Establish a consistent naming scheme for pipelines, datasets, linked services, and triggers. For example, pl_project_frequency_description for pipelines. Standardization simplifies management at scale.
Azure Data Factory is a cloud-managed , ELT (Extract, Load, Transform) , and data integration service. It allows you to orchestrate data movement and transform data at scale using compute services such as Azure HDInsight, Azure Databricks, and SQL databases.
One of the most powerful features of Azure Data Factory is . This allows data engineers to develop data transformation logic visually without writing code.
Once deployed, click "Go to resource" and select "Launch Studio" to open the Azure Data Factory Studio interface. 4. Key Features of Azure Data Factory
Understanding the architecture of ADF requires familiarity with its core components: A. Data Factory (The Container) javatpoint azure data factory
Understanding ADF's key features helps clarify why it has become the industry standard for cloud-based data integration.
A is a logical grouping of activities that together perform a unit of work. For example, a pipeline might copy data from an Azure Blob storage location and then transform it using a compute service like Azure Databricks. Pipelines allow you to manage a series of related tasks as a single, coordinated job. The activities within a pipeline can be set to run sequentially or in parallel, giving you fine-grained control over the execution flow.
Connect to all your required data sources using Linked Services and move data into a centralized cloud storage (like Azure Data Lake Gen2) via Copy Activities.
Activities represent a processing step in a pipeline. There are three main types of activities: Azure Data Factory is a cloud-managed , ELT
The Integration Runtime is the compute infrastructure that ADF uses to execute activities. There are three types:
Click to save your operational components permanently to the live production cloud service. Triggers in Azure Data Factory
These are the processing steps within a pipeline. Examples include the Copy Activity , which moves data, or Data Flow Activity , which transforms it.
For grouping data and calculating averages, sums, or counts. This allows data engineers to develop data transformation
Go to the tab: Select SourceCSVDataset from the dropdown.
Automating pipelines requires orchestration handles known as . ADF supports four primary execution triggers:
Web, ForEach, Until, and If Condition activities manage pipeline logic. 3. Datasets
