How to Build a Data Lake for Life Science with Amazon EMR?

What is Amazon EMR?

Amazon EMR, previously known as Amazon MapReduce, is a managed cluster platform by AWS. This offering by AWS makes working on Big Data effortless. Workloads already pre-configured with big data workloads can be easily set up with Amazon EC2 instances.

Amazon EMR lets you run big data tools like Apache Spark, Hive, and Presto. These tools are commonly used for analyzing, transforming, and querying massive datasets. They are open-source and powerful, but traditionally hard to manage on your own infrastructure.

With EMR, AWS sets up everything for you, runs it efficiently, and scales the system automatically depending on how much data you're working with.

How Does Amazon EMR Work?

Amazon EMR (Elastic MapReduce) makes it easy and cost-effective to process big data using open-source frameworks like Apache Spark, Hadoop, Hive, Presto, and many others.

Here's how it works, step by step:

Cluster Creation:

• You launch an EMR cluster, a group of EC2 (virtual) machines managed by EMR.
• You choose the number of machines and the software (Spark, Hadoop, etc.) you want installed.
  
  

Data Processing:

• You upload your data (e.g., in Amazon S3).
• Your chosen framework (e.g., Spark) splits the data into chunks and distributes processing tasks across all machines in the cluster.
• Each machine works on part of the problem in parallel.
  
  

Cluster Management:

• EMR automatically manages the cluster starting, stopping, replacing failed nodes, and scaling up or down based on workload.
• You can configure it to save costs by using Spot Instances or shutting down the cluster when the job finishes.
  
  

Results Storage:

• When processing finishes, results are stored back in S3 or in databases like Redshift or RDS, ready for further analysis.

How to Build a Data Lake for Life Science?

A data lake offers a modern solution for providing a centralized, scalable, and cost-effective repository. This repository stores both raw and processed genomic data while enabling advanced analytics and compliance with strict regulatory standards like HIPAA and GDPR.

Here's how to build a data lake for genomic data step by step.

Step 1: Define Data Lake Objectives and Use Cases

Start by identifying your primary goals:

• Do you want to accelerate drug discovery using genomics?
• Integrate genomic data with clinical trial data for personalized medicine?
• Build machine learning models for biomarker discovery?

Define use cases like variant calling pipelines, cohort analysis, or genome-wide association studies (GWAS). Clear objectives help shape architecture and technology choices.

Step 2: Identify and Catalog Data Sources

Genomic data in life sciences comes from diverse sources:

• Raw sequencing data (FASTQ, BAM, CRAM files)
• Variant data (VCF files)
• Reference genomes
• Annotation databases (e.g., ClinVar, dbSNP)
• Clinical trial data
• Electronic health records (EHRs)
• Metadata from sequencing labs

Step 3: Choose Your Cloud Storage Architecture

Most life sciences organizations build data lakes on Amazon S3 for scalability and cost efficiency.

• Store raw genomic files in their native formats.
• Organize data into logical "zones":
  
  
  • Raw Zone → untouched original data
  • Cleansed Zone → standardized, validated data
  • Curated Zone → data ready for analytics and machine learning
• Enable versioning to keep track of data changes over time—a critical requirement for regulated environments.

Step 4: Design Data Ingestion Pipelines

Ingest large genomic datasets efficiently:

• Use AWS DataSync to move on-premises sequencing data to the cloud.
• Implement scheduled ingestion pipelines for real-time or frequent updates (e.g., annotation databases).
• Validate checksums (e.g., MD5) to ensure data integrity during transfers.

Step 5: Implement Metadata and Data Cataloging

Genomic data is meaningless without context. Use a centralized catalog like AWS Glue Data Catalog.

Enrich datasets with metadata:

• Sample ID
• Sequencing platform
• Coverage depth
• Reference genome version
• Clinical or research project linkage
• Data sensitivity tags for compliance

Step 6: Ensure Data Security and Compliance

Life sciences handle sensitive data, requiring strict security and compliance:

• Encrypt data at rest and in transit.
• Use fine-grained access controls (e.g., IAM policies).
• Implement audit logging to track who accessed or modified data.
• Mask or tokenize sensitive identifiers where possible.

• Define data retention policies.
• Document data flows for regulatory audits.
• Use cloud-native security services like AWS Macie for sensitive data detection.

Step 7: Build Data Transformation and ETL Pipelines

Raw genomic data often requires extensive processing:

• Align raw reads to a reference genome.
• Call variants (e.g., using GATK pipelines).
• Annotate variants with public or proprietary databases.
• Convert file formats (e.g., BAM → CRAM, or VCF → Parquet for analytics).

Step 8: Enable Analytics and Machine Learning

Once your curated data is ready, unlock insights through analytics:

• Use AWS Athena to query genomic data stored as Parquet files directly in the data lake.
• Integrate clinical data to run association studies.
• Feed processed genomic datasets into machine learning platforms like Amazon SageMaker for predictive biomarker discovery, patient stratification, and drug response modeling.

Step 9: Implement Data Sharing and Collaboration

Life sciences research is collaborative:

• Provide secure data-sharing zones for external researchers or partners.
• Use Data Lake Federation to enable controlled access without duplicating massive datasets.
• Maintain data governance policies to manage who can access which datasets and for what purposes.
  
  

Step 10: Monitor, Optimize, and Scale

A genomic data lake is never “finished.” Continuously:

• Monitor storage costs and optimize with lifecycle policies (e.g., move cold data to cheaper tiers like S3 Glacier).
• Update pipelines to accommodate new data formats or analytical tools.
• Regularly review compliance and security controls.
• Gather user feedback to improve data discoverability and performance.

Case Study

A leading life sciences company sought to transform how it manages and analyzes genomic data to drive faster research outcomes and unlock discoveries in precision medicine.

Client Overview

The client is a global biotechnology organization focused on developing targeted therapies and personalized treatment plans. Their research teams generate and analyze terabytes of genomic sequencing data alongside clinical trial results and other biomedical datasets.

The Challenge

The company faced significant hurdles in managing its growing volume of genomic data. Sequencing data was scattered across multiple on-premises servers and cloud storage buckets, making it difficult to access, integrate, or analyze efficiently. Data silos slowed collaborative research and complicated compliance with regulatory requirements like HIPAA and GDPR. Processing large datasets for variant calling, cohort analysis, and machine learning requires substantial computing resources and time, delaying insights crucial for research and development.

Our Solution

We designed and implemented a cloud-based genomic data lake on AWS, tailored for life sciences workloads.

• Data Ingestion: Large genomic datasets were migrated from on-premises systems to Amazon S3 using AWS DataSync, ensuring high-speed transfers and data integrity checks via checksums.
• Storage Architecture: Genomic files—including FASTQ, BAM, and VCF—were centralized in Amazon S3. Data was organized into raw, cleansed, and curated zones, with S3 Object Versioning for compliance and reproducibility. Storage costs were optimized using S3 Intelligent-Tiering and S3 Glacier for archival data.
• Data Cataloging & Metadata: We implemented the AWS Glue Data Catalog to catalog datasets, capture rich metadata (sample IDs, sequencing platforms, annotations), and enable fast data discovery across research teams.
• Data Transformation & ETL: Processing pipelines were built using AWS Glue and Amazon EMR (Elastic MapReduce) for scalable Spark-based workflows to:
  • Align raw sequencing reads to reference genomes
  • Call and annotate variants
  • Convert large genomic files to analytics-friendly formats like Parquet 
  • Orchestration of complex genomics workflows was managed with AWS Step Functions to ensure reliable, repeatable processes.
• Security & Compliance: Data at rest was encrypted with AWS KMS, and fine-grained access control was implemented using AWS Lake Formation and IAM policies. Encryption, audit logging, and strict role-based permissions enforced compliance with HIPAA and GDPR. AWS CloudTrail provided complete visibility into data access and operations.
• Analytics & Machine Learning:  Researchers leveraged Amazon Athena for serverless querying of curated Parquet datasets directly in S3. For advanced modeling, processed genomic and clinical data were used in Amazon SageMaker to build machine learning models predicting biomarker significance and patient response to therapies.
• Monitoring & Cost Management: We used AWS CloudWatch to monitor pipeline performance and AWS Cost Explorer to track spending, ensuring cost optimization across workloads.

The Result

Impact on Drug Discovery

The organization significantly accelerated drug discovery workflows with its AWS-based genomic data lake. Variant processing pipelines that once took three days could now complete in under 10 hours, enabling rapid identification of genetic biomarkers linked to disease. Integrated clinical and genomic datasets allowed researchers to build predictive models for patient stratification and therapy response in days rather than weeks.

These improvements reduced time-to-insight for target discovery and trial design, helping the organization advance new drug candidates into preclinical studies several months ahead of schedule. Ultimately, the AWS data lake transformed how research teams collaborate, analyze complex data, and drive innovation in precision medicine.

Partner With Mactores

A well-architected data lake on AWS empowers organizations to centralize disparate genomic and biomedical data, ensure regulatory compliance, and accelerate advanced analytics and machine learning initiatives.

By breaking down data silos and enabling seamless data integration, life sciences companies can uncover hidden insights, shorten drug discovery timelines, and move closer to delivering truly personalized medicine. Building a genomic data lake is not just a technological upgrade—it's a strategic step toward transforming scientific research and improving patient outcomes.

If you want to build a data lake to centralize and analyze your life science data, partner with Mactores.

FAQs

•  Why can't we store genomic data in traditional databases instead of a data lake?

  Traditional relational databases aren't designed to handle the sheer size and complexity of genomic data files like FASTQ, BAM, or VCF, which can be hundreds of gigabytes or terabytes. A data lake on services like Amazon S3 allows you to store raw and processed genomic files cost-effectively and scale to petabytes without performance issues.

• How does a genomic data lake help with regulatory compliance like HIPAA or GDPR?
  A well-designed data lake on AWS incorporates security features like encryption (using AWS KMS), fine-grained access controls (through AWS Lake Formation and IAM), and comprehensive audit logging (with CloudTrail). These tools help ensure that sensitive genomic and clinical data remains secure and that organizations can demonstrate compliance with regulations like HIPAA and GDPR. 

• How can machine learning models benefit from a genomic data lake?

  Absolutely. A genomic data lake centralizes massive volumes of high-quality, curated data, critical for building accurate machine learning models. With AWS services like Amazon SageMaker, researchers can easily train and deploy models to predict disease risks, identify novel biomarkers, or stratify patient populations for clinical trials. The data lake ensures that genomic and clinical data are accessible and integrated, dramatically accelerating precision medicine research.