a new file is added to Azure Blob storage. Pipelines can be used in data factories to orchestrate the movement and transformation of data. Data can be copied from one data store to another, and data can be transformed when it is moved. Pipelines can also be used to run Azure HDInsight Hadoop, Spark, and MapReduce jobs, and Azure Machine Learning models.