Undoubtedly, technology has revolutionized virtually every aspect of human existence, including how we conduct business. One of the many tools that have enabled this change is cloud computing. As it continues to gather steam in the market, Azure Data Factory’s popularity has been surging lately. Before we understand why that is, let us first see what it is. Azure Data Factory enables the development of data-driven workflows in the cloud to orchestrate and automate both movement and transformation of data.
Azure Data Factory (ADF) is a cloud-based data integration service. It allows the creation of data-driven workflows that help orchestrate data movement between supported data stores. The development teams can monitor and manage workflows using programmatic and UI mechanisms. ADF is used for keeping data migrations, getting data from a client’s server or online data to an Azure Data Lake, carrying out various data integration processes & integrating data from different ERP systems & loading it into Azure Synapse for reporting
With the introductions out of the way, allow us to now walk you through some of its key benefits:
- Primed for enterprise data: Azure Data Factory — a cloud-based solution capable of operating with on-premise and cloud-based data stores, thus ensuring scalability and cost-effectiveness.
- Support for multiple compute environments: It offers support for several computing environments and activities and that, in turn, ensures execution and dispatch within data pipelines are simplified.
- Smooth data pipeline operations: Yet another compelling benefit of this handy offering from the tech behemoth is that it eases data pipeline operations via provisions such as automated deployment, reusable templates, etc.
Time to talk about the critical components of Azure Data Factory:
- Datasets: Datasets represent data: the data that needs to be ingested or stored, i.e., input or output, in the activities.
- Pipeline: A rational collection of activities that execute a unit of work performed by ADF is referred to as a pipeline of operations.
- Activities: A representation of an individual processing task in a pipeline; activities produce or consume datasets. They serve to define the actions that will be performed on the data.
- Linked Services: Linked Services serve to identify the connection information necessitated for the tool to connect to external resources, i.e., source or destination. To cut a long story short, it defines a compute service or a target data store.
- Integration runtime: Basically the bridge between Activity and Linked Services, integration runtime is the infrastructure from where the activities are dispatched or where these activities are run.
Finally, let us also take a quick look at some of the primary use cases of Azure Data Factory:
- It can be used to bolster data migrations
- Companies also make use of Azure Data Factory to move data from an online server or a client-server to an Azure Data Lake
- Azure Data Factory can also be put to work for the execution of different data integration processes
- Another exciting use case for Azure Data Factory is for integrating data from various ERP systems before moving it to Azure Synapse for reporting
Well, that about sums it up, folks; while this guide seeks to inform you about Azure Data Factory, it also demonstrates how this Microsoft offering makes a world of benefits accessible to organizations that choose to use it. So, if you are also looking to leverage this mighty tool from the stables of Microsoft, we recommend that you start looking for a trusted Microsoft Azure Cloud consulting services vendor immediately.