My data engineering team at Fossil recently released some of Rust-based components of our Data Platform after faced performance and maintenance challenges of the old Python codebase. I would like to share the insights and lessons learned during the process of migrating Fossil's Data Platform from Python to Rust.
This post provides a quick and easy guide on how to run Apache Spark in GitHub Actions for testing purposes
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.
More than 200+ companies are using ClickHouse today. With many features support, it's equally powerful for both Analytics and Big Data service backend.
Postgres has built-in functions to handle Full Text Search queries. This is like a "search engine" within Postgres.