Azure Data Factory vs Azure HDInsight

Overview

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Azure HDInsight

Stacks29

Followers138

Votes0

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

1. Data Transformation: Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and transformation. It provides a visual interface to design, build, and deploy data pipelines. On the other hand, Azure HDInsight is a fully managed cloud service that makes it easy to process big data using popular open-source frameworks such as Hadoop, Spark, and Hive. It provides scalable processing power for big data analytics, but it does not have built-in data transformation capabilities like ADF.
2. Built-in Connectors: ADF has a wide range of built-in connectors that allow you to connect to various data sources and sinks, such as Azure Blob Storage, Azure Data Lake Storage, SQL Server, and Oracle. It also supports integration with other Azure services like Azure SQL Database and Azure Synapse Analytics. On the contrary, HDInsight supports connectors for various data sources and sinks as well, but it is primarily designed for processing big data using open-source frameworks. It may require additional configuration and development effort to connect with non-Hadoop data sources like Azure Blob Storage or SQL Server.
3. Data Processing: ADF supports both batch and real-time data processing. It allows you to schedule and orchestrate the execution of data pipelines for batch processing, and it also provides integration with Azure Stream Analytics for real-time data processing. In contrast, HDInsight is optimized for batch processing of big data. It provides scalable processing power for executing complex data processing tasks in parallel using distributed computing frameworks like Hadoop and Spark. Real-time data processing capabilities are limited in HDInsight compared to ADF.
4. Monitoring and Management: ADF provides built-in monitoring and management capabilities that allow you to monitor the execution of data pipelines, track data lineage, and manage access control. It also integrates with Azure Monitor and Azure Log Analytics for advanced monitoring and diagnostic capabilities. On the other hand, HDInsight offers a comprehensive monitoring and management experience that includes cluster management, job monitoring, and logging. It provides integration with Azure Monitor, Azure Log Analytics, and Azure Diagnostic Logs for analyzing cluster performance, troubleshooting issues, and monitoring job progress.
5. Cost Structure: ADF follows a pay-as-you-go pricing model, where you pay for the data movement and transformation activities that you perform. The cost is based on the number of activities executed and the volume of data processed. In contrast, HDInsight has a different cost structure that is based on the size and type of the cluster deployed. You pay for the virtual machines and storage resources used by the cluster, as well as any additional Azure services integrated with HDInsight.
6. Scalability: ADF provides automatic scaling capabilities that allow you to scale up or down the execution of data pipelines based on demand. You can configure auto-scaling rules to adjust the number of parallel activities executed based on factors like data volume or time of day. On the other hand, HDInsight provides scalable processing power for big data analytics. You can easily scale the number of virtual machines in the cluster to handle large data volumes or high computational requirements.

In Summary, Azure Data Factory (ADF) provides data transformation capabilities, built-in connectors, support for both batch and real-time data processing, monitoring and management features, a pay-as-you-go cost structure, and automatic scaling capabilities. In contrast, Azure HDInsight is optimized for batch processing of big data using open-source frameworks, supports connectors for various data sources and sinks, offers comprehensive monitoring and management capabilities, has a different cost structure based on cluster size and type, and provides scalable processing power for big data analytics.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Azure Data Factory, Azure HDInsight

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

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?

269k views269k

Comments

Detailed Comparison

Azure Data Factory	Azure HDInsight
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.	It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises
Statistics
GitHub Stars 516	GitHub Stars -
GitHub Forks 610	GitHub Forks -
Stacks 253	Stacks 29
Followers 484	Followers 138
Votes 0	Votes 0
Integrations
Octotree Java .NET	IntelliJ IDEA Apache Spark Kafka Visual Studio Code Hadoop Apache Storm HBase Apache Hive Azure Active Directory

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

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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 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

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 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.

Related Comparisons

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

1. Data Transformation: Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and transformation. It provides a visual interface to design, build, and deploy data pipelines. On the other hand, Azure HDInsight is a fully managed cloud service that makes it easy to process big data using popular open-source frameworks such as Hadoop, Spark, and Hive. It provides scalable processing power for big data analytics, but it does not have built-in data transformation capabilities like ADF.
2. Built-in Connectors: ADF has a wide range of built-in connectors that allow you to connect to various data sources and sinks, such as Azure Blob Storage, Azure Data Lake Storage, SQL Server, and Oracle. It also supports integration with other Azure services like Azure SQL Database and Azure Synapse Analytics. On the contrary, HDInsight supports connectors for various data sources and sinks as well, but it is primarily designed for processing big data using open-source frameworks. It may require additional configuration and development effort to connect with non-Hadoop data sources like Azure Blob Storage or SQL Server.
3. Data Processing: ADF supports both batch and real-time data processing. It allows you to schedule and orchestrate the execution of data pipelines for batch processing, and it also provides integration with Azure Stream Analytics for real-time data processing. In contrast, HDInsight is optimized for batch processing of big data. It provides scalable processing power for executing complex data processing tasks in parallel using distributed computing frameworks like Hadoop and Spark. Real-time data processing capabilities are limited in HDInsight compared to ADF.
4. Monitoring and Management: ADF provides built-in monitoring and management capabilities that allow you to monitor the execution of data pipelines, track data lineage, and manage access control. It also integrates with Azure Monitor and Azure Log Analytics for advanced monitoring and diagnostic capabilities. On the other hand, HDInsight offers a comprehensive monitoring and management experience that includes cluster management, job monitoring, and logging. It provides integration with Azure Monitor, Azure Log Analytics, and Azure Diagnostic Logs for analyzing cluster performance, troubleshooting issues, and monitoring job progress.
5. Cost Structure: ADF follows a pay-as-you-go pricing model, where you pay for the data movement and transformation activities that you perform. The cost is based on the number of activities executed and the volume of data processed. In contrast, HDInsight has a different cost structure that is based on the size and type of the cluster deployed. You pay for the virtual machines and storage resources used by the cluster, as well as any additional Azure services integrated with HDInsight.
6. Scalability: ADF provides automatic scaling capabilities that allow you to scale up or down the execution of data pipelines based on demand. You can configure auto-scaling rules to adjust the number of parallel activities executed based on factors like data volume or time of day. On the other hand, HDInsight provides scalable processing power for big data analytics. You can easily scale the number of virtual machines in the cluster to handle large data volumes or high computational requirements.

Azure Data Factory vs Azure HDInsight

Overview