Azure Data Factory vs Databricks: What's the Difference?

Both Azure Data Factory (ADF) and Databricks are useful analytical cloud-based services, but data engineers may not understand the differences between the two. Both can be used for a variety of use cases related to data engineering, including migrating on-premises SISS packages to Azure, performing operational data integration analytics, and integrating data into data warehouses.

These Microsoft Azure products can reliably provide scalable data transformations, aggregations and movement, but one does not replace the other. ADF is primarily a data integration service. It’s used to move data from various sources. Databricks focuses on collaboration among team members so they can code, transform and use data under a unified analytics platform. The two services also vary in the following ways:

• Ease of use - ADF is a Graphical User Interface (GUI)-based data integration tool, while Databricks requires knowledge of Java, R, Python or other coding languages.
• Coding flexibility – With ADF, you have to modify your code to complete activities in less time, whereas Databricks gives developers the opportunity to fine-tune code.
• Batch and stream data processing – Both support batch and streaming, but ADF does not do it in real-time, but when combined with Spark APIs, Databricks can.

Read on to explore more of the differences in each of these services, and the potential applications for each.

What is Microsoft Azure Data Factory?

When dealing with big data, analysts and data scientists need to transform raw, unorganized data into meaningful business insights for the organization. ADF is a fully managed, serverless data integration service that allows you to ingest, transform and load data inside of Azure no matter where the data comes from or what form it’s in. ADF connects to your databases, whether they’re on-premises or in the cloud, and links them to a cloud-based instance of your data so you can use the data factory. ADF lets you use either an extract-transform-load (ETL) or a derivative of that, an extract-load-transform (ELT) tool, to pull data out of the database and put it into a form on which you can operate and change that data so you can use it in other places like the Azure database. In the aforementioned two acronyms, the “E” represents extracting data from one database, the “T” represents the ability to transform that data by deduplicating it, combining it, and ensuring its quality, and the “L,” represents the ability to load that data into a target database so it’s made available into a standardized solution that allows ADF to expose, analyze, consume and visualize that data.

Why would a business want to use ADF?

Large organizations have a lot of data from their customers stored in different databases that need to be transformed into standardized form and loaded into an Azure Sequel Database, a data warehouse that will allow you to see that data and make it consumable through complex analytics like business intelligence and machine learning, giving you insights into customer profiles and the ability to find customer issues that you can address. This can be done by using ADF and pipelines to consume the data, standardize it, and expose it for analysis.   

How it works

With ADF, you can create and schedule data-driven workflows, or pipelines, and take data from a variety of data stores. From there, you can transform data by using Azure Databricks, Azure SQL Database or similar services and organize it into meaningful data stores or data lakes.

ADF can connect to all necessary data and processing sources, including SaaS services, file sharing and other online resources. You can design data pipelines to move large amounts of data at specific intervals or all at once.

Key benefits

It’s particularly beneficial to use ADF if your organization has a multicloud architecture, as you can integrate and centralize data that is stored on various cloud platforms. ADF also integrates and extracts information from applications that write user data to different locations, such as relational databases and object storage in the cloud.

Below are some other benefits to ADF compared to other data integration tools:

• No-code data workflows – ADF can be configured to collect and integrate data from most mainstream data sources, including databases, cloud storage services and file systems, without writing a single line of code. It’s also a good fit for non-technical users, unlike Microsoft SQL Server Integration Services (SSIS) and other more traditional tools that require users to have knowledge of specific coding languages.
• Easy SSIS migration – If your organization is used to using SSIS data pipelines, ADF can lift and shift them with minimal effort. And, since SSIS and ADF are both Microsoft services, it provides the smoothest transition from on-premises cloud data integration, when compared to other data integration services.
• Large supply of data collectors – ADF offers nearly 100 pre-built data connectors, many of which you set up instantly and import data from external sources. This provides you with significantly more collectors than are offered in the public cloud.
• Built-in monitoring and alerts – Monitoring visualization is built in to ADF. This means you can easily track the status of data integration operations, allowing you to identify and react to problems and set up alerts to warn about potential failed operations.
• Fewer up-front costs – ADF offers consumption-based pricing. This varies from the large initial investment needed for most on-premises data integration tools.

Key Differences: Azure Data Factory vs. Databricks

As mentioned previously, there are several key differences between ADF and Databricks. That doesn’t necessarily mean they won’t both be used, as the largest difference is their intended purpose. ADF is primarily used to perform ETL and other data movement processes at scale. This means the process isn’t limited by CPU power or storage because it is executing in the cloud. Databricks also operates at scale, but instead offers a collaborative platform to combine data, perform ETL and build machine learning models under the same platform.

Ease of use

ADF uses GUI tools that allow users to deliver applications faster, thereby increasing productivity. In fact, users can migrate terabytes or petabytes of data to the cloud in a few hours. It also has a drag-and-drop feature that allows you to visually create data pipelines. Databricks, conversely, requires users to have knowledge of Python, Spark, Java or SQL to perform coding activities using notebooks, a more in-depth process that takes much more time to complete.

Coding flexibility

Although ADF does use GUI tools to facilitate ETL through the pipeline, developers are unable to modify backend code. Databricks provides much more flexibility to fine-tune coding and improve performance. Databricks also allows users to easily switch between programming languages, which can be useful when functions from different languages are required.

Data processing

Both ADF and Databricks support batch and stream data processing. Databricks supports live streaming of this bulk data processing, as well as archive data streaming, which occurs in less than 12 hours. These options are supported through Spark API. But ADF supports archive data streaming only.

Conclusion

The decision whether to use ADF or Databricks can vary depending on purpose, scope, timeframe, size of the project, organizational needs and other factors. They are both invaluable cloud-based tools for organizations that need to migrate, aggregate and transform data. With a solid understanding of and training in Microsoft Azure services or Databricks, you can evaluate and execute on your organization’s needs with confidence.