The Microsoft Azure data integration services are known to assist businesses in managing data in the cloud. The service by Microsoft allows the companies to convert the raw data into meaningful information. But, before diving into more details and uses of ADF, let us see what it exactly means.
What is Azure Data Factory?
As per definition, ADF (Azure Data Factory) can be understood as cloud-based data integration and migration service. It stores a significant amount of data and automates its optimization in the cloud. In simple words, the Azure Data Factory service allows the data movement between one service to another in a split second. Let us explore some of the key features of Azure Data Factory:
- Parameterized pipelines
- Control flow
- Flexible scheduling
- Data movement security
- Delta processing
- Monitoring and alerting
- Scalability
What are the uses of Azure Data Factory?
Every cloud-based enterprise project needs various migration activities across multiple data sources, networks, and services. The Azure Data Factory bridges the gap between cloud computing and enterprises. It allows the channel for enterprises to step into the world of computation. Businesses can manage the big data workflows in the cloud. There can be several cloud-based storages like Azure Blob Storage, Azure Data Lake Store, or other on-premise storage. Transforming the data is more accessible, but automating it to process or store it on the cloud platform becomes easier with Azure Data Factory.
Simply put, the Azure Data Factory (ADF) is handy for serverless data migration & transformation practices like:
- Staging data for transformation
- Building logics via visual data transformation
- Building code-free ETL/ELT cloud processes
- Processing and moving SSIS packages in the cloud
- Gaining seamless integration & delivery (CI/CD)
- Creating and executing pipeline from Azure logic
- Achieve great versatility with operational control
- Advanced functionalities as compared with version 1
The combination of data migration and analytics powers up the capability of Azure Data Factory. It empowers you to perform the below operations seamlessly:
- Compile the structured, semi-structured, or unstructured data from multiple resources and consolidate the same.
- Move or store the variable data to a centralized storage system (e.g., Cloud-Based Stores).
- It helps in processing and transforming the data with computing services. You can choose from Azure ML (Machine Learning), Azure Data Lake Analytics, Azure HDInsight, Hadoop, etc.
- Publish the organized data to cloud stores and visualize the final output for more analysis.
Why is Azure Data Factory efficient for use?
The world is moving into the cloud, and big data space and organizations are tapping into these essential elements. The Azure Data Factory can efficiently address these two concerns to enhance your focus on scheduling, monitoring, and managing your pipelines. Here are some reasons why the businesses are switching to adopt Azure Data Factory:
- Drive more benefits
- Improve business process results
- Reduce overhead costs
- Better decision-making & control
- Increase business agility
Azure Data Factory comes with numerous benefits, and you need to pay only for what you use. With no server hassle, it allows you to integrate data with no additional infrastructure & within budget prices.
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