Who is Jesse?
Jesse Anderson is a Data Engineer, Creative Engineer, and Managing Director of Big Data Institute.
From nimble startups to established Fortune 100s, Jesse mentors companies globally on Big Data, leveraging cutting-edge tech like Apache Kafka, Hadoop, and Spark.
A true innovator in the field, Jesse’s groundbreaking teaching methods have not only earned him recognition from industry leaders but also captured the attention of renowned publications like The Wall Street Journal and Harvard Business Review. His published works, spanning Apress, O’Reilly, and Pragmatic Programmers, further showcase his unique approach. Reaching a diverse audience through platforms like CNN, BBC, NPR, Engadget, and Wired, Jesse’s influence extends far beyond the classroom, shaping the future of data education.
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Big Data Institute
Forget outdated consulting models. Big Data Institute, established in 2014, takes a unique, long-term approach to your data challenges. We go beyond quick fixes, collaborating with you to build sustainable, high-performing data teams that maximize your existing resources. Guaranteed results are just the beginning – we’re committed to your long-term data success.
Biblography
Jesse has 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. He has been published on Apress, Pragmatic Programmers, and O’Reilly.
What Great Hybrid Cultures Do Differently (Harvard Business Review)
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)
Macey, Tobias & Anderson, Jesse et al. 97 Things Every Data Engineer Should Know, 2021
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
Anunciao, Heverton & Anderson, Jesse, et al. Data Science and Business Intelligence: Advice from important Data Scientists around the World, 2020
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)
GOTO Chicago (2017)
TWDI (Austin, Boston)
Infinite Conf
DataEngConf
Data Day Texas (Data Engineering Keynote 2019)
Microsoft Build 2019
Big Things Conference
Flink Forward Berlin
Pulsar Summit
Data Science Global Summit
GOTO Amsterdam 2023
DSPA Insights 24 – Porto Edition
Soda Conference London 2024
Amsterdam Data Contracts Workshop 2024
Big Data London 2024
Senzing User Conference
Kohar, Richard. Basic Discrete Mathematics: Logic, Set Theory, & Probability. New Jersey: World Scientific, 2016. Print.
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:
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