Azure HDInsight vs Azure Synapse

Overview

Azure HDInsight

Stacks29

Followers138

Votes0

Azure Synapse

Stacks104

Followers230

Votes10

Azure HDInsight vs Azure Synapse: What are the differences?

Azure HDInsight and Azure Synapse are both used for big data processing and analytics. Let's discuss the key differences between them.

Scalability and Performance: Azure HDInsight is a cloud-based big data analytics service that offers Apache Hadoop, Spark, Hive, and other open-source frameworks. It allows for massive scalability, enabling you to process and analyze large volumes of data efficiently. On the other hand, Azure Synapse (formerly known as Azure SQL Data Warehouse) is a fully managed analytics service that combines enterprise data warehousing and big data processing. Synapse is optimized for high performance and can handle large-scale data warehousing workloads and complex analytics queries.
Unified Analytics Platform: Azure Synapse provides a unified platform that integrates big data, SQL-based analytics, and data integration. It offers a single interface for all your data analytics needs, allowing you to seamlessly analyze data stored in various formats and locations. HDInsight, on the other hand, is primarily focused on big data processing and analytics using Apache Hadoop and Spark. While it supports integration with Azure services like Azure Data Lake Storage and Azure Blob Storage, it does not provide the same level of integration with other data sources as Azure Synapse.
Data Warehousing Capabilities: Azure Synapse offers comprehensive data warehousing capabilities, including columnar storage, advanced data compression, and automatic query optimization. It supports the use of familiar SQL-based querying and provides advanced analytics capabilities using Azure SQL Database and Apache Spark. HDInsight, on the other hand, is more focused on distributed processing and analysis of large datasets using open-source frameworks like Hadoop and Spark. While it supports SQL querying through tools like Hive and Phoenix, it may not offer the same level of performance optimization and data warehousing features as Azure Synapse.
Pricing and Billing: Azure HDInsight follows a pay-as-you-go pricing model, where you are billed based on the number and size of the compute and storage resources provisioned. The pricing for HDInsight includes the cost of virtual machines, storage, networking, and any additional components or services you choose. Azure Synapse, on the other hand, follows a different pricing model based on Data Warehouse Units (DWUs). DWUs determine the amount of compute resources allocated to your Synapse workspace, and you are billed accordingly. The pricing for Synapse includes the cost of compute resources, storage, and data transfer.
Integration with Azure Services: Azure Synapse offers seamless integration with various Azure services, enabling you to build end-to-end data pipelines and workflows. It supports integration with Azure Data Factory, Azure Machine Learning, Azure Data Lake Storage, and other Azure services, allowing you to leverage their capabilities within the Synapse workspace. HDInsight also integrates with Azure services like Azure Data Lake Storage, Azure Blob Storage, and Azure Event Hubs, making it easier to ingest and process data from these sources. However, it may not offer the same level of integration with other Azure services as Azure Synapse.
Security and Governance: Both Azure HDInsight and Azure Synapse provide robust security and governance features to protect your data and comply with regulatory requirements. HDInsight supports authentication and authorization using Azure Active Directory, integration with Azure Virtual Network for private network connectivity, and encryption for data at rest and in transit. Azure Synapse offers similar security features, along with additional capabilities like data masking, row-level security, and dynamic data masking. It also provides built-in compliance with industry standards and regulations.

In summary, Azure HDInsight is a scalable big data analytics service that focuses on distributed processing and analysis using open-source frameworks, while Azure Synapse is a unified analytics platform that combines big data processing, SQL-based analytics, and data warehousing. Synapse offers more comprehensive data warehousing and integration capabilities, whereas HDInsight provides extensive support for open-source frameworks. The pricing and billing models are also different for both services.

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Detailed Comparison

Azure HDInsight	Azure Synapse
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.	It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks 29	Stacks 104
Followers 138	Followers 230
Votes 0	Votes 10
Pros & Cons
No community feedback yet	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Concurrency 1 Dictionary Size Limitation - CCI
Integrations
IntelliJ IDEA Apache Spark Kafka Visual Studio Code Hadoop Apache Storm HBase Apache Hive Azure Data Factory Azure Active Directory	No integrations available

What are some alternatives to Azure HDInsight, Azure Synapse?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

Related Comparisons

Azure HDInsight vs Azure Synapse: What are the differences?

Azure HDInsight and Azure Synapse are both used for big data processing and analytics. Let's discuss the key differences between them.

Scalability and Performance: Azure HDInsight is a cloud-based big data analytics service that offers Apache Hadoop, Spark, Hive, and other open-source frameworks. It allows for massive scalability, enabling you to process and analyze large volumes of data efficiently. On the other hand, Azure Synapse (formerly known as Azure SQL Data Warehouse) is a fully managed analytics service that combines enterprise data warehousing and big data processing. Synapse is optimized for high performance and can handle large-scale data warehousing workloads and complex analytics queries.
Unified Analytics Platform: Azure Synapse provides a unified platform that integrates big data, SQL-based analytics, and data integration. It offers a single interface for all your data analytics needs, allowing you to seamlessly analyze data stored in various formats and locations. HDInsight, on the other hand, is primarily focused on big data processing and analytics using Apache Hadoop and Spark. While it supports integration with Azure services like Azure Data Lake Storage and Azure Blob Storage, it does not provide the same level of integration with other data sources as Azure Synapse.
Data Warehousing Capabilities: Azure Synapse offers comprehensive data warehousing capabilities, including columnar storage, advanced data compression, and automatic query optimization. It supports the use of familiar SQL-based querying and provides advanced analytics capabilities using Azure SQL Database and Apache Spark. HDInsight, on the other hand, is more focused on distributed processing and analysis of large datasets using open-source frameworks like Hadoop and Spark. While it supports SQL querying through tools like Hive and Phoenix, it may not offer the same level of performance optimization and data warehousing features as Azure Synapse.
Pricing and Billing: Azure HDInsight follows a pay-as-you-go pricing model, where you are billed based on the number and size of the compute and storage resources provisioned. The pricing for HDInsight includes the cost of virtual machines, storage, networking, and any additional components or services you choose. Azure Synapse, on the other hand, follows a different pricing model based on Data Warehouse Units (DWUs). DWUs determine the amount of compute resources allocated to your Synapse workspace, and you are billed accordingly. The pricing for Synapse includes the cost of compute resources, storage, and data transfer.
Integration with Azure Services: Azure Synapse offers seamless integration with various Azure services, enabling you to build end-to-end data pipelines and workflows. It supports integration with Azure Data Factory, Azure Machine Learning, Azure Data Lake Storage, and other Azure services, allowing you to leverage their capabilities within the Synapse workspace. HDInsight also integrates with Azure services like Azure Data Lake Storage, Azure Blob Storage, and Azure Event Hubs, making it easier to ingest and process data from these sources. However, it may not offer the same level of integration with other Azure services as Azure Synapse.
Security and Governance: Both Azure HDInsight and Azure Synapse provide robust security and governance features to protect your data and comply with regulatory requirements. HDInsight supports authentication and authorization using Azure Active Directory, integration with Azure Virtual Network for private network connectivity, and encryption for data at rest and in transit. Azure Synapse offers similar security features, along with additional capabilities like data masking, row-level security, and dynamic data masking. It also provides built-in compliance with industry standards and regulations.

Azure HDInsight vs Azure Synapse

Overview