Learn why the Azure Data Factory is a key to migrate data across different data stores by creating pipelines and activities.
Introduction
Data sources ingest data in different sizes and shapes across on-premises and in the cloud,
including product data, historical customer behaviour data, and user data. Enterprise could store these data in data storage services like Azure Blob store, an on-premises SQL Server, Azure SQL Database, and many more.
This blog will highlight how users can define pipelines to migrate the unstructured data from different data stores to structured data using the Azure ETL tool, Azure Data Factory.
What is ETL Tool?
Before diving deep into Azure Data Factory, there is a need to know what the ETL tool is all about. ETL stands for Extract, Transform and Load. The ETL Tool will extract the data from different sources, transform them into meaningful data and load them into the destination, say Data warehouses, databases, etc.
To understand the ETL tool in real-time, let us consider management with various departments like HR, CRM, Accounting, Operations, Delivery Managements, and more. Every department will have its datastore of different types. For instance, the CRM department can produce customer information; the Accounting team may keep various books, and their Applications could store transaction information in Databases. The organization needs to transform these data into meaningful and analyzable insights for better growth. Here comes the ETL tool like Azure Data Factory. Using Azure Data Factory, the user will define the data sets, create pipelines to transform the data and map them with various destinations.
What is Azure Data Factory?
As cloud adoption keeps increasing, there is a need for a reliable ETL tool in the cloud with many integrations. Unlike any other ETL tools, Azure Data Factory is a highly scalable, increased agility, and cost-effective solution that provides code-free ETL as a service.
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Originally published at Serverless360 site
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