Data is playing a very essential role in today's technology-driven world. Data is stored in an unstructured format for various business uses, especially for analytics. Processing unstructured and semi-structured data is a complex process and requires considerable time, resources, and effort. We need advanced technology solutions to eliminate the hurdles involved in data processing.
Azure Data Factory and Databricks are two cloud solutions that streamline the end-to-end process of ETL & integration and provide a strong foundation for analytics. ADF offers ETL & integration services, whereas Databricks streamlines data architecture and provides a centralized platform for AI, data science, analytics, etc.
In this blog, we will understand ADF and Data factory and compare Azure data factory and Data Bricks. Also, we will discuss some of the key benefits of ADF and the benefits of the Data factory.
Azure Data Factory is a serverless cloud ETL and data integration service offered by Azure. It is a hybrid cloud platform designed to support ETL and ELT operations. Unlike traditional ETL solutions, ADF has an advanced code-free UI that supports users in performing complex operations. Also, it helps users with operations like defining datasets, building pipelines, and mapping data to destinations.
Azure Data Factory performs all the ETL and integrations using components like datasets, pipelines, activities, triggers, integration runtime (IR), etc.
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Related Article: Azure Data Factory Interview Questions
Azure Databricks is a collaborative, spark-based analytics platform that leverages Azure Lakehouse to unify all your AI and Analytical workloads. It is highly flexible and supports major cloud providers like AWS, GCP, and Azure. Databricks offers different environments for Data Science, SQL, Machine Learning, Data Engineering, etc. Data engineering teams and machine learning experts can work in a collaborative environment on data science projects.
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The following table is designed to consider different parameters and explain the significant difference between Azure Data Factory and Azure Databricks.
| Parameter | Data Factory | DataBricks |
| Purpose | The primary purpose of ADF is to build integrations for ETL and ELT operations. | Databricks are used for data preparation and collaboration. |
| Ease of Usage | ADF offers easy-to-use drag-and-drop features to build & manage pipelines. | Databricks supports languages such as R, Python, Scala, and Java using the Notebook option. |
| Flexibility in Coding | In ADF, developers can not alter or change the backend code. | Databricks is flexible & developers can alter and optimize code for better performance. |
| Supported Data Structures | Both structured and unstructured data support. | Both structured and unstructured data support. |
| Data Processing | ADF supports only batch and steam processing but not live-streaming | Data processing like batch, stream, and live streaming is supported. |
| Supported Languages | Python, Powershell, .Net | SQL, Python, R, Scala |
| Pricing Model | Pay-as-you-go model | Pay-as-you-go model |
Following are some of the notable advantages of using ADF:
Wrapping up:
This blog has discussed data processing and the role of Azure Data Factory and Databricks. I believe this blog post has helped clear up your confusion between ADF and databricks and given you a fair understanding of how these two technologies differ.
By Tech Solidity
Last updated on February 13, 2024
