Analytics on AWS
10,000+
3X
faster with Amazon EMR than standard Apache Spark
50%
70%
3 PB
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
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Analytics & data warehousing
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Data movement
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Data lake
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Predictive analytics & machine learning
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Analytics & data warehousing
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Analytics & data warehousing
AWS provides the broadest and most cost-effective set of analytics services to help you gain insights faster from all your data.
Broadest selection of analytics services
Each analytics service is purpose-built for a wide range of analytics use cases such as interactive analysis, big data processing, data warehousing, real-time analytics, operational analytics, dashboards, and visualizations.
Services
Beyond all of the certifications and best practices you would expect from AWS, we also have security features designed to help you stay compliant with your best practices and industry regulations.
Price-performant
AWS is committed to providing the best performance at the lowest cost across all analytics services, and we are continually innovating to improve the price performance of our services.
Related AWS services
Resources
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Data movement
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Data movement
AWS makes it easy for you to combine, move, and replicate data across multiple data stores and your data lake.
Ease of use
AWS allows you to easily move data between the data lake and purpose-built data services. For example, AWS Glue is a serverless data integration service that makes it easy to prepare data for analytics, machine learning, and application development.
Faster data integration
AWS gives you the ability to query data across different data sources such as databases, data lakes, and data warehouses. For example, Amazon Athena enables you to use SQL to query a data lake and federated query lets you query live data from relational databases.
Ease of movement
With data stored in a number of different systems, AWS allows you to easily move that data between all of your services and data stores: inside out, outside in, and around the perimeter.
Related AWS services
Resources
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Data lake
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Data lake
Tens of thousands of customers run their data lakes on AWS.
Scalable
Collect, store, organize, and analyze data from multiple sources and formats and scale it to any size. Use AWS Lake Formation to automate tasks required to set up a data lake while saving time defining data structures, schema, and transformations.
Flexible
Easily ingest data in a variety of ways, including leveraging Amazon Kinesis, AWS Import/Export Snowball, AWS Direct Connect, and more. Store all of your data, regardless of volume or format, using Amazon Simple Storage Service (Amazon S3).
Agile
Deploy the infrastructure you need almost instantly. This means teams can be more productive, easily try new things, and roll out projects sooner.
Related AWS services
Resources
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Predictive analytics & machine learning
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Predictive analytics & machine learning
For predictive analytics use cases, AWS provides a broad set of machine learning services and tools that run on your data lake on AWS.
Deeper and faster insights
AWS analytics services leverage proven machine learning (ML) and natural language capabilities to help you gain deeper and faster insights from your data.
Platform integration
AWS provides built-in integration of ML as part of its purpose-built data stores and analytics services, allowing you to create, train, and deploy ML models using familiar languages like SQL.
Experience
AWS is committed to providing the best performance at the lowest cost across all analytics services and we are continually innovating to improve the price-performance of our services.
Related AWS services
Resources
Customers
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Moderna
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Read the case studyModerna 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.
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Invista
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Read the case studyINVISTA 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.
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Intuit
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Watch the videoIntuit 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.
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Pinterest
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Read the case studyPinterest 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.
"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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