Analytics on AWS

Fastest way to get answers from all your data to all your users

Scalable Data Lakes

Tens of thousands of customers run their data lakes on AWS. Setting up and managing data lakes today involves a lot of manual and time-consuming tasks. AWS Lake Formation automates these tasks so you can build and secure your data lake, in days instead of months. For your data lake storage, Amazon S3 is the best place to build a data lake because of its unmatched 11 nine of durability and 99.99% availability; the best security, compliance, and audit capabilities with object level audit logging and access control; the most flexibility with five storage tiers; and the lowest cost with pricing that starts at less than $1 per TB per month.

Purpose-built Analytics Services

AWS gives you the broadest and deepest portfolio of purpose-built analytics services optimized for your unique analytics use cases. These services are all designed to be the best in class, which means you never have to compromise on performance, scale, or cost when using them. For example, Amazon Redshift is 3x faster and at least 50% less expensive than other cloud data warehouses. Spark on Amazon EMR runs 1.7x faster than standard Apache Spark 3.0 and you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions.

Seamless Data Movement

As the data in your data lakes and purpose-built data stores continues to grow, you often times need to be able to easily move a portion of that data from one data store to another. AWS makes it easy for you to combine, move, and replicate data across multiple data stores and your data lake. For example, AWS Glue provides comprehensive data integration capabilities that make it easy to discover, prepare, and combine data for analytics, machine learning, and application development, while Amazon Redshift can easily query data in your S3 data lake. No other analytics provider makes it as easy for you to move your data, at scale, to where you need it the most.

Unified Governance

One of the most important pieces of a modern analytics architecture is the ability for customers to authorize, manage, and audit access to data. This can be challenging, because managing security, access control, and audit trails across all of the data stores in your organization is complex, time-consuming, and error-prone. With capabilities like centralized access control and policies, and column level filtering of data, no other analytics provider gives you the governance capability to manage access to all of your data across your data lake and your purpose-built data stores from a single place.

Performant and Cost-effective

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. In addition to industry-leading price performance for analytics services, S3 intelligent tiering saves customers up to 70% on storage cost for data stored in your data lake, and Amazon EC2 provides access to an industry leading choice of over 200 instance types, up to 100Gbps network bandwidth, and the ability to choose between on-demand, reserved, and spot instances.

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
Predictive Analytics and Machine Learning	Platform services	Amazon SageMaker

Use cases

Data warehousing

Run SQL and complex, analytic queries against structured and unstructured data in your data warehouse and data lake, without the need for unnecessary data movement.

Try Amazon Redshift »

Big data processing

Quickly and easily process vast amounts of data in your data lake or on-premises for data engineering, data science development, and collaboration.

Try Amazon EMR »

Real time analytics

Collect, process, and analyze streaming data, and load data streams directly into your data lakes, data stores, and analytics services so you can respond in real time.

Try Amazon MSK » Try Amazon Kinesis »

Operational analytics

Search, explore, filter, aggregate, and visualize your data in near real time for application monitoring, log analytics, and clickstream analytics.

Try Amazon Elasticsearch Service »

Customers

"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

View all customers »

Additional resources

AWS Data Lab

Create tangible deliverables that accelerate your data and analytics modernization initiatives. AWS Data Lab is a four-day intensive engagement between your team of builders and AWS technical resources.

Learn more »