In this article, we’ll quickly run through the 10 most important factors when building a data lake: We’ve covered design principles and best practices in more depth elsewhere – you can check out the links to dive deeper. Design Principles and Best Practices for Building a Data Lake To learn more, check out What is a Data Lake? and Understanding Data Lakes and Data Lake Platforms. As we shall see in the examples below, there are many different combinations of these tools that can be used to build a data lake, based on the specific skillset and tooling available in the organizatino. ĭata lake architecture is simply the combination of tools used to build and operationalize this type of approach to data – starting from event processing tools, through ingestion and transformation pipelines, to analytics and query tools. This makes it ideal for businesses that need to analyze data that is constantly changing, or very large datasets. In a data lake, we store large amounts of unstructured data in an object store such as Amazon S3, without structuring the data in advance and while maintaining flexibility to perform further ETL and ELT on the data in the future. What is data lake architecture?Ī data lake is an architecture pattern rather than a specific platform, built around a big data repository that uses a schema – on – read approach. Use this guide 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. That’s why we’ll jump right into real-life examples of companies that have built their data lakes on Amazon S3, after covering some basic principles of data lake architecture. Well, we’re strong believers in the notion that an example is worth a thousand convoluted explanations. What’s next? How do you go about building a data lake that delivers the results you’re expecting? So you’ve decided it’s time to overhaul your data architecture. Upsolver’s newest offering, SQLake, takes advantage of the same cloud-native processing engine used by the Upsolver customers featured here. With SQLake you can build and run reliable data pipelines on streaming and batch data via an all-SQL experience.
For a more detailed, hands-on example of building a data lake to store, process and analyze petabytes of data, check our data lake webinar with ironSource and Amazon Web Services. This article presents reference architectures and examples of data lake implementations, from around the web as well as from Upsolver customers. 8 Data Lake Examples to Copy and Learn From.Design Principles and Best Practices for Building a Data Lake.
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