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 reinvent their business with data. From data movement, data storage, data lakes, big data analytics, machine learning, and anything in between, AWS offers purpose-built services that provide the best price-performance, scalability, and lowest cost.
Data stored at any scale
AWS analytics services are built to handle large amounts of data at scale and automate a lot of manual and time-consuming tasks. AWS-powered data lakes, supported by S3’s unmatched availability, can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot.
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 get insights from your data, using the most appropriate tool for the job, and are 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 goes beyond standard security functionality like encryption and access control to pro-active monitoring and unified management of security policies. Customers can centrally define and manage security, governance, and auditing policies to satisfy regulations specific to their industry and geography.
ML integration
AWS provides built-in integration of ML as part of its purpose-built analytics services. You can build, train, and deploy ML models quickly with Amazon SageMaker—a fully managed service that provides the tools required for every step of the ML development lifecycle in one integrated environment.

10,000+

data lakes run on AWS

3X

faster with Amazon EMR than standard Apache Spark

50%

less expensive than other cloud data warehouses

70%

savings on storage cost for data in data lakes

3 PB

of data storage in a single cluster with Amazon Elasticsearch Service

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 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 & machine learning

Customers

  • data_sol_page_customer_logo_moderna
  • 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 S3.

    Read the case study 
  • Invista
  • Invista case study
    Nielsen

    INVISTA migrated from siloed data to a data lake on AWS—building a modern data architecture with AWS analytics services to unlock the potential of the digital plant, use data to remove manual processes, and transform their manufacturing workstreams. The company saved more than $2 million/year and has created $300 million of value from companywide data.

    Read the case study 
  • Intuit
  • Intuit customer video
    data_sol_page_customer_logo_intuit

    Intuit migrated to a solution based on Amazon Redshift. It scales to more than 7X the data volume with zero effort and delivers 20X the performance, which has led to a 90 percent reduction in time to insight and a 66 percent reduction in cost.

    Watch the video 
  • Pinterest
  • Pinterest case study
    data_sol_page_customer_logo_pinterest

    Pinterest scaled daily log search and analytics to 1.7TB and reduced cost by 30% by moving to managed analytics using Amazon Elasticsearch service. This enabled the company to scale its log-analysis capabilities and reduce operational burdens, improve security, and save costs.

    Read the case study 
JD-Power_Logo_@1x

"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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