Data Lakes and Analytics on AWS

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

Easiest to build data lakes and analytics

Setting up and managing data lakes involves a lot of manual and time-consuming tasks such as loading, transforming, securing, and auditing access to data. AWS Lake Formation automates many of those manual steps and reduces the time required to build a successful data lake from months to days.

Scalable and cost effective

Data volumes are growing exponentially, but your cost to store and analyze that data can’t also grow at those same rates. AWS provides comprehensive tooling to help control the cost of storing and analyzing all of your data at scale, including features like Intelligent Tiering for data storage in S3 and features that help reduce the cost of your compute usage, like auto-scaling and integration with EC2’s Spot instances.

Comprehensive and open

We provide the broadest and deepest portfolio of purpose-built analytics tools so you can quickly get insights from your data using the most appropriate tool for the job. All of our analytics services support open file formats like Apache Parquet so you don’t need to move and transform your data in order to analyze it, but can instead store it once in a standard format and analyze it using whatever tool or technique is most appropriate.

Secure infrastructure for analytics

Securing vast volumes of data is one of the biggest challenges facing most organizations. 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. For example, Amazon Macie helps find sensitive data that was accidentally stored in the wrong place and Amazon Inspector helps spot configuration errors that might lead to data breaches.

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
	Operational analytics	Amazon Elasticsearch Service
	Dashboards and visualizations	Amazon QuickSight
Data movement	Real-time data movement	Amazon Kinesis Data Analytics \| Amazon Kinesis Data Firehose \| Amazon Kinesis Data Streams \| Amazon Kinesis Video Streams
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, without the need for unnecessary data movement.

Try Amazon Redshift »

Big data processing

Quickly and easily process vast amounts data for data engineering, data science development, and collaboration.

Try Amazon EMR »

Real time analytics

Collect, process and analyze streaming data as it arrives in your data lake, and respond in real-time.

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 »

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Additional resources

AWS Data Lab

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

Learn more »

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