Well, on thursday I went through my first international talk at Coalesce where I’ve talked about dbt in a data mesh world (basically it’s trino + data mesh + dbt at talkdesk). Quite exciting 😄

Data Engineering

• Should you put several event types in the same Kafka topic? - Good tips on how to separate kafka topics
• How Airbnb Built “Wall” to prevent data bugs - airbnb take on data quality tests
• Why query planning for streaming systems is hard - I’ve been working with streaming pipelines (in scala) and I gotta say that good SQL in streaming would be much better to simplify some workflows
• Hudi, Iceberg and Delta Lake: Data Lake Table Formats Compared - a good take on which table format to use, I’m currently focusing on iceberg it’s good to see competition in the field
• Hive Metastore – Why It’s Still Here and What Can Replace It? - another article from lakefs, this one gives more of a history review to where we are
• Democratizing Metric Definition and Discovery at Airbnb - airbnb has developed a framework to generate metrics from configs and automate and streamline the whole process
• The missing piece of the modern data stack - another take on why we need a metrics layer
• Upgrading to 1.0.0 - dbt has finally gotten it’s first stable release! We’ve gotten a ton of features but I’m exited for the metric layer and where it leads

Engineering

• Cleaning Up Git History - I’ve more recently gotten into the problems of how to manage a repo with multiple contributions and having a clean history can really help
• Ship / Show / Ask - This is a review on how to balance between shipping immediatly and waiting for peer-review
• What’s New In Python 3.10 - Python 3.10 has been some time ago but I hadn’t taken the opportunity to read the release notes. Pattern matching is really exciting as we see ittaking some ideas from languages like scala