How does Azure Data Factory work?

As a business owner, understanding how Azure Data Factory works will help you maximize its full potential. 

The ADF (Azure Data Factory) is a fully managed serverless data integration service that allows you to ingest, prepare, and transform your data. With ADF, building, managing, and deploying data pipelines from one interface is now easy. 

Azure Data Factory’s ability to connect with multiple data sources and create automated pipeline routines that are easy to scale has made it a go-to solution for small and large-scale businesses.

In this blog, you will learn how Data Azure works and the various vital components that make it a reliable solution for managing data pipelines.

What Is Azure Data Factory?

Azure Data Factory is a cloud-based data pipeline orchestrator and ETL(Extract Transform Load) tool provided by Microsoft Azure. It is designed to orchestrate and automate the movement and transformation of data. It is an all-in-one package that handles the implementation of data-driven workflow. 

Also, it has a simple user interface (UI) for intuitive visual data integration and transformation. It allows you to write custom code for advanced data manipulation.

With Azure Data Factory, you can build pipelines to process and transform data at scale. It supports batch processing, real-time data ingestion, and hybrid data movement. The service provides connectors for a wide range of data sources and destinations, including Azure services such as Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, and Azure Databricks, on-premises systems, and third-party platforms to enable end-to-end data workflows.

Let’s look at how the Azure Data Factory works.

How does Azure Data Factory work?

Azure Data Factory is a group of interconnected systems that makes end-to-end data movement easy for data engineers. We will break its operation into four simple steps to understand how Azure Data Factory works:

1. Connect and Collect
2. Transform
3. CI/CD & Publish
4. Monitor and Alert

Connect and Collect

Azure data factory can connect to a wide range of data sources. It supports over 100 connectors, making it easy for you to connect to both on-premise and cloud infrastructures. It supports many databases, files, and big data platforms like Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, and more. After connecting to multiple sources, you can collect your data into a central repository.

Transform

Azure Data Factory enables data transformation through various built-in transformation activities. You can apply data transformations such as filtering, aggregating, sorting, joining, and mapping to modify the structure and format of your data. It also integrates with Azure Databricks, which allows you to carry out advanced data transformation using Spark-based processing. You can also use CodeFree, a built-in UI-based platform for data transformation.

CI/CD & Publish

Data pipelines in Azure Data Factory define data movement and workflow transformation workflow.

With Azure Data Factory, you can execute and deploy data pipelines using Azure DevOps and GithHub. You can also define dependencies between activities, control the order of execution, and handle error conditions. Azure Data Factory provides scheduling options to run pipelines at specific times or trigger them based on events or data availability.

Monitor and Alert

Azure Data Factory offers monitoring and management features to track the execution and performance of data pipelines. You can monitor pipeline runs, view activity logs, track data lineage, and troubleshoot issues. It also integrates with Azure Monitor and Azure Log Analytics for centralized monitoring and alerting.Are you ready to harness the power of Azure Data Factory and need to know how to go about it? Look no further, as Foghorn Consulting is your solution. Contact us to see how we can help you get started.

Components Azure Data Factory

Multiple layers of technology are involved in order to best utilize Azure Data Factory to facilitate data integration and orchestration. This section will list the key components that make an end-to-end data pipeline orchestration with Azure Data Factory possible. Here are the main features:

Data Pipeline

A data pipeline represents a logical workflow in Azure Data Factory that performs a certain task. It consists of interconnected services or tools that define the steps for moving, transforming, and processing data. 

Data pipelines guide how data flow from various data sources to the end user. When you build pipelines with Azure Data Factory, you manage your data operations as a set, not as single operations.

Activities

Activities are the processing steps or building blocks of data pipelines. They represent individual tasks or operations performed on the data. Azure Data Factory provides a rich set of pre-built activities. These activities are classified into three categories: data movement, transformation, and control. 

These categories cover various data integration scenarios, such as data movement, transformation, wrangling, copying, executing stored procedures, running custom code, and more.

Data Sets

Data sets represent the data structures for the input and output data used in activities. They provide the necessary metadata and information details to interact with data sources. Data sets specify the table name, file name, location, schema, partitioning, and other properties required for data integration. 

Every data set in Azure Data Factory refers to a linked service, which determines the set of potential dataset attributes.

Linked Services

Linked services define the connection and authentication information that Data Factory needs to be able to connect to external data sources and destinations such as databases, file systems, cloud storage, SaaS applications, and more. Linked services store the connection strings, credentials, and other configuration settings required to access the data. 

Linked Services are used for mainly two purposes in an Azure Data Factory:

• To represent a data store that includes a SQL Server database, Oracle database, file share, or Azure blob storage account. 
• To define a compute resource that can host the execution of a data factory activity.

Triggers

Triggers define the configuration settings, execution schedule, or event-based conditions for managing data pipelines. It states when a pipeline should run and stop. Azure Data Factory has different types of triggers. These include time-based schedules, event-based triggers, and manual triggers. Triggers allow you to automate and schedule data integration processes based on set time intervals, specific times of the day, or available data.

Integration Runtimes

Integration runtimes provide the compute infrastructure for which ADF performs data movement and transformation activities such as running an SSIS package, data flow, data movement, and running code on various compute targets on Azure. 

Azure Data Factory supports three different integration runtimes, and they are:

•  Azure-SSIS IR (Azure SQL Server Integration Services Integration Runtime)
• Azure Integration Runtime
• Self-hosted Integration Runtime.
  

Azure Integration Runtime is a fully managed service provided by Azure, while Self-hosted Integration Runtime runs within your infrastructure.

Data Flows

Data flows allow you to design graphs and build data transformation processes within Azure Data Factory without writing codes. It provides an intuitive, code-free environment for developing ETL (Extract, Transform, Load) workflows. It also allows you to build reusable codes for your data pipeline routines.

Now that you understand the critical components of an Azure Data Factory, let’s look at some reasons why you should consider using Azure Data Factory.

Why Azure Data Factory?

Azure Data Factory offers numerous benefits you can enjoy when you use it for your data integration and orchestration projects. Here are some key benefits to using Azure Data Factory:

1. Scalable: Azure Data Factory is easily scalable and reliable, designed to handle large-scale data integration workloads. It can efficiently process and move data across various data sources and destinations, allowing you to scale your data integration processes anytime.
2. Continuous integration and Deployment: The Azure Data Factory integration makes it easy to develop, build, and deploy GitHub to Azure without stress.
3. Monitoring and Management: Azure Data Factory offers robust monitoring and management features. You can monitor pipeline performance, track data lineage, view logs, and diagnostic information, and set up alerts and notifications for any issues or failures. This helps in troubleshooting and optimizing data integration workflows.
4. Security and Compliance: Azure Data Factory incorporates security and compliance measures to protect data during integration processes. It supports secure data transfer and encryption, role-based access control (RBAC), and integration with Azure Active Directory for authentication and authorization.

Get Expert Help With Azure Data Factory

Now that you understand the Azure Data Factory components in theory and how they can benefit your business, the next step is to speak to a Foghorn expert. 

As your trusted cloud consulting partner, Foghorn is here to help you gain strategic insights, optimize your infrastructure, and unlock the full potential of cloud technologies. We’re eager to help you achieve scalable success and soar above the competition. Contact us today, and let’s start this exciting journey together!