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Databricks to Amazon EMR: Operationalize Data Science on AWS With MLOPS

Apr 24, 2024 by Bal Heroor

Data science has witnessed a significant shift. Gone are the days of siloed experimentation and analysis. Today, it focuses on building robust, scalable MLOps pipelines seamlessly integrating with existing infrastructure.
Regarding cloud-based big data processing, Databricks and Amazon EMR emerge as prominent contenders. Databricks, with its user-friendly interface and built-in data science functionalities, has become a popular choice for rapid prototyping and experimentation. However, for organizations heavily invested in the AWS ecosystem and seeking granular control over cost and resources, Amazon EMR presents a compelling alternative.

Key Difference between Dataricks and Amazon EMR

Understanding the core distinctions between Databricks and EMR is crucial for making an informed migration decision. Let's explore these key differences:

  • Focus: Databricks is a managed data science platform specifically designed for Apache Spark Workloads. It offers a comprehensive suite of tools for data extraction, analysis, model training and deployment. Databricks offers a user-friendly, web-based interface. EMR, on the other hand, is an AWS service that manages clusters and runs various big data frameworks, including Spark and Hadoop. It allows you to configure your cluster down to the finest details. You can create a highly customized and optimized data environment using Amazon EMR.
  • Ease of Use: Databricks has a user-friendly interface, pre-configured environments, and built-in functionalities like notebooks, visualization tools, and job scheduling. This is ideal to get started quickly and explore data interactively. EMR requires more configuration and orchestration from the user, demanding a deeper understanding of Spark and AWS services.
  • Cost: The pricing structure of Databricks and Amazon EMR differs significantly. Databricks has a pay-per-use model based on workload and cluster size. However, it might be expensive for resource-intensive workloads. EMR offers a better pricing model. You pay only for the underlying compute resources (EC2 instances) and additional EMR service charges. This can be cost-effective for organizations that are already using AWS services.
  • Cloud Agnosticism: While Databricks is cloud-agnostic and can be deployed on AWS, Microsoft Azure, or Google Cloud Platform, EMR only works in the AWS ecosystem. However, this approach helps EMR offer better integration with other AWS services.
  • Data Science Functionality: Databricks boasts a broader range of built-in features designed explicitly for data science and engineering tasks, such as data visualization tools and model deployment functionalities. EMR, while capable of handling Spark workloads, might necessitate adopting additional tools for these functionalities within the AWS ecosystem.


Why Should You Switch to Amazon EMR?

  • Cost Optimization: For organizations already heavily invested in AWS and seeking to optimize considerable data processing costs, EMR's pay-as-you-go structure makes it a compelling option.
    Deep AWS Integration: EMR seamlessly integrates with other AWS services like S3 for storage, SageMaker for model deployment, and CloudWatch for monitoring. This tight integration streamlines data pipelines and simplifies management within the AWS ecosystem.
  • Large-Scale Batch Processing: AWS can scale the clusters efficiently. This leads to an efficient batch processing job for extensive workloads. With large-scale batch processing, you can manage big data effectively.
  • Control and Customization: EMR offers better control over cluster configuration, security settings, networking options, and auto-scaling policies. This empowers organizations to tailor their environment to specific workload requirements.
  • Existing AWS Expertise: Teams with deep expertise in managing AWS services will find EMR a natural fit. The learning curve associated with EMR might be easier for those familiar with AWS functionalities.
  • Open Source Focus: Organizations committed to open-source solutions will appreciate EMR's reliance on open-source technologies like Spark and Hadoop. This avoids vendor lock-in associated with some proprietary platforms.


How do you Migrate From Databricks to Amazon EMR?

Transitioning from Databricks to Amazon EMR necessitates meticulous planning and execution to ensure a smooth and successful migration. This section delves into the intricate steps involved, offering a comprehensive guide for senior data engineers with a deep understanding of platforms and big data concepts.

  • Pre-Migration Assessment
    • Workload Characterization: A thorough examination of your Databricks workloads forms the foundation for a successful migration. This involves profiling your workloads to understand the following:
    • Model Training and Evaluation: Analyze the resource consumption (CPU, memory, storage) patterns of your machine learning training and evaluation jobs running on Databricks. Identify peak usage periods and potential bottlenecks.
    • Feature Engineering and Data Preprocessing: Evaluate the resource requirements of your data preprocessing and feature engineering pipelines within Databricks. This includes understanding the volume and types of data processed (structured, unstructured) and the libraries used.
    • Model Deployment: If your MLOps pipeline involves deploying models on Databricks, assess the dependencies on Databricks-specific deployment functionalities.
    • Databricks Cost Breakdown: Compile your current Databricks billing data to understand your cost structure. This includes charges for Cluster usage for model training and batch inference jobs, data storage (model artifacts, training data), and Databricks-specific features for model deployment.
    • EC2 Instance Costs: Estimate costs for instance types suitable for machine learning workloads. Consider GPU-optimized instances (P-series, G-series) for computationally intensive training tasks or inference workloads with real-time requirements.
    • EMR Service Charges: Account for EMR service charges based on your anticipated cluster usage patterns (on-demand vs. spot instances, cluster size, and running duration).
    • Additional AWS Services: Factor in costs for any additional AWS services you plan to integrate with EMR, such as S3 for storage, Kinesis for real-time data ingestion, or Step Functions for job scheduling.
  • Data Migration Strategy
    • Storage Compatibility: Select a suitable storage solution on AWS for your data. A common choice is Amazon S3, which offers scalability, durability, and cost-effectiveness for big data storage. Ensure compatibility with downstream processing tools in your EMR MLOps pipeline.
    • Metadata Management: Utilize the AWS Glue Data Catalog to manage metadata for your training data. Glue Data Catalog is a centralized repository for registering your data in S3, enabling efficient discovery and access within your EMR environment.
    • Data Governance with AWS Lake Formation: For organizations with stringent data governance requirements, consider leveraging AWS Lake Formation. Lake Formation offers features like data lineage tracking, access control, and continuous monitoring to ensure data quality and security throughout the migration process and within your migrated MLOps pipeline on EMR.
  • Code Conversion and Translation
    • Databricks Notebooks to EMR Notebooks: While EMR supports Spark notebooks, syntax or library compatibility issues might exist. Here are some strategies to address these:
    • Third-Party Conversion Tools: Explore AWS Marketplace offerings that can assess and potentially convert your Databricks notebooks for EMR compatibility. These tools can automate some of the conversion tasks, reducing manual effort.
    • Open-Source Libraries: Utilize open-source libraries for basic code conversion, particularly for common data preprocessing and machine learning functionalities.
    • Manual Code Review and Optimization: Review your Spark code for EMR compatibility. Pay close attention to areas like:
    • Library Usage: Ensure machine learning libraries and big data frameworks used in your notebooks are compatible with the version of Spark you plan to use on EMR.
    • Databricks Functionalities: Identify codes that rely on built-in Databricks functionalities. This includes visualization libraries or deployment models. You can use tools like Apache Zeppelin for data virtualization and AWS SageMaker for deployment models. AWS SageMaker will help you track, compare, and visualize training models within your MLOps pipelines.
    • MLOps Pipelines Optimization for EMR: Optimize your MLOps pipeline for EMR once compatibility issues are addressed. These techniques involve:
    • Model Serialization and Deserialization: Ensure your models can be efficiently serialized and deserialized across different environments (Databricks vs. EMR) to facilitate seamless loading and deployment within your EMR workflows. Popular formats include PMML (Portable Model Markup Language) or joblib.
    • Containerization: Consider containerizing your MLOps pipeline stages (data preprocessing, training, deployment) using Docker containers. This promotes portability and consistency across different environments, including Databricks and EMR.
  • EMR Cluster Configuration
    • Cluster Sizing: Determine the optimal EMR cluster configuration for your MLOps pipelines using the insights from the pre-migration assessment. Consider factors like:
    • Instance Types: Choose GPU-optimized instance types (P-series, G-series) with sufficient memory capacity to handle your training data and model complexity for computationally intensive training tasks. Consider these same instance types for batch inference workloads with real-time requirements or explore options with high CPU throughput (C-series).
    • Number of Nodes: Scale your EMR cluster based on the parallelism required for distributed training jobs or batch inference pipelines. Utilize auto-scaling to adjust the cluster size dynamically based on workload demands.
    • Storage Options: Choose the most suitable storage options for your EMR cluster:
    • EBS (Elastic Block Store): Utilize EBS volumes for temporary data storage, such as intermediate results or shuffle files generated during Spark job execution. EBS volumes offer different performance tiers (HDD/SSD) based on your access needs.
    • S3 (Simple Storage Service): Leverage S3 as a cost-effective and scalable solution for storing your persistent data (input data, processed output). S3 offers various storage classes (Standard, Glacier, etc.) optimized for different access patterns and cost considerations.
    • Software Configuration: Specify the Spark version you intend to use on EMR. EMR supports a range of Spark versions. Choose a version that balances compatibility with your codebase and access to the latest features and bug fixes. Additionally, configure any additional libraries or frameworks needed for your workloads beyond the core Spark libraries.
    • Security and Advanced Configurations:  Define EMR configurations for:
    • Security (IAM Roles): Configure IAM roles to manage access to S3 buckets containing training data, model artifacts, and EMR cluster resources. This ensures secure access to your MLOps workflows.
    • Logging and Monitoring: Set up configuration to track cluster activity, including job execution logs, application logs, and resource utilization metrics. Utilize services like Amazon CloudWatch for centralized logging and monitoring of your EMR cluster and MLOps pipeline execution.
    • Spot Instances for Cost Optimization: Consider using spot instances for EMR clusters, particularly for non-critical training jobs or batch inference tasks. Spot instances offer significant cost savings compared to on-demand instances but come with the possibility of interruption.
  •  Model Deployment and Serving on EMR
    • Leveraging Amazon SageMaker: Integrate Amazon SageMaker for model deployment and serving within your EMR-based MLOps pipeline. SageMaker offers a managed service for building, training, and deploying machine learning models at scale.
    • Model Packaging: Utilize SageMaker for model packaging, creating a containerized representation of your trained model that is ready for deployment.
    • Hosting Options: Choose a deployment option within SageMaker that aligns with your needs:
      Real-time Inference: Deploy your model to SageMaker endpoints for low-latency, real-time predictions.
      Batch Transform Jobs: Utilize SageMaker batch transform jobs for offline predictions on large datasets.


Transitioning from Databricks to Amazon EMR presents many advantages yet demands a deep understanding of AWS services. Navigating this transition seamlessly necessitates a skilled team capable of managing pivotal tasks during and after migration.

Enter Mactores, a premier player in the realm of digital transformation. Our seasoned experts are primed to assist you in meticulously planning, strategizing, and optimizing your EMR platform.

Are you curious about how Mactores can facilitate your transition? Reach out to us today!


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