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Databricks to Amazon EMR: Build Serverless Enterprise Data Science Platform on AWS

Apr 26, 2024 by Nandan Umarji

Data's ever-growing volume and complexity necessitate robust and scalable solutions for data science workflows. Serverless data science platforms built on cloud infrastructure giants like Amazon Web Services (AWS) offer a compelling approach to address this challenge. These platforms eliminate the burden of managing and provisioning physical servers, allowing data science teams to focus on core competencies like analytics, modeling, and insights generation.

This blog post guides CIOs, CTOs, CDOs, and data science/engineering professionals in navigating the process of building a serverless data science platform on AWS. We'll delve into the two leading options for such platforms - Databricks and Amazon EMR - providing a comparative analysis to help you make an informed decision. Additionally, the blog offers a step-by-step tutorial, practical insights for optimizing performance and cost, and best practices for leveraging AWS for your data science initiatives.

Databricks vs. Amazon EMR: A Comparative Analysis


Databricks is a managed service specifically designed for big data and machine learning workloads. It offers a unified environment encompassing data warehousing, data processing, and advanced analytics capabilities. Key features of Databricks include:

  • Collaborative Notebooks: Databricks provides interactive notebooks that support popular programming languages like Python, R, and Scala. These notebooks facilitate collaborative data exploration, analysis, and visualization, enabling seamless teamwork among data scientists and engineers.
  • Auto-scaling Clusters: Databricks automatically provisions and scales computing resources based on workload demands. This eliminates the need for manual cluster management, ensuring optimal resource utilization and cost efficiency.
  • Integration with Cloud Services: Databricks integrates seamlessly with various cloud platforms, including AWS, Azure, and GCP. This allows for data ingestion and storage across diverse sources, offering flexibility and scalability for your data science workflows.

Amazon EMR

Amazon EMR, on the other hand, is a fully managed cluster platform tailored explicitly for big data processing on AWS. It offers a wide range of features, including:

  • Support for Big Data Frameworks: EMR supports popular big data frameworks like Apache Hadoop, Apache Spark, and Presto. This flexibility lets you choose the most suitable framework for your data processing needs.
  • Seamless Integration with AWS Services: EMR integrates tightly with other AWS services like Amazon S3 for storage, Amazon Redshift for data warehousing, and Amazon Kinesis for real-time data processing. This integrated ecosystem allows for building comprehensive data pipelines within the AWS infrastructure.
  • Customizable Cluster Configurations: EMR offers granular control over cluster configurations, enabling you to optimize resource allocation based on specific processing requirements.


Key Considerations for Choosing Between Databricks and EMR

The choice between Databricks and EMR depends on several factors critical to your data science environment:

  • Ease of Use: Databricks, with its managed notebooks and auto-scaling features, offers a more user-friendly experience, particularly for teams with less experience in cluster management.
  • Scalability: Both Databricks and EMR offer auto-scaling capabilities. However, EMR might provide more granular control over cluster configurations for highly customized scaling needs.
  • Cost-Effectiveness: Cost optimization depends on your specific workload patterns. Databricks' auto-scaling can potentially reduce idle resource costs. However, EMR might offer lower upfront costs for certain configurations.
  • Workload Requirements: Consider the specific frameworks and tools your data science team utilizes. EMR provides broader framework support, whereas Databricks might be a better fit if your primary focus is on Apache Spark workloads.
  • Existing Cloud Integrations: If you have a pre-existing cloud environment (e.g., Azure), Databricks' multi-cloud support might be advantageous. However, EMR offers the tightest integration with other AWS services for a fully native AWS experience.


Step-by-Step Tutorial: Setting Up a Serverless Data Science Platform with Amazon EMR on AWS

This tutorial guides you through setting up a serverless data science platform on AWS using Amazon EMR.

  • Prerequisites:
    • An AWS account with administrative privileges
    • Basic understanding of cloud computing concepts
    • Familiarity with data science tools and frameworks (Python, R, Spark)
  • Steps to Follow
    • Sign in and Access the EMR Console
    • Navigate to the AWS Management Console at
    • Sign in using your AWS account credentials
    • Access Amazon EMR Service
    • In the AWS Management Console search bar, type "EMR" and press Enter.
    • Click on "Amazon EMR" from the search results to access the EMR service console.
  • Create or Manage EMR Studios (Optional)

EMR Studios provides a centralized environment for managing EMR applications. If you already have an EMR Studio in the desired AWS Region, skip to step 4.

To create a new EMR Studio:

    • On the EMR console landing page, locate the option "Get started" and click on it.
    • In the "Get Started with Amazon EMR Serverless" window, choose "Create and launch Studio". EMR Serverless will create a new EMR Studio for you.
  • Create an EMR Serverless Application
    • In the EMR console navigation pane, click "EMR Serverless" to access the serverless applications management section.
    •  Click on "Create application" to initiate the application creation process.
  • Configure Your Application
    • The "Create studio" UI opens in a new tab. Here, you'll configure your application details:
    • Application Name: Enter a descriptive name for your application.
    • Type: Choose "Spark" if your primary focus is on Spark workloads. You can select other options like "Hive" based on your needs.
    • Release Version: Select a recent EMR-supported Amazon Machine Image (AMI) version. A version with pre-configured Spark is recommended.
  • Settings
    • Use default settings for batch jobs only: Choose this option if you plan to run only batch data processing jobs.
    • Use default settings for interactive workloads: Select this for interactive data exploration and analysis needs. You can also run batch jobs on these applications.
    • Click "Create Application" to create your EMR Serverless application.
  • Submit a Job Run or Interactive Workload
    • In the EMR Studio "Application details" page, click on "Submit job"
  • Configure your job submission details: 
    • Name: Enter a name for your job run.
    • Runtime role: Choose the IAM role you created with the necessary permissions for EMR to access AWS resources.
    • Script location: Specify the S3 location of your data processing script (e.g., Spark script). Script arguments: Provide any additional arguments required by your script.
    • Spark properties (optional): Edit Spark properties to customize your job execution (e.g., memory allocation for executors).
    • Click "Submit job" to initiate your data processing job.
  • View Application UI and Logs
    • Monitor your job progress in the "Job runs" tab on the application details page.
      Once the job finishes, you can view the application UI (e.g., Spark UI) and logs for details on job execution.
  • Clean Up (Optional)

    EMR Serverless applications automatically stop after 15 minutes of inactivity. However, you can manually release resources:
    • In the "List applications" page, locate your application.
    • Click on "Actions" and choose "Stop" to stop the application.
    • Once stopped, select the application again and choose "Actions" followed by "Delete" to remove it.

Optimizing Performance, Scalability, and Cost

Leverage Serverless Features of AWS and Chosen Service (Databricks or EMR)

A key advantage of serverless architecture is the elimination of manual server provisioning and management. Both Databricks and EMR offer features that contribute to cost optimization:

  • Auto-scaling: Both services offer auto-scaling capabilities to adjust resource allocation based on workload demands. This ensures you're only paying for the resources you utilize.
  • Spot Instances: Consider utilizing AWS Spot Instances within your Databricks clusters or EMR configurations. Spot Instances offer significant cost savings by leveraging unused EC2 capacity, though they can be interrupted. Evaluate your workload tolerance for interruptions before implementing Spot Instances.
  • Serverless Compute Options: AWS offers serverless compute services like AWS Lambda, which can be integrated with your data science workflows for specific tasks. These services eliminate server management overhead and scale automatically based on invocations.

Best Practices for Data Science Workflows on AWS

Here are some best practices to optimize your data science workflows on AWS:

  • Data Partitioning: Partitioning data in storage (like S3) based on logical units (e.g., date, customer ID) improves query performance by allowing data access for specific partitions instead of scanning entire datasets.
  • Caching Mechanisms: Utilize caching mechanisms within your chosen service (Databricks or EMR) to store frequently accessed data in memory, reducing retrieval latency for subsequent operations.
  • Code Optimization: Focus on efficient code practices within your data science scripts. Techniques like vectorization and lazy evaluation in tools like Spark can significantly improve processing pipelines.
  • Monitoring and Logging: Implement monitoring and logging tools to track resource utilization, job performance, and potential errors within your serverless platform. This allows for proactive identification and troubleshooting of issues, ensuring optimal performance.

Conclusion: Conquering DataOps with EMR

Building a serverless data science platform on AWS with either Databricks or EMR offers numerous advantages. These platforms eliminate server management overhead, allowing data science teams to focus on core tasks like data exploration, model building, and generating insights. This blog's comparative analysis and step-by-step guide serve as a starting point for your journey toward building a robust and scalable data science environment on AWS.


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