Need advice about which tool to choose?Ask the StackShare community!

Amazon EMR

+ 1
Azure HDInsight

+ 1
Add tool

Amazon EMR vs Azure HDInsight: What are the differences?

Amazon EMR and Azure HDInsight are two popular cloud-based big data processing platforms. Let's explore the key differences between them.

  1. Pricing and Cost Management: Amazon EMR offers a flexible pricing model, allowing users to pay for the resources they consume on an hourly basis. It provides cost optimization features like instance fleets and spot instances, which can significantly reduce the overall cost. Azure HDInsight follows a similar pricing model, but it offers additional flexibility with options like reserved instances and hybrid benefits that can lead to cost savings. HDInsight also provides a Total Cost of Ownership (TCO) calculator to estimate the cost of running workloads.

  2. Supported Technologies: Amazon EMR supports a wide range of big data tools and frameworks, including Apache Hadoop, Apache Spark, Apache Hive, Apache Pig, and more. It provides a comprehensive ecosystem for big data processing and analytics. Azure HDInsight also supports various open-source big data technologies like Hadoop, Spark, Hive, and Pig. Additionally, HDInsight offers integrations with Microsoft services like Azure Machine Learning and Power BI, providing seamless workflows.

  3. Integration with Ecosystem: Amazon EMR integrates well with other AWS services, such as Amazon S3 for storage, AWS Glue for data preparation, and Amazon Redshift for data warehousing. This integration facilitates easier data movement and processing within the AWS ecosystem. Azure HDInsight is tightly integrated with the Azure ecosystem, allowing seamless integration with services like Azure Data Lake Storage, Azure Data Factory, and Azure SQL Database. The integration enables a unified data pipeline across different Azure services.

  4. Security and Identity Management: Amazon EMR provides robust security features, including encryption at rest and in transit, secure access controls, and integration with other AWS security services like AWS Identity and Access Management (IAM) and AWS Key Management Service (KMS). Azure HDInsight also offers advanced security capabilities, such as encryption, role-based access control (RBAC), and integration with Azure Active Directory (Azure AD) for identity management. It also provides integration with Azure Security Center for threat detection and monitoring.

  5. Ease of Use and Management: Amazon EMR offers an intuitive web-based console for managing clusters, scaling resources, and monitoring performance. It also provides integration with AWS CloudFormation for automated deployment and management. Azure HDInsight provides an easy-to-use web interface and command-line tools for cluster management, scaling, and monitoring. It also offers integration with Azure Resource Manager for infrastructure management and Azure Automation for automated workflows.

  6. Machine Learning Capabilities: Amazon EMR provides integration with Amazon SageMaker, a powerful machine learning platform. This integration enables users to leverage machine learning capabilities for analyzing big data. Azure HDInsight offers integration with Azure Machine Learning, allowing users to build, deploy, and manage machine learning models at scale. The integration provides seamless integration between big data processing and machine learning workflows.

In summary, Amazon EMR, based on Apache Hadoop and other open-source frameworks, is tightly integrated with the AWS ecosystem, offering scalability and flexibility for processing large datasets. Azure HDInsight, on the other hand, is based on the Hortonworks Data Platform (HDP) and offers integration with the Azure platform, providing similar big data processing capabilities with seamless integration with other Azure services.

Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon EMR
Pros of Azure HDInsight
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
  • 4
    Backed by Amazon
  • 3
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Massive data handling
  • 1
    Great support
    Be the first to leave a pro

    Sign up to add or upvote prosMake informed product decisions

    What is Amazon EMR?

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

    What is Azure HDInsight?

    It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention Amazon EMR and Azure HDInsight as a desired skillset
    What companies use Amazon EMR?
    What companies use Azure HDInsight?
    See which teams inside your own company are using Amazon EMR or Azure HDInsight.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Amazon EMR?
    What tools integrate with Azure HDInsight?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    Aug 28 2019 at 3:10AM


    PythonJavaAmazon S3+16
    What are some alternatives to Amazon EMR and Azure HDInsight?
    Amazon EC2
    It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    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.
    Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
    See all alternatives