Chapter 8. Six Lessons for a Data Informed Future
Itâs amazing how a little tomorrow can make up for a whole lot of yesterday.
John Guare
The promise of a cloud data lake architecture lies in the boundless diversity of scenarios that it enables. In the previous chapters, weâve focused on the most commonly used patterns of data processing with Spark- and Hadoop-flavored technologies. Other aspectsâsuch as real-time stream processing, which generates quick insights on real-time data, and advanced analytics scenarios, which build smart applications on the data lakeâare gaining fast adoption. One thing that all the concepts and frameworks we covered in the previous chapters have in common is that at every juncture in the design or implementation of the cloud data lake, choices are available to you, and each choice comes with trade-offs on cost, complexity, and flexibility. As you make these decisions when designing your cloud data lake, itâs only natural to have the following questions:
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How do I know I made the right choice?
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As my organization grows and so do the scenarios on my data lake, how do I iterate and drive transformation?
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How do I ensure that my organization can be agile to gather and address the next set of requirements?
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How do I think about a global strategy and stay ahead of the needs of my organization?
In this chapter, I leverage the format of lessons learned to provide a structure for you to think about the technical, cultural, and organizational ...
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