In this approach, you have a division of labor, where you distribute the steps across to different people, e.g. We will build on these fundamentals to focus on performance in Chapter 5. Once again, we will be leveraging Klodars Corporation, a fictitious organization to illustrate our strategies. In this chapter, we will take a closer look at these considerations and also strategies to optimize your data lake for scale, while continuing to optimize for costs. Contrary to popular opinion, scale and performance are not always a trade off with costs, but they very much go hand in hand together. To ensure your design is also scalable and continues to perform as your data and the use cases grow, its important to realize the various factors that impact the scale and performance of your data lake. As they say in business, going from 0 to 1 is a different challenge than going from 1 to 100 and from 100 to 1000. Your business is booming and your data estate is growing rapidly. Your data lake is very successful that the demand for more scenarios is now higher, and you are busy serving the needs of your new customers. Theoretically, you also have the first set of use cases and scenarios successfully running in production. ![]() If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the author at you change the way you look at things, the things you look at change.Īfter reading the first three chapters, you have all that you need to get your data lake architecture up and running on the cloud, and at a reasonable cost profile for your organization. Please note that the GitHub repo will be made active later on. ![]() This will be the 4th chapter of the final book. With Early Release ebooks, you get books in their earliest formâthe authorâs raw and unedited content as they writeâso you can take advantage of these technologies long before the official release of these titles.
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