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  5. Azure Data Factory vs Azure Databricks

Azure Data Factory vs Azure Databricks

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
Azure Databricks
Azure Databricks
Stacks252
Followers396
Votes0

Azure Data Factory vs Azure Databricks: What are the differences?

Introduction

Azure Data Factory and Azure Databricks are two popular data integration and processing services offered by Microsoft Azure. While both services enable users to handle and process big data, they have distinct differences in terms of their purpose, functionality, and use cases.

  1. Data Integration vs Data Processing: Azure Data Factory is primarily focused on data integration. It provides a platform for orchestrating and automating data pipelines that move and transform data from various sources to a target destination. On the other hand, Azure Databricks is designed for data processing and analytics. It offers an Apache Spark-based analytics platform that enables big data processing, machine learning, and interactive data exploration.

  2. Codeless vs Code-centric: Azure Data Factory offers a visual interface for building and managing data pipelines using a codeless, drag-and-drop approach. It allows users to easily create and monitor pipelines without writing extensive code. In contrast, Azure Databricks is more code-centric and provides a notebook-based development environment where users can write and execute code in languages like SQL, Python, R, and Scala. This gives more flexibility and control to developers but requires coding expertise.

  3. Data Movement vs Data Transformation: Azure Data Factory excels in data movement and transformation tasks. It provides various connectors and integration with other Azure services, allowing users to efficiently move and transform data between different sources and destinations. Azure Databricks, on the other hand, focuses on advanced data transformation and processing capabilities. It enables users to perform complex transformations, aggregations, and analytics on large datasets using Apache Spark's powerful processing engine.

  4. Managed Service vs Collaborative Platform: Azure Data Factory is a fully managed service provided by Azure, which means that Microsoft handles the infrastructure and maintenance tasks, allowing users to focus on their data integration workflows. Azure Databricks, on the other hand, is a collaborative platform that offers advanced analytics capabilities on top of Apache Spark. Users have more control over the underlying infrastructure and can collaborate with teammates using features like shared notebooks and interactive dashboards.

  5. Enterprise Integration vs Advanced Analytics: Azure Data Factory is well-suited for enterprise-wide data integration scenarios, enabling organizations to connect and consolidate data from various on-premises and cloud sources. It offers built-in data governance and monitoring features to ensure data accuracy and compliance. Azure Databricks, on the other hand, is geared towards advanced analytics use cases. It provides scalable machine learning capabilities, real-time data processing, and support for distributed computing, making it ideal for data scientists and data engineers.

  6. Ecosystem Integration vs Standalone Analytics: Azure Data Factory integrates seamlessly with other Azure services like Azure Blob Storage, Azure SQL Database, and Azure Data Lake Storage, enabling users to leverage the Azure ecosystem for their data integration workflows. Azure Databricks, on the other hand, can be used as a standalone analytics platform or integrated with other Azure services. It offers extensive libraries and tools for data engineering, machine learning, and data visualization.

In summary, Azure Data Factory focuses on data integration and movement, providing a codeless and managed platform for orchestrating data pipelines. Azure Databricks, on the other hand, emphasizes data processing and analytics, offering a code-centric and collaborative platform for advanced data transformations, machine learning, and interactive data exploration.

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Advice on Azure Data Factory, Azure Databricks

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Comments

Detailed Comparison

Azure Data Factory
Azure Data Factory
Azure Databricks
Azure Databricks

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Optimized Apache Spark environment; Autoscale and auto terminate; Collaborative workspace; Optimized for deep learning; Integration with Azure services; Support for multiple languages and libraries
Statistics
GitHub Stars
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
253
Stacks
252
Followers
484
Followers
396
Votes
0
Votes
0
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
Scala
Scala
Azure DevOps
Azure DevOps
Databricks
Databricks
Python
Python
GitHub
GitHub
Apache Spark
Apache Spark
.NET for Apache Spark
.NET for Apache Spark

What are some alternatives to Azure Data Factory, Azure Databricks?

Google Analytics

Google Analytics

Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.

Mixpanel

Mixpanel

Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Piwik

Piwik

Matomo (formerly Piwik) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Clicky

Clicky

Clicky Web Analytics gives bloggers and smaller web sites a more personal understanding of their visitors. Clicky has various features that helps stand it apart from the competition specifically Spy and RSS feeds that allow web site owners to get live information about their visitors.

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