AWS Data Lake: Architecture, Best Practices and Tutorials

Level up your Amazon Web Services data lake with 20+ of our top articles, ebooks and webinars. From general best practices to specific tutorials on Amazon S3, Amazon Athena and more, we’ve got you covered.

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Table of contents

    Why AWS Data Lakes?

    A decade ago, data lakes were considered a niche interest in enterprise data management, and most companies were still relying on the enterprise data warehouse as the foundation of their data architecture. However, as data has grown increasingly complex – semi-structured, continuously generated, voluminous, and lacking a predefined schema – data lakes grew more prominent as a solution for dealing with data that the EDW just couldn’t handle.

    Today, it’s difficult to find a large-scale data infrastructure that doesn’t incorporate data lake design patterns: unstructured storage, open-source file formats, and leveraging best-of-breed analytics tools for different use cases rather than relying on monolithic enterprise-wide deployments.

    Alongside the growing volume and complexity of data, there has been a movement away from on-premises deployments and towards cloud-based infrastructure-as-a-service. Amazon Web Services is the leading provider in this space, and offers a variety of on-demand services for storage, compute, analytics and more – either through its own tools, or as part of its large partner network.

    This page collects the essential resources we’ve published over the years around building, maintaining and managing your AWS data lake. We hope it helps guide you on your data lake journey!

    Make your data lake analytics-ready with Upsolver. the only platform that lets you build continuous SQL pipelines directly on your data lake. Start for free!

    Cloud Data Lakes: The Basics

    • Data Lake as a Service: Is There a GUI-based Data Lake?  Why are data lakes still so difficult in the age of everything-as-a-service? Why are they still dependent on a select group of specialists skilled in arcane programming languages and frameworks? In other words – what’s stopping the data lake from becoming ‘productized’? We try to answer these questions. Read the article
    • Data Lake, Data Warehouse, or Data Lakehouse: Organizations of all sizes can now capture more data from more sources –  more quickly than ever before. But what good is all that real-time data if it takes six months until you can use it? This conundrum is at the core of the data warehouse vs data lake debate. We cover the differences and how to choose between alternatives. Read the article

    AWS Data Lake Architecture

    • 4 Guiding Principles for Modern Data Lake Architecture: Getting data lakes right can be tricky. How do you design a data lake that will serve the business, rather than weigh down your IT department with technical debt and constant data pipeline rejiggering? In this article we cover the four essential principles for effectively architecting your data lake Read the article

    AWS Data Lake Tutorials

    AWS Data Lake Ecosystem and Tools

    AWS Data Lake Architecture Diagrams and Use Cases

    • Examples of Data Lake Architecture on Amazon S3: This post is all about real-life examples of companies that have built their data lakes on Amazon S3. Use it for inspiration, reference, or as your gateway to learn more about the different components you’ll need to become familiar with for your own initiative. Read the article
    • Frictionless Data Lake ETL for Petabyte-scale Streaming Data: ironSource, an app monetization and video advertising platform, managed to transform 500K events per second, using only a visual interface and SQL, saving thousands of engineering hours, reducing latency, and increasing system scale by a factor of ten. Discover how Upsolver helped ironSource reach these results. Watch the webinar
    • Data Architecture for AWS Athena: 6 Examples to Learn From: In this article we’ll look at a few examples of how you can incorporate Athena in different data architectures and to support various use cases – streaming analytics, ad-hoc querying and Redshift cost reduction. For each use case, we’ve included a conceptual AWS-native example, and a real-life example provided by Upsolver customers. Read the article

    Making AWS Data Lakes Analytics-ready with Upsolver

    • Upsolver Technical Whitepaper: In this in-depth technical paper we will present the infrastructural challenges of working with big data streams, and how to tackle these challenges using a data pipeline platform that provides data management, processing and delivery as services – all within a data lake architecture. Read the ebook


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