Analytics on AWS

Fastest way to get answers from all your data to all your users
AWS provides the broadest selection of analytics services that fit all your data analytics needs and enables organizations of all sizes and industries to reinvent their business with data. From data movement, data storage, data lakes, big data analytics, and machine learning (ML) to anything in between, AWS offers purpose-built services that provide the best price performance, scalability, and lowest cost.
Store data at any scale
AWS analytics services are built to handle large amounts of data at scale and automate many manual and time-consuming tasks. AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. Use AWS analytics services to gain deeper insights than with traditional data silos and data warehouses.
Purpose-built for performance and cost
AWS is the fastest and most cost-effective place to store and analyze data. AWS analytics tools are purpose-built to help you quickly extract data insight using the most appropriate tool for the job, and optimized to give you the best performance, scale, and cost for your needs.
Unified data access, security, and governance
AWS provides a comprehensive set of tools that go beyond standard security functionality, like encryption and access control, to offer unified security policy management and proactive monitoring. Centrally define and manage your security, governance, and auditing policies to satisfy industry- and geography-specific regulations.
Machine learning integration
AWS offers built-in ML integration as part of our purpose-built analytics services. You can build, train, and deploy ML models quickly with Amazon SageMaker—a fully managed service that provides tools for every step of the ML development lifecycle in one integrated environment.
AWS Analytics - Modern Data Strategy (2:15)


data lakes run on AWS


faster with Amazon EMR than standard Apache Spark


less expensive than other cloud data warehouses


savings on storage cost for data in data lakes

3 PB

of data storage in a single cluster with Amazon OpenSearch Service (successor to Amazon Elasticsearch Service)

AWS analytics services

Data warehousing, interactive analytics, big data processing, operational analytics, dashboards, and visualizations

Real-time data movement

Data lake: Object storage, backup and archive, data catalog, and third-party data

Platform services, frameworks, and interfaces

AWS analytics services

Category Use cases AWS service
Analytics Interactive analytics Amazon Athena
Big data processing Amazon EMR
Data warehousing Amazon Redshift
Real-time analytics Amazon Kinesis Data Analytics
Operational analytics Amazon OpenSearch Service (successor to Amazon Elasticsearch Service)
Dashboards and visualizations Amazon QuickSight
Visual data preparation Amazon Glue DataBrew
Data movement Real-time data movement Amazon Managed Streaming for Apache Kafka (Amazon MSK) | Amazon Kinesis Data Streams | Amazon Kinesis Data Firehose | Amazon Kinesis Video Streams | AWS Glue
Data lake Object storage Amazon S3 | AWS Lake Formation
Backup and archive Amazon S3 Glacier | AWS Backup
Data catalog
AWS Glue | AWS Lake Formation
Third-party data AWS Data Exchange
Predictive Analytics and Machine Learning Frameworks and interfaces AWS Deep Learning AMIs
Platform services Amazon SageMaker

Use cases

  • Analytics & data warehousing
  • Data movement
  • Data lake
  • Predictive analytics & ML


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  • data_sol_page_customer_logo_invista
  • data_sol_page_customer_logo_intuit
  • data_sol_page_customer_logo_pinterest
  • Moderna
  • Moderna case study
    BMW Group

    Moderna runs all its SAP S/4HANA workloads on AWS, including manufacturing, accounting, and inventory management, which enables the company to achieve greater efficiency and visibility across its operations. Moderna uses Amazon Redshift as a central repository for all the data it captures and stores backups in Amazon S3.

    Read the case study 
  • Invista
  • Invista case study

    INVISTA migrated from siloed data to a data lake on AWS. The company built a modern data architecture with AWS analytics services to transform their manufacturing workstream, use data to remove manual processes, and unlock the potential of its digital plant. INVISTA saved more than $2 million per year and has created $300 million in value from company-wide data.

    Read the case study 
  • Intuit
  • Intuit customer video

    Intuit migrated to an Amazon Redshift-based solution that scales to more than 7X the data volume with zero effort and delivers 20X performance over the company's previous solution. This resulted in a 90 percent reduction in time-to-insight, and a 66 percent cost reduction.

    Watch the video 
  • Pinterest
  • Pinterest case study

    Pinterest scaled daily log search and analytics to 1.7 TB and reduced cost by 30 percent by moving to managed analytics using Amazon OpenSearch Service (successor to Amazon Elasticsearch Service). The company scaled its log analysis capabilities to reduce operational burdens, improve security, and reduce costs.

    Read the case study 

"We built a 120TB data lake in Amazon S3, with 1500 different schemes and use AWS analytics services like Glue, Redshift, and Athena extensively. We couldn’t get these insights from a bunch of siloed databases and warehouses - we needed an S3 scale data lake."

- Bernardo Rodriguez
Chief Digital Officer, J.D. Power

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