What is Azure Data Factory?

Data is being produced at an unparalleled pace in modern times and is kept in diverse relational and non-relational databases and storage systems. However, simply having large amounts of data is not enough to extract valuable insights. Raw data is unstructured, disorganized, and lacks context, making it difficult for analysts, data scientists, and business decision-makers to understand its meaning or derive actionable insights from it.

To tackle this issue, a solution for data is needed that can manage and implement procedures to refine and convert large volumes of raw data into valuable and actionable business insights. This service should include tools and techniques to clean, organize, and structure data, as well as sophisticated algorithms for data analysis and modeling. Today, we will discuss one such service from Microsoft’s Azure Platform, known as Azure Data Factory.

Using this service, organizations can unlock the full potential of their big data and make data-driven decisions that drive growth, innovation, and competitive advantage.

This blog post will provide a comprehensive understanding of Azure Data Factory, including its components and how it enables the organization and extraction of business insights from large data sets.

What is Azure Data Factory written on clouds.

So, what is Azure Data Factory MEANS?

Azure Data Factory is a fully managed cloud service designed for intricate hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. This service automates and manages the movement and transformation of data across various data stores and computes resources. Using Data Factory, users can create and schedule data-driven workflows, or pipelines, to extract data from disparate data stores and build complex ETL processes to transform data using visual data flows or compute services such as Azure HDInsight, Azure Data bricks, Azure Synapse Analytics, and Azure SQL Database.

The main goal of Data Factory is to collect data from one or multiple sources, transform it into a format that is appropriate for usage, and facilitate convenient processing by end-users. As data sources may present data in different formats and contain noise that requires filtering, Azure Data Factory allows users to transform data to ensure compatibility with other services in the data warehouse solution.

Use cases 

For example, consider a healthcare organization that collects data from various sources such as patient records, hospital equipment, and medical devices. The organization wants to analyze this data to improve patient outcomes, identify areas for cost savings, and enhance overall healthcare delivery.

To do this, the organization needs to integrate the data from these disparate sources into a unified data platform in the cloud, such as Azure Data Lake Storage. The data needs to be cleaned, transformed, and enriched with additional reference data from other sources, such as healthcare guidelines and medical research papers.

The organization plans to use Azure Data Factory to orchestrate this data movement and transformation. It will create pipelines to ingest data from various sources, transform it using Azure Data bricks or Azure HDInsight, and then store it in Azure Data Lake Storage.

The organization also plans to use Azure Machine Learning to build predictive models that can analyze the data and generate insights for better decision-making. Finally, the organization will use Azure Synapse Analytics to create interactive dashboards and reports for its stakeholders.

By using Data Factory, the healthcare organization can streamline its data integration and transformation process, reduce costs, and improve healthcare outcomes for its patients.

What are the main components of Azure Data Factory?

A subscription to Azure could contain single or multiple instances of Azure Data Factory, which is comprised of the following essential elements:


  • In Azure Data Factory, a pipeline is a logical grouping of activities that perform a specific unit of work. It can be thought of as a workflow that defines the sequence of operations required to complete a task. A pipeline can include various activities that perform different types of data integration and transformation operations.
  • An Azure Data Factory instance can have one or more pipelines, depending on the complexity of the data integration and transformation tasks that need to be performed.
  • A pipeline in can be run manually or automatically using a trigger. Triggers allow you to execute pipelines on a schedule or based on an event, such as the arrival of new data in a data store.
  • Activities in an Azure Data Factory pipeline can be chained together to operate sequentially or independently in parallel. Chaining activities together can create a workflow that performs a sequence of operations on the data, while running activities independently in parallel can optimize performance and efficiency.
  • An example of an activity in a pipeline could be a data transformation activity, which performs operations on the data to prepare it for analysis or consumption. Data transformation activities can include various operations such as filtering, sorting, aggregating, or joining data from multiple sources. By combining multiple data transformation activities in a pipeline, you can create complex workflows that perform sophisticated data integration and transformation tasks. For example, you could use data transformation activities to cleanse and standardize customer data from multiple sources before loading data into a warehouse for analysis.


  • Activities in a pipeline: In Azure Data Factory, activities represent an individual processing step or task in a pipeline. They can include data movement activities, data transformation activities, and control activities that define actions to perform on your data.
  • Activities in a pipeline define actions to perform on your data, such as copying, transforming, or filtering the data. Each activity in a pipeline consumes and/or produces datasets, which are the inputs and outputs of the activity.
  • Activities in Azure Data Factory can support data movement, data transformation, and control tasks. Data movement activities are used to copy data from one data store to another, while data transformation activities perform operations on the data, such as filtering, sorting, or aggregating it. Control activities are used to control the flow of data between activities and perform conditional logic or error handling.
  • Activities in a pipeline can be executed in both a sequential and parallel manner. Chaining activities together in a sequence can create a workflow that performs a series of operations on the data, while executing activities in parallel can optimize performance and efficiency.
  • Activities can either control the flow inside a pipeline or perform external tasks using services outside of Data Factory. For example, you might use a web activity to call a REST API or a stored procedure activity to execute a stored procedure in a SQL database.
  • An example of an activity in Azure Data Factory could be a copy activity, which copies data from one store to another data store. The copy activity can include various settings to specify the source and destination data stores, the data to be copied, and any transformations to be applied during the copy operation. By combining multiple activities in a pipeline, you can create complex data integration and transformation workflows that meet your specific business needs.

Type of Activities

  1. Data Movement: Copies data from one store to another.
  2. Data Transformation: Uses various services like HDInsight (Hive, Hadoop, Spark), Azure Functions, Azure Batch, Machine Learning, and Data Lake Analytics.
  3. Data Control: Performs actions like invoking another pipeline, running SSIS packages, and using control flow activities like for each, set, until, and wait.


  • A dataset is a logical representation of the data that you want to work within your Azure Data Factory pipelines. A dataset defines the schema and location of the data, as well as the format of the data. A dataset can be thought of as a pointer to the data that you want to use in your pipeline.
  • A dataset represents a particular data structure within a data store. For example, if you have a SQL Server database, you might have multiple datasets that each represent a table or view within that database.
  • A dataset is used as the input or output of an activity in your pipeline. For example, if you have a pipeline that copies data from one storage account to another, you would define two datasets: one for the source data and one for the destination data.
  • To work with data in a particular data store, you need to create a linked service that defines the connection details to that data store. In the case of Azure Blob storage, you would create a Blob Storage linked service that specifies the account name, access key, and other details of the storage account.
  • Once you have defined a linked service, you can create a dataset that references the data that you want to use in your pipeline. In the case of Azure Blob storage, you would create a dataset that specifies the container and blob name, as well as the format of the data (such as Parquet, JSON, or delimited text).
  • In addition to working with Blob storage, Azure Data Factory supports many other data stores, including SQL Database and Tables. For SQL Database, you would create a dataset that specifies the server’s name, database name, and table name, as well as any relevant authentication details. For Tables, you would create a dataset that specifies the storage account name and table name.

Linked Services

  • Linked service is a configuration entity in Azure Data Factory.
  • It defines a connection to the data source and specifies where to find the data.
  • Linked services are similar to connection strings that define connection information for Data Factory to connect to external resources.
  • The information in a linked service varies based on the resource being connected to
  • A linked service can define a target data store or a compute service.
  • Examples of linked services include Azure Blob Storage linked service for connecting a storage account to Data Factory and Azure SQL Database linked service for connecting to a SQL database.

Linked services serve two main purposes in Azure Data Factory:

  1. To define the connection to a data store, such as SQL Server database, PostgreSQL database, Azure Blob storage, and many others. This allows Data Factory to read from or write to that data store.
  2. To define a compute resource that can run an activity. For example, the HDInsight Hive activity requires an HDInsight Hadoop cluster as the compute resource to execute the activity. This enables Data Factory to leverage the power of various compute resources to perform data processing tasks.

Integration Runtime (IR)

  • Integration Runtime (IR) acts as a bridge between Activities and Linked Services in Azure Data Factory.
  • IR provides a computing environment where activities can either run on or get dispatched from.
  • IR is responsible for moving data between data stores and the Data Factory environment.
  • IR can be installed on an Azure VM, self-hosted machine, or run as a managed service.
  • IR is used to provide connectivity to on-premises and cloud-based data stores and compute resources.
  • IR can be configured to manage data flow traffic and data encryption in transit.

Integration Runtime (IR) has three types

  • The first type is Azure IR which is a fully managed, serverless computing service provided by Azure. It enables data movement and transformation within the cloud data stores.
  • The second type is Self-hosted IR which facilitates the movement of data between cloud data stores and a data store that is hosted on a private network.
  • The third type is Azure-SSIS IR which is necessary for executing SSIS packages natively.


The process of initiating the execution of a pipeline is performed by triggers. Triggers have the responsibility of determining the exact time when a pipeline should be executed. They can be set to execute pipelines based on a wall-clock schedule, at a regular interval, or when a specific event takes place. Essentially, triggers serve as the mechanism that decides when a pipeline should be executed, and they are the building blocks that represent the unit of processing required to start a pipeline run.

Trigger types include:

  • Schedule: triggers pipeline execution at a specific time and frequency, such as every Sunday at 2:00 AM.
  • Tumbling Window: triggers pipeline execution at periodic intervals, such as every two hours.
  • Storage Events: triggers pipeline execution in response to a storage event, such as a new file being added to Azure Blob Storage.
  • Custom Events: triggers pipeline execution in response to a custom event, such as an EventGrid event.

Data Flows

These activities enable data engineers to create data transformation logic visually without the need to write any code apart from data expressions. The transformation can be done in multiple steps using a visual editor. These activities are executed within the Azure Data Factory pipeline on the managed Spark cluster of Azure Databricks, allowing for scalable processing. ADF manages all the data flow execution and code translation, making it easy to handle large amounts of data.

Mapping data flows

Mapping data flow is a feature that allows the creation and management of graphical representations of data transformation logic, which can easily be used to transform data of any size. It enables the creation of a library of reusable data transformation routines that can be executed in a scaled-out manner from ADF pipelines.

Control flow

The control flow is a method of organizing the execution of activities in a pipeline. It allows for activities to be linked in a particular order, specifying pipeline-level parameters and passing arguments when invoking the pipeline either on-demand or through a trigger. Additionally, the control flow enables custom-state passing and looping containers like For-each iterators, which assist in executing activities repeatedly.


Parameters are read-only configurations that consist of key-value pairs. They are defined in a pipeline and passed during execution from the run context, which is created by a trigger or manually executed pipeline. Activities within the pipeline use the parameter values.

Datasets are strongly typed and reusable entities that can be referenced by activities. They can consume the properties defined in the dataset definition.

Linked services are also strongly typed and reusable entities. They contain the connection information for a data store or a computing environment.


Variables are a type of data structure that allows data engineers to temporarily store values within pipelines. They can also be used in combination with parameters to enable the transfer of values between pipelines, data flows, and other activities.

How does Azure Data Factory work?

Data Factory offers a comprehensive end-to-end platform for data engineers, comprising interconnected systems.

Connect and collect: Enterprises need to collect various types of data from disparate sources, including on-premises, cloud-based, structured, unstructured, and semi-structured data. This data arrives at different intervals and speeds.

To build an effective information production system, the first step is to connect to all required sources and move the data to a centralized location for processing.

Data Factory provides a managed service to integrate and move data between sources and processing locations, including software-as-a-service (SaaS) services, databases, file shares, and FTP web services. This eliminates the need for costly custom components and services and provides enterprise-grade monitoring, alerting, and controls.

The Copy Activity in a data pipeline can move data from various source data stores to a centralized store in the cloud for further analysis and transformation using Azure Data Lake Analytics or Azure HDInsight Hadoop cluster.

Transform and enrich: Once data is centralized in the cloud, ADF’s mapping data flows can be used for processing and transformation without requiring knowledge of Spark clusters or programming. Alternatively, if you prefer manual coding, ADF offers external activities for executing transformations on compute services like HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning.

Publish: ADF supports CI/CD of data pipelines using Azure DevOps and GitHub, enabling incremental development and delivery of ETL processes. Once the data is transformed, it can be loaded into analytics engines like Azure Data Warehouse, Azure SQL Database, or Azure Cosmos DB, accessible to business users via BI tools.

Monitor: After creating and implementing your data integration pipeline, it is crucial to track the activities and pipelines for successful execution and error identification. Azure Data Factory provides several built-in monitoring options, such as Azure Monitor, PowerShell, API, health panels on the Azure portal, and Azure Monitor logs.

Final Wrap-up

In conclusion, Azure Data Factory is a powerful tool for building and managing data integration pipelines that allow enterprises to efficiently move, transform, and process their data across various sources and destinations. With its intuitive user interface, support for various data stores and processing services, and integration with CI/CD tools, Data Factory provides a comprehensive end-to-end platform for data engineers.

At AnAr Solutions, our team of experienced BI professionals can assist you in leveraging the full potential of Azure Data Factory to build and manage your data integration pipelines. Whether you need help with designing and implementing your data architecture, optimizing your data processing workflows, or monitoring and managing your data pipelines, our team can provide you with customized solutions that meet your specific business needs.

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