Some thoughts:

A "bubbling" topic right now is conversational analytics (i.e. talk to your data). There has been an explosion of tools in the last 6 months. YC is backing one too: https://getnao.io/

I feel like pandas is also somewhat frowned upon, the industry has moved on from that. Most SQL tools can now do everything that we could only do with pandas.

In my network everyone is talking about DuckDB. As long as you are under a 1TB it will have everything you need. I think most people should start with that vs locking themselves into something like Snowflake

A bit of a pedantic nit here: a data warehouse is a usage pattern. It’s not necessarily tied to any specific technology, however it is commonly implemented with OLAP systems like Snowflake, BigQuery, etc. But there’s nothing stopping you from building out your data warehouse in Postgres or MySQL. If you’re stitching together disparate datasets to build a unified model for analytics, you’ve got yourself a data warehouse no matter what system it lives on.

A few suggestions:

- Add a "last updated" note, since this space changes often (see the prefect/dagster situation that just happened)

- Add a note about MCPs and other LLM-driven tools and features are becoming more and more important (e.g. hex.ai or the various MCPs shipped with some of the tools you mention, such as OpenMetadata)

- Maybe organize the various tools you mention briefly by their license/model (lots of them can be self hosted, some are SaaS only), since a fully self-hosted data platform is (at least for now...) very much feasible

I also wish more people would talk more about the "engineering" part of "data engineering". I've seen way too many people who claim a title like "data engineer" but lack the fundamentals of building software and are really just copy-paste scripts together.

What I'd love more DEs to think about are things like {unit,integration,e2e,performance} tests, deployments, infrastructure, networking, monitoring (you do touch on that), and all the other things a regular SWE is expected to have at least basic competency in at a certain level. For instance, tools like dbt natively support tests, but people need to write them. Or how you don't have to click-ops Airbyte, there's a terraform provider etc.

For query engine you can use, for example, Apache Spark, Trino, or Amazon Athena."

DuckDB is eating the query engines and catalogs. Really could use more coverage on how DuckDB is changing the data tools landscape.