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

What I Read: Infrastructure

By Andrew Fairless on April 12, 2023March 30, 2023

https://www.kleinerperkins.com/perspectives/infrastructure-in-23/ Infrastructure in ’23Kleiner PerkinsWednesday, February 22nd 2023 “I set aside some time at the beginning of the year to share what I believe to be the most dynamic andContinue readingWhat I Read: Infrastructure

What I Read: Data Product vs. Service

By Andrew Fairless on April 5, 2023February 22, 2023

https://readtechnically.medium.com/data-as-a-product-vs-data-as-a-service-d9f7e622dc55 Data as a Product vs. Data as a ServiceJustin GageJul 14, 2019 “For hiring in particular, DaaP and DaaS require completely different plans and head counts. It’s extremely rareContinue readingWhat I Read: Data Product vs. Service

What I Read: SQL pipelines

By Andrew Fairless on March 27, 2023February 6, 2023

https://www.robinlinacre.com/recommend_sql/ SQL should be your default choice for data engineering pipelinesRobin Linacre2023-01-30 “SQL should be the first option considered for new data engineering work. It’s robust, fast, future-proof and testable.Continue readingWhat I Read: SQL pipelines

What I Read: Feature Platforms

By Andrew Fairless on March 14, 2023February 4, 2023

https://huyenchip.com//2023/01/08/self-serve-feature-platforms.html Self-serve feature platforms: architectures and APIsChip HuyenJan 8, 2023 “The last few years saw the maturation of a core component of the MLOps stack that has significantly improved theContinue readingWhat I Read: Feature Platforms

What I Read: Data Pipeline Design Patterns

By Andrew Fairless on March 8, 2023January 9, 2023

https://www.startdataengineering.com/post/design-patterns/ Data Pipeline Design Patterns – #1. Data flow patternsStart Data EngineeringDec 11, 2022 “This post will cover the typical data flow design patterns. We will learn about the prosContinue readingWhat I Read: Data Pipeline Design Patterns

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

Tag: data engineering