Traditional data storage and analytic tools can no longer provide the agility and flexibility required to deliver relevant business insights. That’s why many organizations are shifting to a data lake architecture. A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.

A data lake on AWS gives you access to the most complete platform for big data. AWS provides you with secure infrastructure and offers a broad set of scalable, cost-effective services to collect, store, categorize, and analyze your data to get meaningful insights. AWS makes it easy to build and tailor your data lake to your specific data analytic requirements. You can get started using one of the available Quick Starts. A data lake can be used as a source for both structured and unstructured data.

Read Building Big Data Storage solutions (Data Lakes) whitepaper on the ideal storage solution for data lakes, providing breadth and depth of integration with traditional big data analytics tools as well as innovative querying-place analytics tools to eliminate costly and complex extract, transform, and load processes.


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).  


Build virtually any big data application and support any workload regardless of volume, velocity, and variety of data. With 50+ services and hundreds of features added every year, AWS provides everything you need to collect, store, process, analyze, and visualize big data on the cloud.



AWS provides capabilities across facilities, network, software, and business processes to meet the strictest requirements. Environments are continuously audited for certifications such as ISO 27001, FedRAMP, DoD SRG, and PCI DSS.

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Introduction to Big Data on AWS

Amazon Web Services provides a broad range of services to help you build and deploy big data analytics applications quickly and easily. AWS gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.

With 50+ services and hundreds of features added every year, AWS provides everything you need to collect, store, process, analyze, and visualize big data on the cloud.

Read 451 Advisor Whitepaper about the Cloud Based Approach to achieving Business Value from Big Data on the results of in-depth interviews with six organizations about their cloud-based big-data adoption efforts. Learn about the key factors they used to generate actionable business insights from their data and how to get more value from cloud based big data.  

With AWS you can build virtually any big data application. These are just a few examples of how organizations are driving value from big data with AWS

Data warehousing was a discipline that required extensive IT resources but on AWS you get access to elastic resources that can be expanded and reduced as you need them. AWS offers a complete set of services to implement the entire data warehousing workflow from data collection and storage to, processing and visualization. Optimize your query performance and reduce costs by deploying your data warehousing architecture on AWS.

Understanding clickstream buyer behaviour is a requirement of modern e-business and in the AWS cloud it’s made easier than ever before, including the elastic capacity to deals with peaks and troughs in demand – and in real time improve your customer's digital experience and get a better understanding of your website. Collect, process, analyze, and visualize clickstream to easily slice and dice data and gain insights in real-time with AWS

Detecting patterns that are suggestive of fraud requires expensive infrastructure capable of spotting anomalies very quickly. In the AWS Cloud, with infrastructure that is readily available you can turn all data into high-quality predictions by finding and codifying patterns and relationships within it. You can perform fraud detection with no upfront hardware or software investments and pay as you go, so you can start small and scale as your application grows.

With AWS you can build an entire analytics application to power your business. Scale a Hadoop cluster from zero to thousands of servers within just a few minutes, and then turn it off again when you’re done. This means you can process big data workloads in less time and at a lower cost.

Use Amazon Machine Learning to easily add predictive capabilities to your applications. Combine the power of Amazon Kinesis to ingest data from social media or other sources in real time and use Amazon Machine Learning to generate predictions and insights on that data.

Event-driven Extract, Transform, Load (ETL), Use AWS Lambda to perform data transformations - filter, sort, join, aggregate, and more - on new data, and load the transformed datasets into Amazon Redshift for interactive query and analysis making key indicators available to support actionable insights.

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Together we collaborate and developed a variety of resources and technologies for Big Data.


Cloud Computing and Precision Medicine

From the lab to the clinic, precision medicine is increasingly becoming a reality. As the demand for precision medicine grows, learn how AWS and Intel are playing a role in delivering this important new approach to analyzing massive data sets to bring revolutionary treatment to patients.

Read the AWS and Intel whitepaper on how to overcome the Top 5 Big Data Challenges which outlines how other organizations overcame the top big data challenges by building the right fundamentals for a successful big data initiative.