I have been published and covered extensively by both mainstream media and technology publications, including prestigious publications such as The Wall Street Journal, BBC, CNN, and NPR. This includes business and technical publications such as the Harvard Business Review, TechCrunch, KDNuggets, and O’Reilly. I am published on Apress, Pragmatic Programmers, and O’Reilly.

Select Million Monkeys Project Articles

Interviews:

Wall Street Journal, Fox News, The Register, Reno Gazette Journal, The Toronto Star, The Random Fact, Ubuntu Cloud

Articles (not full list):

BBC, CNN, Slashdot (Again), Gizmodo, Engadget, ArsTecnica, Wired, The Washington Post, The Huffington Post

Radio:

NPR, Radio New Zealand,  Australian Broadcasting Company, CBS Radio

Video:

AOL

Books and Academic Papers

Anderson, Jesse. Data Teams: A Unified Management Model for Successful Data-Focused Teams, 2020

Anderson, Jesse. Data Engineering Teams, n.d. https://www.bigdatainstitute.io/data-engineering-teams-book/.

Anderson, Jesse. The Ultimate Guide to Switching Careers to Big Data, n.d. https://www.bigdatainstitute.io/data-engineering-teams-book/.

Franks, Bill & Anderson, Jesse, et al. 97 Things About Ethics Everyone in Data Science Should Know: Collective Wisdom from the Experts, 2020

Fournier, Camille & Anderson, Jesse, et al. 97 Things Every Engineering Manager Should Know: Collective Wisdom from the Experts, 2020

Anunciação, Heverton & Anderson, Jesse, et al. Data Science and Business Intelligence: Advice from important Data Scientists around the World, 2020

Conferences

Strata+Hadoop World (NYC 2012, SJC 2015, NYC 2015, SJC 2016, NYC 2016, SCJ 2017, NYC 2017, Singapore 2017, SJC 2018, London 2018, NYC 2018, SF 2019)

Google Next (2017)

HBaseCon (video)

OSCON

GOTO Chicago (2017)

QCon Sao Paulo

TWDI (Austin, Boston)

Big Data Congress

Infinite Conf

DataEngConf

Data Day Texas (Data Engineering Keynote 2019)

Microsoft Build 2019

Big Things Conference

Flink Forward Berlin

Pulsar Summit

Articles and Guest Posts

To Build Less-Biased AI, Hire a More-Diverse Team (Harvard Business Review) (Quoted)

Why a data scientist is not a data engineer (O’Reilly)

Data engineers vs data scientists (O’Reilly)

Creating Big Data Solutions with Impala (O’Reilly)

The Missing Teams For Data Scientists (KDNuggets)

Comparing Pulsar and Kafka From a CTO’s Point of View (DZone)

Processing Big Data With MapReduce (Pragmatic Programmers)

The Cloud and Amazon Web Services (Pragmatic Programmers)

How to choose a cloud provider (O’Reilly)

What is a Productive Data Engineering Team? (O’Reilly)

On Complexity in Big Data (O’Reilly)

Future-Proof Your Big Data Processing with Apache Beam (The New Stack)

Strata+Hadoop World and Beam (Apache Beam)

Future-proof and scale-proof your code (O’Reilly Radar)

Is my developer team ready for big data? (O’Reilly Radar)

What If The Next Hadoop Doesn’t Use Java? (The New Stack)

The Different Types of Programmers (Inside BigData)

Walking Then Running (Inside BigData)

The Secret to Project Success (Cloudera VISION)

Switching Careers to Programming (Inside BigData)

What Sudoku Can Teach Us About Learning to Program (Inside BigData)

Hadoop 101: The Most Important Terms, Explained (softwareadvice.com)

Insights from the NFL’s Play-by-Play Dataset: What business leaders can learn from football (GigaOM)

Data Insights from the NFL’s ‘Play by Play’ Dataset (Cloudera VISION)

How to Hire Your First Software Engineer (CEO.com)

Hadoop Ecosystem (NetworkWorld)

HBase Thrift Interface Series (blog.cloudera.com)

A Few Good Use Cases (PragPub)

Big Problems…And Data Wasn’t the Only One (PragPub)

Augmenting Unstructured Data (programming.oreilly.com)

HBase REST Interface Series (blog.cloudera.com)

Understanding MapReduce via Boggle (blog.cloudera.com)

Using Eclipse with the QuickStart VM (blog.cloudera.com)

I’ve Been A Bad Feeder (Startup Communities/StartRev.com)

Cloudera Manager Training Series (cloudera.com)

The Cloud Saves Money  (Pragmatic Magazine)

Dealing With the Politics of a Cloud Move  (Pragmatic Magazine)

A Million Monkeys and Shakespeare (Significance)

Academic Papers and Citations
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