Big data integration and processing can be a tedious task for every organization. However, it’s needed to unlock transformational insights. With tools like Azure Data Factory, organizations can make the most of their data regardless of the source and with almost no complexities and challenges.
This article will provide a quick guide to Azure Data Factory, a cloud-based data integration tool.
What Is Azure Data Factory?
Azure Data Factory is a fully managed cloud service by Microsoft that allows users to build scalable extract-transform-load (ETL), extract-load-transform (ELT), and data integration pipelines for their data. These pipelines ingest data from multiple sources—on-premises and cloud environments—and users can process, orchestrate, and transform data using the low-code intuitive authoring interface of Azure Data Factory (ADF). After transformation by ADF, the data can flow downstream into data stores, data warehouses, and business intelligence (BI) tools for analytics and reporting.
Users can also automate, monitor, and manage the whole process using data-driven workflows in real time using programmatic and UI mechanisms. To summarize, Azure Data Factory allows you to transform data efficiently, create data pipelines, and load them into various platforms of your choice to get meaningful insight from the data.
How Does Azure Data Factory Work?
Fundamentally, Azure Data Factory can orchestrate, integrate, and transform data, as well as build a scheduled or one-time pipeline. All these happen through four significant steps: connect, transform, CI/CD, and monitor.
Connecting Azure Data Factory to data sources
Firstly, structured, unstructured, or semi-structured data need to be collected from various sources. With 90+ pre-built connectors, Azure Data Factory can easily draw data from a wide variety of on-premises, cloud, or hybrid sources from inside or outside the Azure ecosystem. The data is then moved and stored in a centralized data store in the cloud using the copy activity of Azure Data Factory’s data pipeline. This whole process is the connect stage of the data factory’s operation.
Transforming data in Azure Data Factory
The data is then transformed using the data factory’s mapping data flows to create and manage data transformation graphs. Azure Data Factory has a no-code/low-code interface for defining data transformations. It’s also extensible, so users can code custom, complex transformations themselves or utilize external platforms like HDInsight Hadoop, Azure Data Lake Analytics, and Azure Synapse Analytics to transform and enrich their data.
Continuously integrate and deploy pipelines
ETL and ELT activities are developed and delivered to production during the continuous integration and continuous deployment (CI/CD) phase using Azure DevOps and GitHub from the data factory. After this, users can load the data into business intelligence tools and analytics engines like Azure SQL Database.
Monitoring Azure Data Factory Pipelines
To prevent and manage breaks or fails, monitoring must be in place. Data pipelines, scheduled activities, and workflows can be monitored visually in an Azure Data Factory monitoring dashboard, or by using API, Azure Monitor, Azure Monitor Logs, and PowerShell to check for success and failure rates. Diagnostic logs can also be generated on Azure Event Hubs and analyzed using Log Analytics. Failed pipelines can be restarted from their starting point, from a specific point in the pipeline, or from where the failure occurred.
What Are the Benefits of Azure Data Factory?
Azure Data Factory’s beauty is in providing users with an agile and scalable end-to-end platform for data integration. Without Azure Data Factory, organizations would have to write custom codes from scratch to integrate, transform, and maintain data from various sources. This process can be daunting, expensive, and hard to maintain. However, Azure Data Factory offers much more to organizations.
Below are a few other benefits of Azure Data Factory.
Low Code/No Code Integration and Transformation
Without writing custom code, the data factory allows users to design data integration workflows and run data transformation operations by mapping in its visual UI interface environment. This makes Azure the choice for citizen developers (i.e., people who build business applications using low code/no code tools). Besides making it accessible to everyone regardless of technical skill, ADF offers a low learning curve as users can build ETL and ELT processes and monitor pipelines code-free.
Built-In Monitoring and Alerting
Azure Data Factory offers built-in monitoring and alerting visualizations. This provides complete visibility and allows users to be proactive and get notified about issues that can disrupt their data pipeline workflows.
Scalability, Performance, and Enterprise-Readiness
Azure Data Factory was designed to handle big data from various sources. Thus, it comes built with multiple data connectors, parallelism, and time-slicing features. For example, Azure Data Factory leverages Azure integration runtime and Spark Cluster for data migration, flows, code generation, and maintenance. All these technologies and features make it an efficient enterprise-ready tool for data integration and workflow management.
Azure Data Factory is built on Azure’s security infrastructure to help secure your data. For example, data store credentials are stored and managed by Azure Data Factory Managed Store and Azure Key Vault. Since it’s a fully managed tool, Azure takes care of all security updates, patches, and encrypted certificates users need and ensures compliance with global data compliance policies.
Fully Managed and Cost-Efficient
The beauty of a fully managed tool is that the service provider handles everything from the infrastructure and technology that powers the platform to the configuration and maintenance of the data integration engine. Building a tool with this capability can take time and effort. Thus, the data factory pay-as-you-go service offers its users convenience, as they don’t need to pay any of these upfront costs and can scale as much as their organization needs.
Data Integration with Azure Data Factory
Azure Data Factory’s strong data integration capabilities rely heavily on several core components.
While a data factory can have multiple pipelines, a pipeline consists of activities designed to perform a specific task. Pipelines make scheduling and monitoring tasks easier because users can manage activities in sequence (chaining) or in parallel (independent) rather than individually.
A pipeline run is an instance of the activities the pipeline is executing. These can be activated manually, by a trigger definition, or by passing arguments to the parameters defined by pipelines.
These represent a single processing step in a pipeline. ADF currently supports three activities: data copy, transformation, and activity orchestration.
This represents the data and data structure you want to input or output in your activities. Dataset can also be linked to a linked service.
These are the connection strings—configuration and information—needed for data connection. They can also represent the data store, or the compute resource hosting the execution of an activity.
Integration Runtime is a computing infrastructure that provides fully managed data flows, activity, movement, and SQL Server Integration Services (SSIS) package execution tasks in data pipelines. There are three types:
- Azure Integration Runtime to fully manage the data movement activities.
- Self-Hosted Integration Runtime manages activities between cloud data stores and data stores in private networks.
- Azure-SSIS IR, which executes SSIS packages.
Other components of Azure Data Factory include:
- Mapping data flow, the transformation logic used to transform your data.
- Triggers, the processing units that determine if a pipeline should be executed or not.
- Parameters, the key and values of your read-only configuration that are passed so your pipeline can run.
- Variables, used with parameters to store temporary values between activities and data pipelines.
Azure Data Factory vs. SQL Server Integration Services (SSIS)
While both SQL Server Integration Services (SSIS) and Azure Data Factory are robust data integration tools and similar in nature, they have some subtle differences.
The initial version of Microsoft SSIS, a robust on-premises data integration and ETL tool, was released with SQL Server 2005. This tool supports ETL processes and data migration from various data sources without writing custom code. It achieves all that through four critical components:
- Control Flow, the SSIS package that arranges and controls the order of the various components.
- Data Flow, which handles the ETL process.
- Packages, which are collections of the Control and Data Flow tasks.
- Parameters, which are the variables run on the SSIS packages.
One thing that makes Azure Data Factory different from SSIS is that it can process big data sets. While SSIS supports only batch processes, ADF supports batch and streaming data processes. ADF can also auto-detect and parse schema in various file formats; thus, it supports structured and unstructured data. On the other hand, SSIS only processes structured data and requires manual schema definition. Lastly, ADF supports SSIS package migration to ADF.
Task Factory Azure Data Factory Edition
SolarWinds Task Factory® Azure Data Factory Edition is a third-party component that enables organizations to move their SSIS packages into ADF without much hassle. The lift and shift strategy is the on-premises to cloud migration of workloads without changes in code, architecture, or infrastructure. Task Factory also streamlines the deployment of your SSIS packages and supports installation and setup in the ADF SSIS Integration Runtime (IR). Thus, SSIS’s users don’t need to give up any enhancements during migration.
The core component of the SolarWinds Task Factory has been optimized to ensure fast performance and accelerated ETL processes. To get started with the Task Factory, check out the documentation.
Take advantage of our 14-day trial by giving Task Factory a try today!
This post was written by Ifeanyi Benedict Iheagwara. Ifeanyi is a data analyst and Power Platform developer who is passionate about technical writing, contributing to open source organizations, and building communities. Ifeanyi writes about machine learning, data science, and DevOps, and enjoys contributing to open-source projects and the global ecosystem in any capacity.