Azure Data Factory (ADF) is a cloud-native service for data integration. It creates ETL (Extract, Transfer, Load) and ELT (Extract, Load, Transform) pipelines, allowing data to move between on premise and cloud environments.
The ADF allows DevOps teams to integrate disparate data systems. Its primary function is to provide access to on-premise SQL servers and cloud data stores on Azure Tables and Blob, as well as Azure SQL Database. Designers provided this connection through a data management gateway.
Azure Data Factory is a scalable ETL (extract, transform, and load) tool. It takes data from other sources, transforms the data into meaningful information, and loads it to preferred destinations, i.e., data warehouses and data lakes. It also performs ELT (Extract, Load, and Transform) functions.
This cloud service consists of datasets, activities, pipelines, and triggers. Being code-free, it helps users perform complex ETL processes quickly. You can use it to define data sets, create pipelines, transform data, and map it to different destinations of your choice.
Components of the Azure Data Factory
It’s essential to familiarize yourself with its components, to understand Azure Data Factory’s functionality, it’s essential to familiarize yourself with its components. These are:
Datasets are representations of data. They represent the data structures within stores, which is the data you want to input or output in your activities.
Datasets contain configuration settings on a granular level. The datasets contain a file name, table name, data structures, and more. An activity can take zero or more datasets as input and one or more as output. For instance, an Azure Table dataset specifies the folder and contains in Azure Tables storage from which the pipeline should read data.
Activities are individual processing steps in a pipeline that define actions on data. Some common activities include copy, hive, etc. For instance, a copy activity can copy data from an on-premise SQL Server to Azure Blob storage. You can use a Hive activity to run Hive activities and process or transform your data from the Blob storage to output data.
Azure Data Factory supports two types of activities. These are data transformation and data movement activities.
A pipeline is a group of activities on Azure Data Factory built to perform a unit of work. A single pipeline can perform a range of activities, from querying databases to inputting data from blob storage.
You can run pipelines manually or automatically with triggers. You can use pipelines to schedule and monitor logically related activities.
Also known as connectors, a linked device connects to the data sources. A linked device is a connection string that defines the connection information needed to connect to external data sources.
The integration runtime is the compute infrastructure that provides a bridge between linked devices and activities. Activities run or get dispatched from the Integration Runtime infrastructure. There are three types of Integration Runtime:
- Azure Integration Runtime: provides a fully managed, serverless infrastructure in Azure. It’s responsible for data movement activities in the cloud.
- Self-Hosted Integration Runtime: manages activities between cloud data stores and data stores in private networks
- Azure-SSIS IR: used to execute SSIS packages.
A trigger is a processing unit that executes a pipeline. You can automate triggers in perioding intervals or upon the occurrence of an event.
How Does Azure Data Factory Work?
Azure Data Factory is a cloud ETL service that performs ETL functions. It connects and collects data from different sources, transforms and enriches the data, and publishes it to your desired destination.
Benefits of the Azure Data Factory
Azure Data Factory is an agile, cost-effective, and highly scalable ETL service. It provides data integration solutions, allowing enterprises to integrate various systems and harness the power of data.
Some of the benefits of using Azure Data Factory include:
Azure Data Factory works with both cloud-based and on-premise data centers. It comes with built-in connectors that enable seamless integration, allowing data ingestion from enterprise data sources.
Unlike traditional ETL tools, Azure Data Factory is a fully managed system. You don’t need cloud architects to configure, install, and maintain data integrations. It leverages Azure Integration Runtime to handle data movements, APIs to ensure peak performance, and Spark Cluster to map data flows.
Azure Data Factory allows users to transform data by mapping data flows based on the Apache Spark platform. DevOps teams can create code-free transformation, reducing time taken and improving productivity.
The Bottom Line
Azure Data Factory is a revolutionary ETL tool that provides data integrations with minimal coding. It works on all enterprise data centers and provide seamless data integrations. It orchestrates data, making it easier to structure and acquire insights for decision making.
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