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.
Data ingestion with Azure data factory
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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:
Components of the Azure Data Factory
Understanding Azure Data Factory's functionality necessitates familiarity with its features. They are as follows: