data engineering – Page 5 – Andrew Fairless, Ph.D.

What I Read: Build vs. Buy, Modern Data Stack

By Andrew Fairless on March 1, 2023January 9, 2023

The Build vs. Buy Guide for the Modern Data Stack The Build vs. Buy Guide for the Modern Data StackNishith AgarwalUpdated December 22, 2022 “There’s been an explosion of solutionsContinue readingWhat I Read: Build vs. Buy, Modern Data Stack

What I Read: Data Engineering 2023 Predictions

By Andrew Fairless on February 20, 2023December 16, 2022

https://www.montecarlodata.com/blog-13-data-engineering-trends-for-2023/ What’s Next for Data Engineering in 2023? 13 PredictionsBarr MosesCEO and Co-founder, Monte Carlo. Proponent of data reliability and action movies.Updated November 29, 2022 “…let’s dive into Tomasz’s predictionsContinue readingWhat I Read: Data Engineering 2023 Predictions

What I Read: Realtime ML

By Andrew Fairless on February 16, 2023December 16, 2022

https://fennel.ai/blog/seven-reasons-why-realtime-ml-is/ Seven Reasons Why Realtime ML Is Here To StayNikhil GargJune 17, 2022 “…more and more top tech companies are making a larger part of their machine learning as realtimeContinue readingWhat I Read: Realtime ML

What I Read: ELT Schedules, Root Cause Analysis

By Andrew Fairless on February 15, 2023December 16, 2022

https://www.montecarlodata.com/how-elt-schedules-can-improve-root-cause-analysis-for-data-engineers/ How ELT Schedules Can Improve Root Cause Analysis For Data EngineersRyan KearnsUpdated December 9, 2022 “In this article, Ryan Kearns… discusses the limitations of segmentation analysis when it comesContinue readingWhat I Read: ELT Schedules, Root Cause Analysis

What I Read: Realtime ML Pipelines

By Andrew Fairless on February 1, 2023December 4, 2022

https://medium.com/@nparsons08/challenges-of-building-realtime-ml-pipelines-4782181425c7 Challenges of Building Realtime ML PipelinesNick ParsonsNov 18 “…as companies start introducing realtime into their ML pipelines, they are finding themselves having to weigh the trade-offs between performance, cost,Continue readingWhat I Read: Realtime ML Pipelines

What I learn: How, learn machine learning

By Andrew Fairless on January 18, 2023December 4, 2022

https://vickiboykis.com/2022/11/10/how-i-learn-machine-learning/ How I learn machine learningVicki BoykisNov 10 2022 “My overarching goal as an MLE is to continuously work towards designing and deploying well-designed, and transparent machine learning systems andContinue readingWhat I learn: How, learn machine learning

What I Read: Data Pipeline Smoke Tests

By Andrew Fairless on January 4, 2023November 5, 2022

https://dagster.io/blog/smoke-test-data-pipeline The Unreasonable Effectiveness of Data Pipeline Smoke TestsSandy RyzaOctober 19, 2022 “Data practitioners waste time writing unit tests to catch bugs they could have caught with smoke tests.”

What I Read: Data Engineers, What’s the profession about

By Andrew Fairless on December 1, 2022October 20, 2022

https://www.kdnuggets.com/2022/10/11-questions-data-engineers-profession-heading.html 11 Questions About Data Engineers: What’s the profession about, and where’s it heading?I hope my answers will be useful to novice data engineers and anyone interested in data engineering.ByContinue readingWhat I Read: Data Engineers, What’s the profession about

What I Read: data catalogs, metadata

By Andrew Fairless on November 28, 2022October 20, 2022

https://towardsdatascience.com/forrester-changed-the-way-they-think-about-data-catalogs-and-heres-what-you-need-to-know-139e13a5869d?gi=7192c997040d Forrester changed the way they think about data catalogs, and here’s what you need to knowThe latest sign of a major shift in how we think about metadataPrukalpaOct 4Continue readingWhat I Read: data catalogs, metadata

Tag: data engineering