What is Azure Data Factory – Here’s Everything You Need to Know

Companies are drowning in a sea of raw data.
As data volumes explode across enterprises, the struggle to manage, integrate, and analyze it is getting real.

Thankfully, with serverless data integration solutions like Azure Data Factory (ADF), data engineers can easily orchestrate, integrate, transform, and deliver data at scale.

Keep reading to learn in detail about this supremely versatile data integration service created by Microsoft Azure.

                 Table of Contents:

• • • What is Azure Data Factory? The Basics
    • How Azure Data Factory Works: Quick Summary
    • Top Features of Azure Data Factory
    • Key Components of Azure Data Factory
    • Azure Data Factory Data Migration: Overview
    • Azure Data Factory: Top Use Cases
    • FAQs
    • Conclusion

What is Azure Data Factory? The Basics

Azure Data Factory (ADF) is a cloud-based ETL and data integration service. The application is designed to simplify data integration for businesses. It’s essentially a fully managed service that helps you orchestrate the movement and transformation of data at scale.

How Azure Data Factory Works: Quick Summary

Connect and Collect Data:

When data resides in disparate systems, you have to declutter and organize them properly for analysis.

ADF connects to various data sources, including on-premises systems, cloud services, and SaaS applications. It then gathers and relocates information to a centralized hub in the cloud using the Copy Activity within data pipelines.

Transform and Enhance the Data:

Once centralized, data undergoes transformation and enrichment. ADF leverages compute services like Azure HDInsight, Spark, Azure Data Lake Analytics, or Machine Learning to process and analyze the data according to defined requirements.

Publish:

Transformed data is then published either back to on-premises sources like SQL Server or kept in cloud storage. This makes the data ready for consumption by BI tools, analytics applications, or other systems.

Manage Workflow:

ADF manages these processes through time-sliced, scheduled pipelines. Workflows can be set be set per your business needs (hourly, daily, weekly, or as one-time executions).

Related Post : Azure Synapse vs Databricks

Top Features of Azure Data Factory

Take a look at the salient features of this powerful serverless data integration service by Microsoft Azure:

Data Integration and ETL Workhorse

With ADF, you can design data pipelines that automate processes like Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT). But data isn’t always in the perfect format for analysis, is it?

ADF addresses this issue by allowing the pipelines to move data between various data sources, both on-premises and in the cloud. You can extract data efficiently and once gathered, you can transform this data using built-in or custom transformations, and then load it into your desired destination.

Orchestration & Scheduling

Developers can use this tool to orchestrate complex data workflows and schedule them to run on a specific cadence (hourly, daily) or even trigger them based on events (new file arrival).

This flexibility reduces the need for manual intervention and improves overall efficiency. The orchestration capabilities take the chore out of large-scale data operation management across your entire organization.

Monitoring and Management

ADF’s comprehensive monitoring features help you obtain a bird’s-eye view of your pipelines’ health. You can monitor pipeline execution status (success, failure), track data lineage (trace the flow of data from source to destination), and identify any errors or bottlenecks hindering performance.

This proactive monitoring allows you to catch and troubleshoot issues early on and ensure your data pipelines deliver reliable results consistently.

Code-free Data Flow

Mapping Data Flows in Azure Data Factory allows non-developers to build complex data transformations, plus clean, filter, and manipulate the data on the fly without writing a single line of code.

The data flows are executed on Azure-managed Apache Spark clusters behind the scenes. This feature democratizes the data transformation tasks, thereby accelerating the development process with reduced dependency on specialized coding skills.

CI/CD Support

Azure Data Factory fully embraces modern DevOps practices by allowing developers to use this tool as part of a continuous integration and delivery (CI/CD) process. Developers can seamlessly integrate your Data Factory pipelines into your existing CI/CD workflows using Azure DevOps or GitHub.

This integration allows you to version control your data factory resources, automate testing, and deploy changes across different environments with ease.

Integrated Security

This tool has a bunch of powerful security features seamlessly woven into its architecture. It integrates with Azure Active Directory (AAD) to let you use your existing user identities and permission structures for granular control over data access within data flows.

Moreover, role-based access control (RBAC) within ADF enables you to assign specific permissions to users and groups. Therefore, only authorized personnel can access and manipulate data pipelines and data stores. Data encryption is applied both at rest and in transit, safeguarding sensitive information throughout the entire data lifecycle.

Lift & Shift SSIS Packages

Azure Data Factory V2 lets you migrate existing SQL Server Integration Services (SSIS) packages to Azure directly into the cloud and run them with full compatibility using the SSIS Integration Runtime.

You can provision more nodes to handle increased workloads and scale down when not needed. More importantly, by lifting and shifting SSIS to Azure, you can reduce the total cost of ownership (TCO) compared to running on-premises.

You can learn the core concepts, features, and real-world applications of this platform in greater detail in the Azure Data Engineer Certification course.

Also Read : What is Delta Lake?

Key Components of Azure Data Factory

Behind the powerful data integration and transformation capabilities of Azure Data Factory are the following 6 components:

Pipelines

A pipeline is a logical grouping of activities that perform a task. A data factory can have multiple pipelines, each with multiple activities. The activities in a pipeline can be structured to run sequentially or concurrently.

Activities

Activities are the building blocks of a pipeline that define the actions to perform on your data. ADF supports data movement activities, data transformation activities, and control activities. Activities can be executed in a sequential or parallel manner.

Datasets

Datasets in Azure Data Factory define the schema and location of data sources or sinks. They represent the data you want to work with and are used in activities within pipelines.

By specifying details like the file format, storage location, and table structure, datasets enable efficient data access and manipulation, ensuring that pipelines can interact with data consistently and accurately.

Linked Services

Linked services define the connection information needed for Azure Data Factory to connect to external resources. They are similar to connection strings used to identify the data source.

Triggers

Triggers define when a pipeline should be executed. There are three types of triggers:

Schedule trigger (runs the pipeline on a schedule)
Tumbling window trigger (runs the pipeline periodically based on a time interval)
Event-based trigger (runs the pipeline in response to an event like file arrival)

Integration Runtimes

Integration runtimes (IR) provide the compute infrastructure for activity execution. There are three types: Azure IR (fully managed serverless compute), Self-Hosted IR (for private network data stores), and Azure-SSIS IR (for running SSIS packages).

Azure Data Factory Data Migration: Overview

Cross-Region:

• Source & Sink Setup: Configure data source (storage accounts, databases) in both regions.
• Copy Activity: Use ADF’s copy activity to define data movement. Specify source and destination details.
• Network Considerations: Configure secure communication between regions using Azure Virtual Network Peering or ExpressRoute for optimal performance.

Same Region:

• Source & Sink Setup: Define source and destination data stores within the same Azure region.
• Copy Activity: Utilize the copy activity to orchestrate data movement.
• Storage Selection: Leverage managed Azure storage services (Blob storage, Data Lake Storage) for scalability and cost-effectiveness during data transfer.

Azure Data Factory: Top Use Cases

Store Data in Azure Data Lake

Using self-hosted integration runtimes, ADF securely connects to on-premises databases or FTP servers, allowing data extraction. For online sources, ADF offers numerous built-in connectors for APIs, cloud services, and databases. 

Once connected, data can be moved to Azure Data Lake using copy activities within pipelines. This ensures the data is available for further processing and analytics.

ERP to Synapse

Azure Data Factory enables the extraction and integration of data from multiple ERP systems into Azure Synapse Analytics for reporting purposes. ADF uses connectors to connect to ERP systems like SAP, Oracle, and Dynamics. 

It can extract transactional and master data, perform necessary transformations, and load the data into Azure Synapse Analytics. This process ensures that consolidated and consistent data is available for building comprehensive reports and dashboards.

GitHub Integration

Azure Data Factory’s GitHub integration lets you store your ADF artifacts – pipelines, datasets, linked services, you name it – right in a GitHub repo. Moreover, developers can create separate branches for development and production, implement pull request workflows, and track changes over time. This GitHub integration also facilitates continuous integration and deployment (CI/CD) for your data pipelines.

And let’s not forget the cherry on top – the ability to reuse code across different Data Factory instances.

Integration with Azure Databricks

Azure Data Factory and Azure Databricks? Now that’s a power couple. This dynamic duo takes data processing to new heights. This collaboration enables seamless execution of Databricks notebooks and jobs directly from ADF pipelines, facilitating complex big data operations. 

ADF can pass parameters from your ADF pipeline straight into your Databricks code. For optimum data consistency and reliability, devs can incorporate Delta Lake within Databricks workflows, allowing for ACID transactions on data lakes. 

Data Governance 

Azure Data Factory’s synergy with Azure Purview brings a new dimension to data integration and governance. ADF’s integration with Purview automatically captures metadata about data movement and transformations, creating a comprehensive map of data flow across the enterprise.

Moreover, Purview’s governance policies can be applied to ADF pipelines for full compliance with data handling regulations and internal standards throughout the ETL process.

JSON and PowerShell

Azure Data Factory uses JSON (JavaScript Object Notation) as its fundamental language for defining resources. This approach offers several technical advantages:

Pipelines, datasets, and linked services are all represented as JSON objects, enabling version control and programmatic manipulation.

The JSON structure allows for nested definitions, supporting complex pipeline architectures with parent-child relationships between activities.

JSON’s flexibility accommodates dynamic property assignments, facilitating parameterization of pipelines for reusability.

PowerShell integration amplifies ADF’s capabilities through the Azure Data Factory module. This module provides cmdlets for comprehensive ADF management:

• New-AzDataFactoryV2Pipeline to create pipelines programmatically
• Invoke-AzDataFactoryV2Pipeline for triggering pipeline runs on demand
• Get-AzDataFactoryV2PipelineRun for retrieving detailed execution logs

FAQs

1. What is Azure Data Factory used for?

Ans. Azure Data Factory (ADF) automates data movement and transformation between various data sources. It’s like a central hub that orchestrates how your data flows across your cloud environment.

2. Is Azure Data Factory an ETL tool?

Ans. Yes, ADF is a highly efficient ETL (Extract, Transform, Load) tool. It can extract data from various sources, transform it for analysis, and then load it into your target destination (data warehouse, data lake).

3. What kind of tool is Azure Data Factory?

ADF is a cloud-based data integration service. It helps you connect to different data sources, process data, and automate data pipelines.

4. What is ADF in simple terms?

Ans. ADF is a serverless data integration service. Its job is to collect data from multiple sources, transform it, and then move it to destinations where it can be analyzed to gain valuable business insights.

Conclusion

In this day and age, companies are collecting data from a multitude of sources, more than ever before. This abundance of data poses significant challenges in distinguishing signals from noise, exacerbated by its frequent dispersion across multiple systems.

By mastering ADF, you can design scalable data pipelines, enhance data quality and consistency, and empower data-driven decision-making processes. It equips you with versatile skills crucial to succeeding in today’s data-centric environments.