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Azure Data Lake

What is Azure Data Factory? (Its Functions and Components)

Understanding Azure Data Factory

In the field of big data, raw, disorganized data is frequently stored in relational, non-relational, and other storage systems. Raw data, on the other hand, lacks the context and meaning needed to deliver significant insights to analysts, data scientists, and business decision-makers. Big data necessitates a service that can orchestrate and operationalize procedures for transforming massive amounts of raw data into useful business insights. Azure Data Factory is a managed cloud solution designed for applications that require complicated hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration.

How does it function?

Azure Data Factory can connect to all of your data and processing sources, including SaaS, file sharing, and other internet services. The Data Factory service allows you to create data pipelines that transport data and schedule them to run at predetermined times. This means we have the option of using a scheduled or one-time pipeline.

A data pipeline's Copy Activity can be used to move data from on-premises and cloud sources to a centralized data storage in the cloud or on-premises for further analysis and processing.

After being saved in a centralized data storage location, data is converted utilizing services like HDInsight Hadoop, Azure Data Lake Analytics, and Machine Learning.

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Azure data factory

What is the purpose of Azure Data Factory?

SSIS is the most widely used on-premises tool for data integration, but there are some challenges to overcome when dealing with data in the cloud. The following methods can be used by Azure Data Factory to address these issues when moving data to or from the cloud:

  • Job scheduling and orchestration: On the cloud, there aren't many services that trigger data integration. Although there are some data movement services available, such as Azure Scheduler, Azure Automation, SQL VM, and so on, Azure Data Factory's job scheduling capabilities are superior to them.

  • Security: Azure Data Factory automatically encrypts every piece of data in transit between the cloud and on-premises.

  • Continuous integration and delivery: Using the Azure Data Factory and GitHub integration, you can easily develop, build, and deploy to Azure.

  • Scalability: Azure Data Factory was created with the ability to handle large amounts of data.

  • Components of the Azure Data Factory

    Understanding Azure Data Factory's functionality necessitates familiarity with its features. They are as follows:

  • Datasets: Datasets contain finer-grained data source configuration parameters. A dataset contains a table name or file name, as well as a structure.

  • Activities include data transfer, transformations, and control flow operations, to name a few. Activity configurations contain options such as database query, saved procedure name, arguments, script location, and others.

  • Linked Services: Linked services store configuration parameters for specific data sources. Information such as the server/database name, file folder, credentials, and so on could be included.

  • Pipelines: Pipelines are logical groups of actions. A data factory's pipelines can have one or more actions.

  • Triggers: Triggers are pipeline scheduling configurations that include start/end dates, execution frequency, and other parameters.

  • READ MORE: AWS Vs Azure For Data Analytics: Comparing The Platform Offerings

    Azure Data Factory

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