Azure Data Factory (ADF) and Databricks are two Cloud services that handle complex and unorganized data with Extract-Transform-Load (ETL) and Data Integration processes to facilitate a better foundation for analysis. While ADF is used for Data Integration Services to monitor data movements from various sources at scale, Databricks simplifies Data Architecture by unifying Data, Analytics, and AI workloads in a single platform.
This article describes the key differences between Azure Data Factory and Databricks. It briefly explains Azure Data Factory and Databricks along with its benefits to gain ideas for the underlying differences relatively. Read on for more information on Azure Data Factory vs Databricks.
Azure Data Factory vs Databricks: Key Differences
Interestingly, Azure Data Factory maps dataflows using Apache Spark Clusters, and Databricks uses a similar architecture. Although both are capable of performing scalable data transformation, data aggregation, and data movement tasks, there are some underlying key differences between ADF and Databricks, as mentioned below.
Azure Data Factory vs Databricks: Purpose
ADF is primarily used for Data Integration services to perform ETL processes and orchestrate data movements at scale. In contrast, Databricks provides a collaborative platform for Data Engineers and Data Scientists to perform ETL as well as build Machine Learning models under a single platform.
Azure Data Factory vs Databricks: Ease of Usage
Databricks uses Python, Spark, R, Java, or SQL for performing Data Engineering and Data Science activities using notebooks. However, ADF provides a drag-and-drop feature to create and maintain Data Pipelines visually. It consists of Graphical User Interface (GUI) tools that allow delivering applications at a higher rate.
Azure Data Factory vs Databricks: Flexibility in Coding
Although ADF facilitates the ETL pipeline process using GUI tools, developers have less flexibility as they cannot modify backend code. Conversely, Databricks implements a programmatic approach that provides the flexibility of fine-tuning codes to optimize performance.
Azure Data Factory vs Databricks: Data Processing
Businesses often do Batch or Stream processing when working with a large volume of data. While batch deals with bulk data, streaming deals with either live (real-time) or archive data (less than twelve hours) based on the applications. ADF and Databricks support both batch and streaming options, but ADF does not support live streaming. On the other hand, Databricks supports both live and archive streaming options through Spark API.
Connectivity to Data on-Premise
Connectivity to on-premise data sources is not yet supported by Azure Data Factory Mapping Data Flows. The first Copy Activity uses integration run times to connect to local SQL servers instead of Spark clusters.
Conversely, Spark clusters are supported by Databricks. It can therefore manage large data more effectively than Azure Data Factory. It also facilitates easy connections to different on-premises data sources.
Businesses continuously anticipate the growing demands of Big Data Analytics to harness new opportunities. With rising Cloud applications, organizations are often in a dilemma while choosing Azure Data Factory and Databricks. If an enterprise wants to experience a no-code ETL Pipeline for Data Integration, ADF is better. On the other hand, Databricks provides a Unified Analytics platform to integrate various ecosystems for BI reporting, Data Science, and Machine Learning.
In this article, you have learned about the comparative understanding of Azure Data Factory vs Databricks. This article also provided information on Azure Data Factory, Databricks, and their benefits.
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