data engineering – Page 11 – Andrew Fairless, Ph.D.

What I Watch: Future of Data Engineering

By Andrew Fairless on March 1, 2021February 27, 2021

https://youtu.be/ZZr9oE4Oa5U Future of Data EngineeringDec 16, 2019InfoQ “Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealingContinue readingWhat I Watch: Future of Data Engineering

What I Read: Feature Store vs Data Warehouse

By Andrew Fairless on February 25, 2021February 18, 2021

https://www.logicalclocks.com/blog/feature-store-vs-data-warehouse ML Engineer Guide: Feature Store vs Data WarehouseOctober 8, 2020ML Best Practicesby Jim Dowling “The feature store is a data warehouse of features for machine learning (ML). Architecturally, itContinue readingWhat I Read: Feature Store vs Data Warehouse

What I Read: What is Data Observability?

By Andrew Fairless on February 20, 2021January 20, 2021

https://towardsdatascience.com/what-is-data-observability-40b337971e3e What is Data Observability?Hint: it’s not just data for DevOps.Barr MosesJun 25 “Instead of putting together a holistic approach to address data downtime, teams often tackle data quality andContinue readingWhat I Read: What is Data Observability?

What I Watch: Functional Data Engineering

By Andrew Fairless on February 19, 2021January 18, 2021

https://youtu.be/4Spo2QRTz1k Functional Data Engineering – A Set of Best Practices | LyftMay 24, 2018 “…we’ll discuss functional programming paradigm and explore how applying it to Data Engineering can bring aContinue readingWhat I Watch: Functional Data Engineering

What I Read: How DAGs grow

By Andrew Fairless on February 14, 2021January 18, 2021

https://greatexpectations.io/blog/deeper-wider-thicker/ How DAGs grow: Deeper, wider, and thickerNovember 16, 2020 “Over time, this turns into a steady, compounding creep in the maintenance time that you’re putting into the project…. StuffContinue readingWhat I Read: How DAGs grow

What I Read: Architectures for Modern Data Infrastructure

By Andrew Fairless on February 4, 2021January 10, 2021

https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/ Emerging Architectures for Modern Data Infrastructureby Matt Bornstein, Martin Casado, and Jennifer Li “We’re now starting to see the rise of massive, complex systems built around data – whereContinue readingWhat I Read: Architectures for Modern Data Infrastructure

What I Read: data quality, ML Ops

By Andrew Fairless on January 27, 2021January 1, 2021

https://greatexpectations.io/blog/ml-ops-data-quality/ Why data quality is key to successful ML OpsIn this first post in our 2-part ML Ops series, we are going to look at ML Ops and highlight howContinue readingWhat I Read: data quality, ML Ops

What I Read: Maintaining Machine Learning in Production

By Andrew Fairless on January 19, 2021December 27, 2020

https://eugeneyan.com/writing/practical-guide-to-maintaining-machine-learning/ A Practical Guide to Maintaining Machine Learning in Production “This follow-up will share some practices I’ve found useful to maintaining machine learning in production. They are: Monitor Training andContinue readingWhat I Read: Maintaining Machine Learning in Production

What I Read: Data Scientists Should Be More End-to-End

By Andrew Fairless on January 17, 2021December 26, 2020

https://eugeneyan.com/writing/end-to-end-data-science/ Unpopular Opinion – Data Scientists Should Be More End-to-End “Eric Colson of Stitch Fix was initially “lured to a function-based division of labour by the attraction of process efficiencies”…Continue readingWhat I Read: Data Scientists Should Be More End-to-End

What I Read: Intro to Data Engineering for Data Scientists

By Andrew Fairless on January 12, 2021December 23, 2020

https://towardsdatascience.com/intro-to-data-engineering-for-data-scientists-fa6c864a3ecc?gi=dd86c8bdeaa0 Intro to Data Engineering for Data ScientistsAn overview of data infrastructure which is frequently asked during interviewsWei WangJul 19 “…I have found that fresh data science graduates often don’tContinue readingWhat I Read: Intro to Data Engineering for Data Scientists

Tag: data engineering