Erik on Software

Personal Reading List: 4Q2022

My personal reading list for October, November, and December 2022.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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AWS Certified Database - Specialty (DBS-C01) Certification, by Kate Gawron, Packt Publishing, 2022. Copy provided by Packt publicist Nivedita Singh.

The Ideal Executive: Why You Cannot Be One and What to Do About It, by Ichak Kalderon, The Adizes Institute, 2004.

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 At Bat (Langsam)

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Machine Learning Engineering in Action, by Ben Wilson, Manning, 2022. Copy provided by author Ben Wilson.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

The Manager's Path: A Guide for Tech Leaders Navigating Growth and Change, by Camille Fournier, O'Reilly, 2017.

The Staff Engineer's Path: A Guide for Individual Contributors Navigating Growth and Change, by Tanya Reilly, O'Reilly, 2022.

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In the Hole

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97 Things Every Data Engineer Should Know, edited by Tobias Macey, O'Reilly, 2021. Copy provided by Promethium.

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (Second Edition), by Christoph Molnar, Independently Published, 2022.

Personal Reading List: 3Q2022

My personal reading list for July, August, and September 2022.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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AWS Certified Database - Specialty (DBS-C01) Certification, by Kate Gawron, Packt Publishing, 2022. Copy provided by Packt publicist Nivedita Singh.

Machine Learning Engineering in Action, by Ben Wilson, Manning, 2022. Copy provided by author Ben Wilson.

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 At Bat (Langsam)

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The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (Second Edition), by Christoph Molnar, Independently Published, 2022.

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In the Hole

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97 Things Every Data Engineer Should Know, edited by Tobias Macey, O'Reilly, 2021. Copy provided by Promethium.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Personal Reading List: 2Q2022

My personal reading list for April, May, and June 2022.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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How Will You Measure Your Life?, by Clayton M. Christensen, James Allworth, and Karen Dillon, Harper Business, 2012.

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (Second Edition), by Christoph Molnar, Independently Published, 2022.

Machine Learning Engineering in Action, by Ben Wilson, Manning, 2022. Copy provided by author Ben Wilson.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Enterprise AIOps: A Framework for Enabling Artificial Intelligence, by Justin Neroda, Steve Escaravage, and Aaron Peters, O'Reilly, 2021. Copy provided by Booz Allen Hamilton.

Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines, by Jen Fisher and Anh Phillips, McGraw Hill, 2021. Copy provided by Deloitte.

Running with Purpose: How Brooks Outpaced Goliath Competitors to Lead the Pack, by Jim Weber, HarperCollins Leadership, 2022. Copy provided by Amazon.

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In the Hole

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AWS Certified Database - Specialty (DBS-C01) Certification, by Kate Gawron, Packt Publishing, 2022. Copy provided by Packt publicist Nivedita Singh.

Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Community Comment: Part 16

The comments I provided in reaction to a community discussion thread:
https://www.linkedin.com/feed/update/urn:li:activity:6883455289505521664?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A6883455289505521664%2C6883466108553244672%29&replyUrn=urn%3Ali%3Acomment%3A%28activity%3A6883455289505521664%2C6883474864909377536%29

Founder of Taproot Foundation and Imperative: Need your advice, please. I buy and get a lot of books as gifts - which I love. My new home office and frankly our house is starting to be overflowing with them. I did a purge last year but there are still piles of books. I hate clutter - it makes me anxious. But I also don’t love digital books. How do you manage your books? Any tricks?

Global Top 100 Influencer in Strategy and Analytics, and Talent & HR Strategy Transformation Leader at IBM:

I hear you!! We’re book rats, and I’ll give you a kidney but not my books. So, living in a New York City apartment compounds my problems. We have bookcases in every room to help..but it’s an ongoing battle.

Regardless of source (gifts, etc.), I try and purge everything that doesn’t fit in the following categories. I also do read a lot online with the Scribd app which helps me manage immediate access to something I want to read without clutter.

I classify my books as:

1. KEEP - because they are sentimental in value, some really old…books I owned as a child, others rare and valuable, some obscure. Too many of my books, alas, fall into this flexible, elastic category.

2. REREAD - books I read often and dip into, just for fun…books and characters I love like people.

3. ASPIRE - books I want to read ..these are stored on my bedside table. So by default; I cannot have more than 10-12 of these at a time. I have 10 right now.

4. REFER - needed reference purposes. This is the most useless category in my opinion as all of these references can be accessed online, and these books are read rarely. Other family members are attached to some of these books; so they stay.

Founder of Taproot Foundation and Imperative: Great categories! Very practical!

Gfesser: You've come up with some nice classifiers, essentially the same as mine! Two additional ideas to consider. (1) My books in the "keep" category are in storage. Since these aren't books I plan to re-read or reference, I get these out of the way. (2) My books in the "aspire" category (which I call the "currently reading" category) are broken down into four subcategories, using baseball nomenclature (and a little German) for my blog readers: "At Bat (Schnell)", "At Bat (Langsam)", "On Deck", and "In the Hole." As I mention in the quarterly reading list updates on my blog, I update these lists throughout each three month time period in an agile way, providing a bit of accountability while at the same time demonstrating that priorities often change.

Global Top 100 Influencer in Strategy and Analytics, and Talent & HR Strategy Transformation Leader at IBM: 

Erik Gfesser, love this. Shall look for your blog.

I also have to confess to an unwarranted and egregious sentiment of “visual satisfaction” from my “keep” category. This is one reason that prevents me from storing them. For example - I was able to find a copy of “The Malachite Casket”, an obscure collection of Russian folktales, which I read as a child in India. I also have the moldy and tattered original, that my sisters and I read. Having these two copies of the same book around, where my kids and I can see them, gives me joy. And alas, contributes severely to my dilemma.

Founder of Taproot Foundation and Imperative: Erik Gfesser you had me at ‘baseball’

Personal Reading List: 1Q2022

My personal reading list for January, February, and March 2022.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Master Your Next Move: The Essential Companion to "The First 90 Days", by Michael D. Watkins, Harvard Business Review Press, 2019.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

Hero on a MIssion: A Path to a Meaningful Life, by Donald Miller, HarperCollins Leadership, 2022. Copy provided by Amazon.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Enterprise AIOps: A Framework for Enabling Artificial Intelligence, by Justin Neroda, Steve Escaravage, and Aaron Peters, O'Reilly, 2021. Copy provided by Booz Allen Hamilton.

Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines, by Jen Fisher and Anh Phillips, McGraw Hill, 2021. Copy provided by Deloitte.

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In the Hole

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Personal Reading List: 4Q2021

My personal reading list for October, November, and December 2021.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Do Something for Nothing: Seeing Beneath the Surface of Homelessness, through the Simple Act of a Haircut, by Joshua Coombes, Akashic Books, 2021. Copy provided by Amazon.

The Success Trail: Learn to Win with a Marathon Runner's Mindset, by Jack Perconte, Second Base Publishing, 2021. Copy provided by author Jack Perconte (former professional baseball player).

Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines, by Jen Fisher and Anh Phillips, McGraw Hill, 2021. Copy provided by Deloitte.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

The Kubernetes Workshop: Learn How to Build and Run Highly Scalable Workloads on Kubernetes, by Zachary Arnold, Sahil Dua, Wei Huang, Faisal Masood, Melony Qin, and Mohammed Abu Taleb, Packt Publishing, 2020.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

Production Kubernetes: Building Successful Application Platforms, by Josh Rosso, Rich Lander, Alex Brand, and John Harris, O'Reilly, 2021.

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 On Deck

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Enterprise AIOps: A Framework for Enabling Artificial Intelligence, by Justin Neroda, Steve Escaravage, and Aaron Peters, O'Reilly, 2021. Copy provided by Booz Allen Hamilton.

Kubernetes in Production Best Practices: Build and Manage Highly-Available Production-Ready Kubernetes Clusters, by Aly Saleh and Murat Karslioglu, Packt Publishing, 2021.

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In the Hole

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Sourcegraph ABC Book, by the Sourcegraph team, Sourcegraph, 2020. Copy provided by Sourcegraph.

Personal Reading List: 3Q2021

My personal reading list for July, August, and September 2021.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Executive Presence for the Modern Leader: A Guide to Cultivating Success and Thriving in the Workplace, by D. A. Benton, Rockridge Press, 2021. Copy provided by Amazon.

The Kubernetes Workshop: Learn How to Build and Run Highly Scalable Workloads on Kubernetes, by Zachary Arnold, Sahil Dua, Wei Huang, Faisal Masood, Melony Qin, and Mohammed Abu Taleb, Packt Publishing, 2020.

Production Kubernetes: Building Successful Application Platforms, by Josh Rosso, Rich Lander, Alex Brand, and John Harris, O'Reilly, 2021.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Do Something for Nothing: Seeing Beneath the Surface of Homelessness, through the Simple Act of a Haircut, by Joshua Coombes, Akashic Books, 2021. Copy provided by Amazon.

Kubernetes in Production Best Practices: Build and Manage Highly-Available Production-Ready Kubernetes Clusters, by Aly Saleh and Murat Karslioglu, Packt Publishing, 2021.

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In the Hole

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Personal Reading List: 2Q2021

My personal reading list for April, May, and June 2021.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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The First 90 Days: Proven Strategies for Getting Up to Speed Faster and Smarter, by Michael D. Watkins, Harvard Business Review Press, 2013. Copy provided by Deloitte.

The Kubernetes Workshop: Learn How to Build and Run Highly Scalable Workloads on Kubernetes, by Zachary Arnold, Sahil Dua, Wei Huang, Faisal Masood, Melony Qin, and Mohammed Abu Taleb, Packt Publishing, 2020.

Production Kubernetes: Building Successful Application Platforms, by Josh Rosso, Rich Lander, Alex Brand, and John Harris, O'Reilly, 2021.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Kubernetes in Production Best Practices: Build and Manage Highly-Available Production-Ready Kubernetes Clusters, by Aly Saleh and Murat Karslioglu, Packt Publishing, 2021.

Python Programming for Beginners: A Kid's Guide to Coding Fundamentals, by Patricia Foster, Rockridge Press, 2020. Copy provided by Amazon.

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In the Hole

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Personal Reading List: 1Q2021

My personal reading list for January, February, and March 2021.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Learning Spark (Second Edition), by Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Lee, O'Reilly, 2020. Copy provided by Databricks.

Scala for Java Developers: A Practical Primer, by Toby Weston, Apress, 2018. 

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

Scala for the Impatient (Second Edition), by Cay Horstmann, Addison-Wesley Professional, 2016.

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 On Deck

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Coding Concepts for Kids: Learn to Code Without a Computer, by Randy Lynn, Rockridge Press, 2020. Copy provided by Amazon.

Python Programming for Beginners: A Kid's Guide to Coding Fundamentals, by Patricia Foster, Rockridge Press, 2020. Copy provided by Amazon.

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In the Hole

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Personal Reading List: 4Q2020

My personal reading list for October, November, and December 2020.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

The Software Architect Elevator: Redefining the Architect's Role in the Digital Enterprise, by Gregor Hohpe, O'Reilly, 2020. Copy provided by Amazon.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

Learning Spark (Second Edition), by Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Lee, O'Reilly, 2020. Copy provided by Databricks.

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 On Deck

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

Python Programming for Beginners: A Kid's Guide to Coding Fundamentals, by Patricia Foster, Rockridge Press, 2020. Copy provided by Amazon.

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In the Hole

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Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Personal Reading List: 3Q2020

My personal reading list for July, August, and September 2020.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Burn-In: A Novel of the Real Robotic Revolution, by P. W. Singer and August Cole, Houghton Mifflin Harcourt, 2020. Copy provided by Amazon.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

Learning Spark (Second Edition), by Jules S. Damji, Brooke Wenig, Tathagata Das, and Denny Lee, O'Reilly, 2020. Copy provided by Databricks.

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 On Deck

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Apache Oozie: The Workflow Scheduler for Hadoop, by Mohammad Kamrul Islam and Aravind Srinivasan, O'Reilly, 2015.

Apache Sqoop Cookbook, by Kathleen Ting and Jarek Jarcec Cecho, O'Reilly, 2013.

Getting Started with Impala: Interactive SQL for Apache Hadoop, by John Russell, O'Reilly, 2015.

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In the Hole

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

Personal Reading List: 2Q2020

My personal reading list for April, May, and June 2020.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages, by Tanmay Bakshi, McGraw Hill, 2020. Copy provided by Amazon.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Presto: The Definitive Guide, by Matt Fuller, Manfred Moser, and Martin Traverso, O'Reilly, 2020. Copy provided by Starburst.

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In the Hole

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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Personal Reading List: 1Q2020

My personal reading list for January, February, and March 2020.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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How to Make Mistakes in Python, by Mike Pirnat, O'Reilly, 2015. Copy provided by O'Reilly.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Rethinking Strategy, by Steve Tighe, Wiley, 2019. Copy provided by author Steve Tighe.

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 At Bat (Langsam)

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Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Serving Machine Learning Models, by Boris Lublinsky, O'Reilly, 2017. Copy provided by Lightbend.

What is Augmented Analytics?: Powering Your Data with AI, by Alice LaPlante, O'Reilly, 2019. Copy provided by Oracle.

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In the Hole

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Getting DataOps Right, by Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli, O'Reilly, 2019. Copy provided by Tamr.

Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Personal Reading List: 4Q2019

My personal reading list for October, November, and December 2019.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

What is Augmented Analytics?: Powering Your Data with AI, by Alice LaPlante, O'Reilly, 2019. Copy provided by Oracle.

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 At Bat (Langsam)

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Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho, O'Reilly, 2015.

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 On Deck

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Thinking Architecturally: Lead Technical Change Within Your Engineering Team, by Nathaniel Schutta, O'Reilly, 2018. Copy provided by Pivotal.

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In the Hole

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Getting DataOps Right, by Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli, O'Reilly, 2019. Copy provided by Tamr.

Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith, by Sam Newman, O'Reilly, 2020. Copy provided by Nginx.

Personal Reading List: 3Q2019

My personal reading list for July, August, and September 2019.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Disruptive Analytics: Charting Your Strategy for Next-Generation Business Analytics, by Thomas W. Dinsmore, Apress, 2016.

The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

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 At Bat (Langsam)

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AWS Certified Solutions Architect Associate All-in-One Exam Guide (Exam SAA-C01), by Joyjeet Banerjee, McGraw-Hill Education, 2018.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Thinking Architecturally: Lead Technical Change Within Your Engineering Team, by Nathaniel Schutta, O'Reilly, 2018. Copy provided by Pivotal.

Evolving Data Infrastructure: Tools and Best Practices for Advanced Analytics and AI, by Ben Lorica and Paco Nathan, O'Reilly, 2019. Copy provided by O'Reilly.

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In the Hole

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Getting DataOps Right, by Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli, O'Reilly, 2019. Copy provided by Tamr.

Machine Learning at Enterprise Scale: How Real Practitioners Handle Six Common Challenges, by Piero Cinquegrana and Matheen Raza, O'Reilly, 2019. Copy provided by Qubole.

Personal Reading List: 2Q2019

My personal reading list for April, May, and June 2019.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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The Model Thinker: What You Need to Know to Make Data Work for You, by Scott E. Page, Basic Books, 2018.

Thinking Architecturally: Lead Technical Change Within Your Engineering Team, by Nathaniel Schutta, O'Reilly, 2018. Copy provided by Pivotal.

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 At Bat (Langsam)

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Kafka: The Definitive Guide, by Neha Narkhede, Gwen Shapira, and Todd Palino, O'Reilly, 2017. Copy provided by Confluent.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Apache NiFi for Dummies, by Christopher Gambino, Dan Rice, Joseph Niemiec, Mark Johnson, and Jordan Martz, Wiley, 2018. Copy provided by Hortonworks and Attunity.

AWS Certified Solutions Architect Associate All-in-One Exam Guide (Exam SAA-C01), by Joyjeet Banerjee, McGraw-Hill Education, 2018.

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In the Hole

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Designing Autonomous Teams and Services: Deliver Continuous Business Value through Organizational Alignment, by Nick Tune and Scott Millett, O'Reilly, 2017. Copy provided by O'Reilly.

Introducing Istio Service Mesh for Microservices, by Christian Posta and Burr Sutter, O'Reilly, 2018. Copy provided by Red Hat.

Personal Reading List: 1Q2019

My personal reading list for January, February, and March 2019.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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An Introduction to Machine Learning Interpretability, by Patrick Hall and Navdeep Gill, O'Reilly, 2018. Copy provided by Dataiku.

AWS Certified Solutions Architect Associate All-in-One Exam Guide (Exam SAA-C01), by Joyjeet Banerjee, McGraw-Hill Education, 2018.

Thinking Architecturally: Lead Technical Change Within Your Engineering Team, by Nathaniel Schutta, O'Reilly, 2018. Copy provided by Pivotal.

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 At Bat (Langsam)

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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.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 On Deck

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Apache NiFi for Dummies, by Christopher Gambino, Dan Rice, Joseph Niemiec, Mark Johnson, and Jordan Martz, Wiley, 2018. Copy provided by Hortonworks and Attunity.

Building Evolutionary Architectures: Support Constant Change, by Neal Ford, Rebecca Parsons, and Patrick Kua, O'Reilly, 2017. Copy provided by O'Reilly.

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In the Hole

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Designing Autonomous Teams and Services: Deliver Continuous Business Value through Organizational Alignment, by Nick Tune and Scott Millett, O'Reilly, 2017. Copy provided by O'Reilly.

Introducing Istio Service Mesh for Microservices, by Christian Posta and Burr Sutter, O'Reilly, 2018. Copy provided by Red Hat.

Personal Reading List: 4Q2018

My personal reading list for October, November, and December 2018.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Common Errors in Statistics and How to Avoid Them (Fourth Edition), by Phillip I. Good and James W. Hardin, Wiley, 2012.

Practical Hive: A Guide to Hadoop's Data Warehouse System, by Scott Shaw, Andreas Francois Vermeulen, Ankur Gupta, and David Kjerrumgaard, Apress, 2016.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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 At Bat (Langsam)

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Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, by Dean Abbott, Wiley, 2014. Copy provided by Amazon.

Kafka: The Definitive Guide, by Neha Narkhede, Gwen Shapira, and Todd Palino, O'Reilly, 2017. Copy provided by Confluent.

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 On Deck

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Apache NiFi for Dummies, by Christopher Gambino, Dan Rice, Joseph Niemiec, Mark Johnson, and Jordan Martz, Wiley, 2018. Copy provided by Hortonworks and Attunity.

Building Evolutionary Architectures: Support Constant Change, by Neal Ford, Rebecca Parsons, and Patrick Kua, O'Reilly, 2017. Copy provided by O'Reilly.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron, O'Reilly, 2017. Copy provided by O'Reilly.

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In the Hole

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Designing Autonomous Teams and Services: Deliver Continuous Business Value through Organizational Alignment, by Nick Tune and Scott Millett, O'Reilly, 2017. Copy provided by O'Reilly.

Introducing Istio Service Mesh for Microservices, by Christian Posta and Burr Sutter, O'Reilly, 2018. Copy provided by Red Hat.

Personal Reading List: 3Q2018

My personal reading list for July, August, and September 2018.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Building Evolutionary Architectures: Support Constant Change, by Neal Ford, Rebecca Parsons, and Patrick Kua, O'Reilly, 2017. Copy provided by O'Reilly.

Designing Event-Driven Systems: Concepts and Patterns for Streaming Services with Apache Kafka, by Ben Stopford, O'Reilly, 2018. Copy provided by O'Reilly.

High Performance In-Memory Computing with Apache Ignite, by Shamim Bhuiyan, Michael Zheludkov, and Timur Isachenko, Lulu.com, 2017.

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark, by Holden Karau and Rachel Warren, O'Reilly, 2017.

Introducing Istio Service Mesh for Microservices, by Christian Posta and Burr Sutter, O'Reilly, 2018. Copy provided by Red Hat.

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 At Bat (Langsam)

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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.

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 On Deck

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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron, O'Reilly, 2017. 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.

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In the Hole

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Designing Autonomous Teams and Services: Deliver Continuous Business Value through Organizational Alignment, by Nick Tune and Scott Millett, O'Reilly, 2017. Copy provided by O'Reilly.

Kafka: The Definitive Guide, by Neha Narkhede, Gwen Shapira, and Todd Palino, O'Reilly, 2017. Copy provided by Confluent.

Spark: The Definitive Guide, by Bill Chambers and Matel Zaharla, O'Reilly 2018. Copy provided by O'Reilly.

Personal Reading List: 2Q2018

My personal reading list for April, May, and June 2018.

Color Key: Special Notes, Completed Reading. Updated throughout the Quarter.

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At Bat (Schnell)

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Building Evolutionary Architectures: Support Constant Change, by Neal Ford, Rebecca Parsons, and Patrick Kua, O'Reilly, 2017. Copy provided by O'Reilly.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, by Martin Kleppmann, O'Reilly, 2017.

The Obstacle is the Way: The Ancient Art of Turning Adversity to Advantage, by Ryan Holiday, PROFILE BOOKS, 2015.

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 At Bat (Langsam)

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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.

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.

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 On Deck

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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron, O'Reilly, 2017. Copy provided by O'Reilly.

High Performance In-Memory Computing with Apache Ignite, by Shamim Bhuiyan, Michael Zheludkov, and Timur Isachenko, Lulu.com, 2017.

Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce, O'Reilly, 2017. Copy provided by MarkLogic.

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In the Hole

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Designing Autonomous Teams and Services: Deliver Continuous Business Value through Organizational Alignment, by Nick Tune and Scott Millett, O'Reilly, 2017. Copy provided by O'Reilly.

Kafka: The Definitive Guide, by Neha Narkhede, Gwen Shapira, and Todd Palino, O'Reilly, 2017. Copy provided by Confluent.  

MongoDB in Action (Second Edition), by Kyle Banker, Peter Bakkum, Shaun Verch, Douglas Garrett, and Tim Hawkins, Manning Publications, 2016.