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Migrate Databricks to Amazon EMR: Enterprise Data Science on AWS

May 8, 2024 by Bal Heroor

Imagine this: you're drowning in data—petabytes of information crash against your servers daily, a treasure trove of insights waiting to be unearthed. But the sheer volume is overwhelming. How can you transform this data into actionable intelligence and drive business results?

This is where data science comes in. A recent McKinsey Global Institute report states that organizations leveraging data science have seen a 10-to-40% increase in productivity across various departments! Databricks, a popular data science platform, has been a game-changer, helping businesses unlock the secrets hidden within their big data.

But what if there's a way to optimize costs, gain even greater control, and seamlessly integrate with the vast AWS ecosystem? Enter Amazon EMR. Migrating your data science platform from Databricks to EMR can be a strategic move for enterprises seeking these advantages.

Why Make the Move to EMR?

Databricks offers a user-friendly managed environment, but EMR unlocks several benefits specifically designed for enterprise data science workloads:

  • Cost Savings: EMR's pay-as-you-go model means you only pay for the resources you use, unlike Databricks' consumption-based pricing. Every dollar saved goes straight back into your bottom line.
  • Scalability: EMR gives you fine-grained control over the underlying infrastructure. Choose the perfect EC2 instance types, configure security settings with laser precision, and manage cluster configurations down to the nitty-gritty. This allows you to scale compute resources based on your needs and optimize performance for specific data science tasks.
  • Integration with AWS Services: Migrating to EMR unlocks a world of seamless integration if you are already invested in the AWS ecosystem. You can store your data efficiently in S3, deploy machine learning models with SageMaker, and utilize Kinesis for real-time data pipelines – all within a unified AWS environment. This streamlines your data workflows and fosters a cohesive data science platform.
  • State-of-the-Art Security: Data security is paramount. While Databricks offers data governance features, EMR offers IAM roles for granular access control and integrates with top-tier AWS security services like CloudTrail and Amazon GuardDuty. This is especially crucial for organizations with strict data compliance requirements.
  • Open-Source Freedom: EMR embraces Apache Spark at its core, allowing the freedom to integrate custom tools and libraries within the open-source ecosystem. This is great for organizations that already use open-source tools and want better customization.

Migrating Your Data Science Platform: A Step-by-Step Guide

Migrating a complex data science platform requires careful planning. Here's a roadmap to get you started:

  • Pre-Migration Assessment: Before diving in, look at your current Databricks workloads. Analyze resource consumption patterns (CPU, memory) to understand your resource needs on EMR. Additionally, compare Databricks costs with estimated EMR expenses based on your chosen cluster types and AWS service charges.
  • Data and Model Onboarding: You must move your data and machine learning models. Choose a suitable AWS storage solution like S3 to store your data while maintaining compatibility with EMR workflows. You can utilize the AWS Glue Data Catalog to organize and manage all this metadata for efficient access within EMR. Moreover, you can use the Amazon SageMaker Model to manage and track your machine-learning models.
  • Code Conversion and Translation: Databricks notebooks might need adjustments to run smoothly on EMR. You can bridge the gap in the following ways:
    • Third-party conversion tools can assess your code for EMR compatibility.
    • Open-source libraries can handle basic code conversion tasks.
    • A manual review might be necessary to address Databricks-specific functionalities and ensure library compatibility.
    • For your MLOps pipelines, optimize code for EMR by ensuring efficient model serialization/deserialization. Consider containerization with Docker for added portability.


EMR Cluster Configuration

Consider the following steps to configure EMR clusters:

  • Cluster Sizing: Choose the right EC2 instance types based on your workload requirements. Consider GPU instances for computationally intensive tasks. Remember, EMR allows you to scale up or down your cluster size to meet fluctuating needs.
  • Software Configuration: Specify the Spark version that is compatible with your codebase and libraries. You can also configure additional libraries for your specific data science tasks.
  • Security and Advanced Configurations: Define IAM roles for secure access to S3 and EMR resources. Set up logging and monitoring for EMR jobs and cluster health using Amazon CloudWatch. Use spot instances for cost optimization, but be aware of potential interruptions.


Challenges and Best Practices for a Smooth Migration

Migrating your data science platform requires careful planning and execution. Here are some potential challenges and best practices to ensure a smooth transition:


  • Code Compatibility: There might be some differences between Databricks notebooks and EMR syntax or libraries. Utilize the conversion techniques mentioned earlier and plan for potential manual adjustments.
  • Operational Overhead: EMR requires more manual effort to manage clusters than Databricks' managed environment. Evaluate your team's expertise in EMR and AWS to determine if additional training or resources might be necessary.
  • Adapting to a New Platform: A new platform means new ways of doing things. Encourage open communication and collaboration between your data science and engineering teams throughout migration.


Best Practices for Success

  • Pilot Migration: Conduct a pilot migration with a small workload to test the feasibility of the process and identify potential issues before migrating your entire platform.
  • Clear Communication: Establish communication channels between data science, engineering, and IT teams throughout the migration journey.
  • Leverage Resources: AWS offers extensive documentation and support resources for EMR and related services. Don't hesitate to utilize these resources for troubleshooting and guidance.


Migrating your data science platform to Amazon EMR can unlock significant benefits for your enterprise. Cost optimization, scalability, deeper AWS integration, improved security, and open-source flexibility contribute to a more efficient and robust data science environment.

However, migrating a complex data science platform requires expertise and careful planning. While this blog provides a roadmap, partnering with Mactores can make all the difference. Our data engineers possess in-depth knowledge of EMR, the broader AWS ecosystem, and the intricacies of data science workflows.

Our team of experienced professionals can guide you through every step of the migration process, ensuring a smooth transition and maximizing the potential of EMR for your data science initiatives.

Contact us today to discuss your data science platform migration and unlock the full power of the AWS cloud for your organization.  


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