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Amazon EMR vs Databricks: What are the differences?


In this article, we will explore the key differences between Amazon EMR and Databricks. Both Amazon EMR and Databricks are popular big data processing platforms, but they have some distinct features and capabilities that differentiate them. Understanding these differences can help in making an informed choice when selecting a platform for your big data processing needs.

  1. Scalability and Managed Services: Amazon EMR is a fully managed cluster platform that allows you to easily provision, scale, and manage a cluster of compute resources for big data processing. It provides automatic scaling capabilities to handle variable workloads efficiently. Databricks, on the other hand, is a unified analytics platform that offers a managed service for big data and machine learning. It provides auto-scaling clusters and a serverless architecture for optimized resource utilization.

  2. Integration with Cloud Services: Amazon EMR integrates seamlessly with other AWS services, such as Amazon S3 for data storage, Amazon Redshift for data warehousing, and Amazon Kinesis for real-time streaming data processing. It leverages the full range of AWS services to build end-to-end big data pipelines. Databricks also offers integrations with various cloud services, including Azure Blob Storage, Azure Data Lake Storage, and Azure Event Hub. It is tightly integrated with the Azure ecosystem and provides native integration with Azure services.

  3. Notebook Environment: Databricks provides a collaborative notebook environment that allows data scientists and engineers to create and execute code, visualize data, and share insights. It offers built-in support for popular programming languages like Python, R, and Scala. Amazon EMR, on the other hand, doesn't have a native notebook environment. It supports popular open-source tools like Apache Zeppelin, Jupyter Notebook, and RStudio, which can be installed on the cluster for interactive data analysis.

  4. Machine Learning Capabilities: Databricks has strong native integration with popular machine learning frameworks like Apache Spark MLlib and TensorFlow. It provides a comprehensive machine learning library and tools for building and deploying machine learning models at scale. Amazon EMR also supports machine learning frameworks like Apache Spark and TensorFlow, but it doesn't have the same level of native integration and built-in tools as Databricks.

  5. Enterprise Features and Security: Databricks offers advanced enterprise features like fine-grained access controls, data encryption at rest and in transit, and integration with Active Directory for user authentication. It provides a robust security infrastructure to ensure data privacy and compliance. Amazon EMR also provides enterprise features like encryption, fine-grained access controls, and integration with AWS Identity and Access Management (IAM). However, Databricks offers a more comprehensive set of security features tailored specifically for big data processing.

  6. Pricing and Cost Management: Amazon EMR offers a flexible pricing model based on the EC2 instances and storage resources used by the cluster. It provides options for on-demand instances, reserved instances, and spot instances to optimize cost. Databricks pricing is based on the DataBricks Units (DBUs), which combines compute and storage resources. It offers different pricing tiers based on the usage patterns and requirements. Databricks provides cost management tools and optimization recommendations to help reduce overall costs.

In summary, Amazon EMR is a fully managed big data processing platform with strong integration with AWS services, while Databricks is a unified analytics platform with a focus on collaboration and machine learning. Databricks provides a richer set of native tools and capabilities, but Amazon EMR offers more flexibility and integration options with the AWS ecosystem. Ultimately, the choice between Amazon EMR and Databricks depends on specific requirements, skillsets, and preferences for cloud platform providers.

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Pros of Amazon EMR
Pros of Databricks
  • 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
  • 1
    Best Performances on large datasets
  • 1
    True lakehouse architecture
  • 1
  • 1
    Databricks doesn't get access to your data
  • 1
    Usage Based Billing
  • 1
  • 1
    Data stays in your cloud account
  • 1

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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 Databricks?

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.

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Blog Posts

Aug 28 2019 at 3:10AM


PythonJavaAmazon S3+16
What are some alternatives to Amazon EMR and Databricks?
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.
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.
See all alternatives