As more data is available, it becomes more challenging to handle. Investing in innovative services and tools lets you get more value from data. Modern businesses must embrace effective tools, technologies, and innovative methods to succeed.
This is where the Azure Data Factory comes into play. ADF allows you to orchestrate data processes, analyze the data, and gain insights.
What is Azure Data Factory?
Azure Data Factory is a Microsoft cloud service offering that provides data integration from various sources. It is part of the Azure platform. ADF is a great option for creating hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration pipelines. In simple terms, an ETL tool collects data from various sources, transforms it into useful information, and transfers it to destinations such as data lakes, data warehouses, etc.
How does ADF work?
- Combine and connect: Gather and combine data from various sources. The data can be structured, semi-structured, or unstructured.
- Centralize and store: Transfer and store data from on-premises storage to a centralized location, such as a cloud-based store.
- Transform and analyze: After storing data in centralized cloud storage, use computing services such as HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning to process or transform the data collected.
- Publish: After refining the data and converting it into consumable form, publish it to cloud stores like Azure Data Lake, Azure Datawarehouse, and Azure Cosmos DB, whichever analytics engine your business users can point from to their BI apps.
- Visualize and Monitor: For further analysis, visualize the output data using third-party apps like Tableau, Microsoft Power BI, Sisense, etc.
Why do companies need Azure Data Factory?
Every cloud project demands data migration activities across various data sources (such as on-premises or cloud), networks, and services. Azure Data Factory is a vital enabler for organizations looking to enter the universe of cloud technology.
Below are some of the reasons why companies should adopt ADF to start their data journey:
- Secure data integration
- Easy migration of ETL/Big Data workloads to the cloud
- Low learning curve
- Code-free and low-code data transformations
- Greater scalability & performance
- Reduce overhead expenses
- Easily run or migrate SSIS packages to Azure
Data Factory is necessary for enterprises taking their initial steps towards the Cloud and thus attempting to integrate on-premise data with the Cloud. Azure Data Factory includes an Integration Runtime engine and a Gateway service that can be placed on-premises and ensures reliable and safe data transfer from and to the cloud.