Reading diary

Reading · 2024

Notes from 2024. 39 entries.

Jump toDecember 2024

December 2024

10 notes

November 2024

5 notes

Tuesday

November 26

October 2024

6 notes

September 2024

12 notes

Sunday

September 29

Copy-on-Write (CoW) — pandas 2.2.3 documentation

  • Ptty big change to pandas 3.0 but one I think will bring a lot of clarity to data transformations

Announcing DuckDB 1.1.0 – DuckDB by The DuckDB team


Introducing Contextual Retrieval \ Anthropic


What is a Vector Index? An Introduction to Vector Indexing by Alejandro CantareroField CTO of AI, DataStax


Chunking · Malaikannan


Chronon - Airbnb’s End-to-End Feature Platform - InfoQ by Nikhil Simha

  • This is a very high level overview of the feature platform but allowed me to get a better sense on why use this platform. But I’d love to test it to understand if for those without streaming I’m this isn’t a bit overkill

Friday

September 20

Saturday

September 14

Friday

September 13

Tuesday

September 3

Production-ready Docker Containers with uv by Hynek Schlawack

Many of us can save a child’s life, if we rely on the best data - Our World in Data by By: Max Roser

  • Another article that I find eye opening when we look at data to improve our decisions

Why I Still Use Python Virtual Environments in Docker by Hynek Schlawack

  • Suddenly this way of working, very similar to node modules, is being talked everywhere.

Python Developers Survey 2023 Results

  • Was looking for this for a long time. Got to learn a bit more on twine, mlflow and sqlmodel. In terms of getting on track with python world I think I’m good with my current rss feed and podcast

Elasticsearch is Open Source, Again | Elastic Blog by ByShay Banon29 August 2024

Monitor data quality in your data lake using PyDeequ and AWS Glue | AWS Big Data Blog

  • Data drift detection 👀
  • Makes sense. Pydeequ is just a wrapper

How top data teams are structured by Mikkel Dengsøe

  • As I work on a team that doesn’t have this kind of distribution I need to reflect a bit on the impact of being the sole data engineer on an ML team

Talks - Amitosh Swain: Testing Data Pipelines by PyCon US

August 2024

3 notes

Monday

August 26

uv: Unified Python packaging

Wow, need to test this out on my side projects.

CSS finally adds vertical centering in 2024 | Blog | build-your-own.org by James Smith

Timeless Skills For Data Engineers And Analysts by SeattleDataGuy

Skimmed the article but although high level I find the main points very true. Understanding the system, being on top of the state of the art. That and the tips for head of data

CSS 4, 5, and 6! With Google’s Una and Adam by Syntax

Great episode! Nice to see that css is getting better and better

Friday

August 23

I’ve Built My First Successful Side Project, and I Hate It by Sebastian Witowski

Wow, I’d love this one man saas but knowing how it can burn you out…

NodeJS Evolves by Syntax

Nice episode on the long sought features for node that have been introduced on bun and demo (single file, typescript support and top await async are the big ones for me)

Talks - Brandt Bucher: Building a JIT compiler for CPython by PyCon US

Python Insider: Python 3.13.0 release candidate 1 released

Great to see a new version coming along! Is pdb worth using with vs code? 🤔

Google kills Chromecast, replaces it with Apple TV and Roku Ultra competitor | Ars Technica by Samuel Axon

As a google to owner would be good to know that my working hardware wouldn’t brick just because I don’t want to upgrade

Tuesday

August 6

Can Reading Make You Happier? | The New Yorker by Ceridwen Dovey

Beyond Hypermodern: Python is easy now - Chris Arderne

Although I had my eyes already on rye I certainly didn’t know it was so full feature. Gotta try and change some of my projects to it ,

Visual Studio Code July 2024 by Microsoft

Python support slowly getting good

Introducing GitHub Models: A new generation of AI engineers building on GitHub - The GitHub Blog by Thomas Dohmke

Creativity Fundamentally Comes From Memorization

Gen AI Increases Workloads and Decreases Productivity Upwork Study Finds - InfoQ by Sergio De Simone

The report raises a good point about an increase in workload. With a big productivity boost bosses might start loading even bigger workloads than what we gained from the productivity boost

Ofc this isn’t good as people will feel overloaded

tea-tasting: a Python package for the statistical analysis of A/B tests | Evgeny Ivanov by Evgeny Ivanov

Super interesting IMO. Might give it a try on my team when we start deploying solutions to our clients

Data Science Spotlight: Cracking the SQL Interview at Instacart (LLM Edition) | by Monta Shen | Jul 2024 | tech-at-instacart by Monta Shen

Would be interesting to test this out. Have an example of a dataset that can be queried using duckdb. Given a question understand if a query is correct how to fix it and improve it’s performance. One in Sal and another in pyspark (or ibis/pandas)

July 2024

3 notes

Friday

July 26

Let’s Consign CAP to the Cabinet of Curiosities - Marc’s Blog by Marc Brooker

Interesting topics to research a bit more on CAP alternatives

Why Your Generative AI Projects Are Failing by Ben Lorica 罗瑞卡

In summary, to have a good AI product we need to have data with quality which requires good data governance. With this data we need to define useful products that we can measure it’s value using data driven metrics. We must also ensure the product has good practices avoiding security or bias issues

Engage your audience by getting to the point, using story structure, and forcing specificity – Ian Daniel Stewart

Resumed by:

storyline talk

Slack Conquers Deployment Fears with Z-score Monitoring - InfoQ by Matt Saunders

This is something I would love to implement. Allowing to define the metrics on which to evaluate a new feature, the expected hypothesis and revert the feature (i.e feature flags) automatically with a report on the experiment

DuckDB + dbt : Accelerating the developer experience with local power - YouTube

Could I replace Athena with this? I think the main blocker for me is I want to work with S3. And need to check how it runs for a really large dataset…

How Unit Tests Really Help Preventing Bugs | Amazing CTO

Good tip. For any project define the metric of code coverage goals and start increasing on the project

Mocking is an Anti-Pattern | Amazing CTO

Building an open data pipeline in 2024 - by Dan Goldin by Dan Goldin

Thursday

July 25

Spark-Connect: I’m starting to love it! | Sem Sinchenko by Sem Sinchenko

This article wasn’t properly parsed by omnivore but the big takeaways:

  • We can add plugins for extended functionality to our spark server
  • Using spark connect we can implement any library in any language we want and send grpc requests to the server (spark connect server needs to be running)
  • spark connect works on >3.5. Should be much better on v4.0
  • If I truly want to be good in spark I eventually need to relearn scala/java
  • Glue is cool but it’s still on version 3.3. All these goodies will take too long to be implemented in glue

So you got a null result. Will anyone publish it? by Kozlov, Max

After reading the statistics books I can see much more clear the value of proving a null hypophesis, this is the feeling I am getting out of the academia. We are seeing more research without any any added value. Goodhart’s law.

Maestro: Netflix’s Workflow Orchestrator | by Netflix Technology Blog | Jul, 2024 | Netflix TechBlog by Netflix Technology Blog

Sounds just like an airflow contender. with the plus of being able to run notebooks 🤔

How to Create CI/CD Pipelines for dbt Core | by Paul Fry | Medium by Paul Fry

Simplify PySpark testing with DataFrame equality functions | Databricks Blog by Haejoon Lee, Allison Wang and Amanda Liu

Good theme for a blog post on the changes of spark 4, this is really useful for human errors (been there multiple times)

How to build a Semantic Layer in pieces: step-by-step for busy analytics engineers | dbt Developer Blog by Gwen Windflower

So this will be generated on the fly as views by the semantic layer?, This looked neat until the moment I understood that the semantic layer requires dbt cloud

Meta’s approach to machine learning prediction robustness #### Engineering at Meta by Yijia Liu, Fei Tian, Yi Meng, Habiya Beg, Kun Jiang, Ritu Singh, Wenlin Chen, Peng Sun

Meta seems to be a couple of years ahead of the industry. The article doesn’t provide a lot of insights but gives a feeling of their model evaluation being mostly automated + having a good AI debug tool

Free-threaded CPython is ready to experiment with! | Labs

First step in a long way before we can get run python without GIL. Interested on seeing if libraries like pandas will be able to leverage multithreading eventually with this

Amazon DataZone introduces OpenLineage-compatible data lineage visualization in preview | AWS Big Data Blog

Mixed feelings here. Great to see Open Lineage implemented at AWS. However it feels again that AWS just created the integration and won’t be driving the development of open lineage

Unlocking Efficiency and Performance: Navigating the Spark 3 and EMR 6 Upgrade Journey at Slack - Slack Engineering by Nilanjana Mukherjee

What could be improved to help this kind of migrations be done in a matter of days?, Livy might be deprecated in favor of spark connect. With their migration to Spark 3 and eventually 3.5 (not clear on this article) they could be interested in moving new jobs to connect , Basically solved issues by using the old behaviours. These will need to be migrated eventually. Would need to better understand these features , This looks like an important detail. With no explicit order spark can have random order of rows?, Cool to see these migrations and teams using open source solutions. EMR although expensive with a good engineering team can prove to be quite cost effective

DuckDB Community Extensions – DuckDB by The DuckDB team

Visual Studio Code June 2024 by Microsoft

The Rise of the Data Platform Engineer - by Pedram Navid by Pedram Navid

The need to define a data platform is something I see everywhere. It really looks like we are missing a piece here. Netflix maestro for example seems like a good contender for to solve the issue of (yet another data custom platform)

Lambda, Kappa, Delta Architectures for Data | The Pipeline by Venkatesh Subramanian

Write-Audit-Publish (WAP) Pattern - by Julien Hurault by Julien Hurault

This articles brings me the question. Can we improve dbt by using WAP? How does the rollback mechanism work when a process fails?

AWS Batch Introduces Multi-Container Jobs for Large-Scale Simulations - InfoQ by Renato Losio

Data Council 2024: The future data stack is composable, and other hot takes | by Chase Roberts | Vertex Ventures US | Apr, 2024 | Medium by Chase Roberts

Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions | AWS Big Data Blog

Super interesting to see how we can enable data quality visibility

Pyspark 2023: New Features and Performance Improvement | Databricks Blog by in Industries

Cost Optimization Strategies for scalable Data Lakehouse by Suresh Hasundi

Good to use open data lakes showing the big cost and speed improvements

Prompt injection and jailbreaking are not the same thing

Flink SQL and the Joy of JARs by ByRobin MoffattShare this post

Catalogs in Flink SQL—Hands On by ByRobin MoffattShare this post

Catalogs in Flink SQL—A Primer by ByRobin MoffattShare this post

Why is this so hard? 😭