Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes. This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.


As organizations are collecting and analyzing increasing amounts of data, traditional on-premises solutions for data storage, data management, and analytics can no longer keep pace. Data siloes that aren’t built to work well together make storage consolidation for more comprehensive and efficient analytics difficult. This, in turn, limits an organization’s agility, ability to derive more insights and value from its data, and capability to seamlessly adopt more sophisticated analytics tools and processes as its skills and needs evolve.

A data lake, which is a single platform combining storage, data governance, and analytics, is designed to address these challenges. It’s a centralized, secure, and durable cloud-based storage platform that allows you to ingest and store structured and unstructured data, and transform these raw data assets as needed. You don’t need an innovation-limiting pre-defined schema. You can use a complete portfolio of data exploration, reporting, analytics, machine learning, and visualization tools on the data. A data lake makes data and the optimal analytics tools available to more users, across more lines of business, allowing them to get all of the business insights they need, whenever they need them.

Until recently, the data lake had been more concept than reality. However, Amazon Web Services (AWS) has developed a data lake architecture that allows you to build data lake solutions cost-effectively using Amazon Simple Storage Service (Amazon S3) and other services.

Using the Amazon S3-based data lake architecture capabilities you can do the following:

  • Ingest and store data from a wide variety of sources into a centralized platform.
  • Build a comprehensive data catalog to find and use data assets stored in the data lake.
  • Secure, protect, and manage all of the data stored in the data lake.
  • Use tools and policies to monitor, analyze, and optimize infrastructure and data.
  • Transform raw data assets in place into optimized usable formats.
  • Query data assets in place.
  • Use a broad and deep portfolio of data analytics, data science, machine learning, and visualization tools.
  • Quickly integrate current and future third-party data-processing tools.
  • Easily and securely share processed datasets and results.

The remainder of this paper provides more information about each of these capabilities. Figure 1 illustrates a sample AWS data lake platform.

Sample AWS data lake platform
Figure 1: Sample AWS data lake platform

Amazon S3 as the Data Lake Storage Platform

The Amazon S3-based data lake solution uses Amazon S3 as its primary storage platform. Amazon S3 provides an optimal foundation for a data lake because of its virtually unlimited scalability. You can seamlessly and nondisruptively increase storage from gigabytes to petabytes of content, paying only for what you use. Amazon S3 is designed to provide 99.999999999% durability. It has scalable performance, ease-of-use features, and native encryption and access control capabilities. Amazon S3 integrates with a broad portfolio of AWS and third-party ISV data processing tools.

Key data lake-enabling features of Amazon S3 include the following:

  • Decoupling of storage from compute and data processing. In traditional Hadoop and data warehouse solutions, storage and compute are tightly coupled, making it difficult to optimize costs and data processing workflows. With Amazon S3, you can cost-effectively store all data types in their native formats. You can then launch as many or as few virtual servers as you need using Amazon Elastic Compute Cloud (EC2), and you can use AWS analytics tools to process your data. You can optimize your EC2 instances to provide the right ratios of CPU, memory, and bandwidth for best performance.
  • Centralized data architecture. Amazon S3 makes it easy to build a multi-tenant environment, where many users can bring their own data analytics tools to a common set of data. This improves both cost and data governance over that of traditional solutions, which require multiple copies of data to be distributed across multiple processing platforms.
  • Integration with clusterless and serverless AWS services. Use Amazon S3 with Amazon Athena, Amazon Redshift Spectrum, Amazon Rekognition, and AWS Glue to query and process data. Amazon S3 also integrates with AWS Lambda serverless computing to run code without provisioning or managing servers. With all of these capabilities, you only pay for the actual amounts of data you process or for the compute time that you consume.
  • Standardized APIs. Amazon S3 RESTful APIs are simple, easy to use, and supported by most major third-party independent software vendors (ISVs), including leading Apache Hadoop and analytics tool vendors. This allows customers to bring the tools they are most comfortable with and knowledgeable about to help them perform analytics on data in Amazon S3.

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Building Big Data Storage Solutions for Maximum Flexibility