1 An urgent need for efficiency in data processing
This chapter covers
- The challenges of dealing with the exponential growth of data
- Comparing traditional and recent computing architectures
- The role and shortcomings of Python in modern data analytics
- Techniques for delivering efficient Python computing solutions
An enormous amount of data is being collected all the time, at intense speeds, and from a broad scope of sources. It is collected whether or not there is currently a use for it. It is collected whether or not there is a way to process, store, access, or learn from it. Before data scientists can analyze it, before designers and developers and policymakers can use it to create products, services, and programs, software engineers must ...
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