Jesse Anderson is a Data Engineer, Creative Engineer, and Managing Director of Big Data Institute.
He mentors companies all over the world ranging from startups to Fortune 100 companies on Big Data. This includes projects using cutting-edge technologies like Apache Kafka, Apache Hadoop, and Apache Spark.
He is widely regarded as an expert in the field for his novel teaching practices. Jesse is published on Apress, O’Reilly, and Pragmatic Programmers. He has been covered in prestigious publications such as The Wall Street Journal, Harvard Business Review, CNN, BBC, NPR, Engadget, and Wired.
Jesse started Big Data Institute in 2014 with the vision of doing big data consulting and training in a different way. Big Data Institute takes a long-term approach to help you build the right team and use your existing resources more efficiently. We guarantee results and success.
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)
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)
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)
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
Google Next (2017)
GOTO Chicago (2017)
TWDI (Austin, Boston)
Data Day Texas (Data Engineering Keynote 2019)
Microsoft Build 2019
Big Things Conference
Flink Forward Berlin
Data Science Global Summit
Agogo, D. & Anderson, J. (2019). Teaching Tip: The Data Shuffle: Using Playing Cards to Illustrate Data Management Concepts to a Broad Audience. Journal of Information Systems Education, 30(2), 84-96.
Kohar, Richard. Basic Discrete Mathematics: Logic, Set Theory, & Probability. New Jersey: World Scientific, 2016. Print.
Articles (not full list):
We’d love to hear how Jesse can help your data team be more successful.