Azure Data Factory vs. Databricks is the battle between the two widely used data integration tools. Both ADF and Databricks are capable of handling structured and unstructured data. However, they come with their own upsides and downsides.
Azure Data Factory acts as an orchestration tool for data integration services. The primary role of ADF is to carry out ETL workflows and orchestrate data transmission at scale.
On the other hand, Azure Databricks acts as a single collaboration platform. The main aim of the tool is to help data engineers and data scientists to perform ETL and build ML models.
In this head-to-head comparison guide, we will compare two powerful technologies of the cloud computing world.
What Is Azure Data Factory?
Azure Data Factory (or ADF) is a cloud-based PaaS (Platform as a Service) offered by the Microsoft Azure platform. The pre-built connectors make the tool suitable for hybrid Extract-Load-Transform (ELT), Extract-Transform-Load (ETL), and other data integration pipelines.
Below are a few benefits of ADF for data science projects.
Fully Managed: As the deployment process of traditional ETL tools is complex, organizations need experts to install, configure, and maintain data integration environments. However, this is not the case with ADF. It is fully managed by Microsoft and utilizes Azure Integration Runtime to handle data movements.
Low-Code: ADF enables developers to transform data by mapping data flows. Users can create code-free transformations to reduce the turnaround time for data analytics. Hence, it improves business productivity.
Graphical User Interface: Unlike traditional ETL platforms, ADF provides a graphical user interface where drag-and-drop features are used to quickly create a data integration pipeline. The best part about GUI is that such developments help users avoid configuration issues.
What Is Databricks?
Undoubtedly, Azure Data Factory and Databricks are two popular ETL and data engineering tools. However, they are slightly different. Unlike ADF, which is a PaaS tool, Azure Databricks is a SaaS-based data engineering tool. It helps you process and transforms massive data quantities to build ML models. Additionally, Databricks supports various cloud services, including AWS, Azure, and GCP.
Below are some advantages of the Apache Spark-based distributed platform.
Integration: Databricks seamlessly integrates with Azure to drive big data solutions with ML tools in the cloud. Users can visualize the ML solutions in Power BI using the Databricks connector.
Collaboration: Databricks instantly bring the scripts written in notebooks to the production phase. Multiple members can build data modeling and machine learning applications efficiently using the collaborative feature.
Adaptability: Databricks allows different programming languages like SQL or Python to interact with Spark. The Spark-based analytics incorporates Language API at the backend to facilitate its interaction with Spark. That said, Databricks is regarded as highly adaptive.
No matter which tool you choose, contacting the experts is important. Inferenz data experts understand the specific needs of businesses, so you can select the right data integration tool.
Key Differences Between Azure Data Factory Vs. Databricks
Both ADF and Databricks use a similar architecture and help users perform scalable data transformation. According to the Statista report, global data creation will rise to more than 180 zettabytes by 2025. Witnessing the growth of data, organizations are adopting cloud computing solutions. Before you choose, it’s important to learn the major differences between the two.
Ease Of Usage
With Azure Data Factory, users can quickly perform complex ETL processes. The drag-and-drop feature allows users to create and maintain data pipelines visually. On the contrary, Databricks uses multiple programming languages, including Python, Java, R, Spark, or SQL, during data engineering and data science project.
Verdict: ADF wins as it is easier to use than Data bricks.
Purpose
Azure Data Factory is primarily used for ETL processes and orchestrating large-scale data movements. On the other hand, Databricks is like a collaborative platform for data scientists. Here, they can perform ETL as well as build machine learning models under a single platform.
Verdict: Both platforms are suitable for different purposes. Hence, the choice between the two tools depends on the user’s needs.
Data Processing
Enterprises often perform stream or batch processing when working with large data volumes. While streaming data deals with archived or live data based on the application, batch processing deals with bulk data. Though both ADF and Databricks can effectively support streaming and batch options, the former does not offer live streaming.
Verdict: If you’re looking to use the live streaming feature, Databricks wins the case. However, if you want a fully managed data integration service that supports batch and streaming services, go ahead with Azure Data Factory.
Coding Flexibility
Azure Data Factory streamlines the ETL pipeline process using the GUI tools. However, developers have less flexibility using ADF as they cannot modify the backend code. On the contrary, Databricks offers a programmatic approach that provides the flexibility to fine-tune codes and optimizes performance.
Verdict: Both the data integration and ETL tools offer flexible coding. Therefore, it is a tie.
Which Data Integration Tool Should You Choose?
In today’s highly competitive era, enterprises constantly focus on harnessing new opportunities using big data analytics. However, with the advancement of cloud applications, businesses are often confused between ADF and Databricks.
If you’re an enterprise looking for a no-code ETL pipeline for data integration, it’s better to choose ADF. Conversely, if you want a unified analytics platform to integrate various ecosystems for BI reporting, machine learning, and data science, choose Databricks.
To know more about Azure Data Factory vs. Databricks tool comparison, feel free to contact the experts of Inferenz today!
FAQs About Azure Databricks Vs. ADF
Why use Databricks instead of ADF?
Azure Data Factory is generally used for ETL processes, data movement, and data orchestration. On the other hand, Databricks helps in real-time data collaboration and data streaming.
Is Azure Databricks an ETL tool?
Yes. Databricks ETL is an AI and data tool that helps organizations accelerate the functionality and performance of ETL pipelines.
What is an Azure Synapse?
Azure Synapse integrates analytical services for bringing enterprise data warehouse and big data analytics under a single platform.
