This post provides a quick and easy guide on how to run Apache Spark in GitHub Actions for testing purposes
I've been using Gastby since 2019 and while it's great that I can write my blogs in NeoVim, commit and push to Github, and have Cloudflare Pages build and publish to CDN, but I was becoming frustrated with the slow building times and the overall maintenance requirements.
Why does Helm Charts interpret 0777 to 511? It took me quite some time to debug it.
The field of Natural Language Processing (NLP) has seen remarkable advancements in recent years, and the emergence of the Generative Pre-trained Transformer (GPT) has revolutionized the way NLP models operate. GPT is a cutting-edge language model that employs deep learning to generate human-like text. Unlike conventional NLP models, which required extensive training on specific tasks, GPT is pre-trained on vast amounts of data and can be fine-tuned for various NLP tasks
I asked ChatGPT to explain 20 important concepts of Apache Spark. Let's see what it has to say!
If you're interested in data engineering with Rust, you might want to check out Polars, a Rust DataFrame library with Pandas-like API.
This blog post will provide an overview of the data engineering tools available in Rust, their advantages and benefits, as well as a discussion on why Rust is a great choice for data engineering.
Recently, I was working on building a new Logs dashboard at Fossil to serve our internal team for log retrieval, and I found ClickHouse to be a very interesting and fast engine for this purpose. In this post, I'll share my experience with using ClickHouse as the foundation of a light-weight data platform and how it compares to another popular choice, Athena. We'll also explore how ClickHouse can be integrated with other tools such as Kafka to create a robust and efficient data pipeline.
Airflow since 2.4, in addition to scheduling DAGs based upon time, they can also be scheduled based upon a task updating a dataset. This will change the way you schedule DAGs.
There are several scenarios when you will need to override or patch upstream dependencies. Like testing a bugfix of your crates before pushing to crates.io, a non-working upstream crate has a new feature or a bug fix on the master branch of its git repository that you'd want to try, etc. In these cases, the [patch] section of Cargo.toml might be useful.