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This chapter is from the book

This chapter is from the book

Data Just Right: Introduction to Large-Scale Data & AnalyticsData Just Right: Introduction to Large-Scale Data & Analytics

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This chapter is from the book

This chapter is from the book

Data Just Right: Introduction to Large-Scale Data & AnalyticsData Just Right: Introduction to Large-Scale Data & Analytics

Learn More Buy

Summary

Solving large-scale data challenges ultimately boils down to building a scalable strategy for tackling well-defined, practical use cases. The best solutions combine technologies designed to tackle specific needs for each step in a data processing pipeline. Providing high availability along with the caching of large amounts of data as well as high-performance analysis tools may require coordination of several sets of technologies. Along with this, more complex pipelines may require data-transformation techniques and the use of specific formats designed for efficient sharing and interoperability.

The key to making the best data-strategy decisions is to keep our core data principles in mind. Always understand your business needs and use cases before evaluating technology. When necessary, make sure that you have a plan to scale your data solution—either by deciding on a database that can handle massive growth of data or by having a plan for interoperability when the need for new software comes along. Make sure that you can retrieve and export data. Think about strategies for sharing data, whether internally or externally. Avoid the need to buy and manage new hardware. And above all else, always keep the questions you are trying to answer in mind before embarking on a software development project.

Now that we’ve established some of the ground rules for playing the game in the Era of the Big Data Trade-Off, let’s take a look at some winning game plans.

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Last Update: November 17, 2020