Personal Reading List: 3Q2017
My personal reading list for July, August, and September 2017.
Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.
At Bat (Schnell)
Fast Data Architectures for Streaming Applications: Getting Answers Now from Data Sets that Never End, by Dean Wampler PhD, O'Reilly, 2016. Copy provided by O'Reilly.
Kafka: The Definitive Guide, by Neha Narkhede, Gwen Shapira, and Todd Palino, O'Reilly, 2017. Copy provided by Confluent.
Microservices vs. Monoliths: The Reality Beyond the Hype, by Chris Richardson, Dan Haywood, Mike Gehard, Matt McLarty, Mark Little, and Adrian Cockcroft, C4Media (Publisher of InfoQ.com), 2017. Copy provided by InfoQ.com.
Next Generation Databases: NoSQL, NewSQL, and Big Data, by Guy Harrison, Apress, 2015.
At Bat (Langsam)
Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, by Dean Abbott, Wiley, 2014. Copy provided by Amazon.
Common Errors in Statistics and How to Avoid Them (Fourth Edition), by Phillip I. Good and James W. Hardin, Wiley, 2012.
Python Machine Learning, by Sebastian Raschka, Packt Publishing, 2015.
Python Machine Learning Cookbook, by Prateek Joshi, Packt Publishing, 2016.
On Deck
Cloud Data Warehousing for Dummies, by Joe Kraynak, John Wiley & Sons Inc., 2017. Copy provided by Snowflake.
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, by Martin Kleppmann, O'Reilly, 2017.
Fast Data: Smart and At Scale: Design Patterns and Recipes, by Ryan Betts and John Hugg, O'Reilly, 2015. Copy provided by O'Reilly.
In the Hole
Cassandra: The Definitive Guide (Second Edition), by Jeff Carpenter and Eben Hewitt, O'Reilly, 2016.
Hadoop Application Architectures: Designing Real World Big Data Applications, by Mark Grover, Ted Malaska, Jonathan Seidman, and Gwen Shapira, O'Reilly, 2015. Copy provided by O'Reilly.
Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.