In 2017, we redefined "Data Engineering." Today, we're at a similar inflection point for the Semantic Layer.

In 2022 (MDS R.I.P.), we treated it as a fancy SQL generator: Thousands of YAML lines to calculate metrics for dashboards.

Deep down, we all knew it was overkill. 1/4

We've solved observability for dashboards. Now we need to solve it for agents.

Had a great time writing for the Metadata Weekly newsletter about the shift from Data Trust to Decision Trust:

metadataweekly.substack.com/cp/179547368

Smart Brevity by Jim VandeHei, Mike Allen, and Roy Schwartz should be mandatory reading before you're allowed to send your 1st email/Slack message.

And it's more vital than ever now, since we upgraded from "human-generated fluff" to "AI-supercharged fluff". 1/2

The dbt Labs-Fivetran merger is such a Logan Roy move by Fivetran - they now own all three data transformation tools that emerged from the MDS: dbt, SQLMesh, and SDF (via dbt Labs).
Post-MDS world is full of surprises

Excited to share that my masterclass, "Data Modeling for Data Products," is now available on-demand via ModernData101!

If you're a Data PM, Analytics Engineer, or Data Engineer focused on building valuable (& scalable) data products, this session is for you! 1/2

Peak Paris is going to a (fantastic) contemporary dance show at a department store - (Babel at Le Bon Marché👌🏼)

Very interesting article by @apachedatafusion.bsky.social team on user-defined/custom indexes in Parquet - really surprised that other Parquet readers/writers don't leverage this, given that file pruning remains limited with "vanilla" Parquet in many scenarios.
datafusion.apache.org/blog/2025/07...

Data Espresso #12 is out ☕
This edition covers:
- A modern playbook for data modeling in a product-driven world
- Why we need a "Spotify Wrapped for everything"
- A two-step formula for building data products that actually matter
dataespresso.substack.com/p/espresso-1...
1/4

Data modeling is back (and it's good news!), but we can't just copy-paste the old playbook. Instead, there's a big opportunity to adapt existing data modeling methodologies to today's world: data products, limitless compute, and a big need for speed. 1/2

Today's data platforms are powerful, but the actual experience is still clunky (tool hopping, siloed metadata, etc.). The missing piece is the last mile/experience layer – a polished UI/UX layer connecting all the backend systems. A great example is Airbnb's data timeliness UI: shorturl.at/3n5w9
1/5

This is a great post by the Discord data team about how they augmented several dbt features like materializations and macros.

The "meta" attribute continues to be severely underused by data teams, but they neatly leverage it here for custom versioning.
discord.com/blog/overclo...

Data Espresso #10 is out ☕️
This edition covers:
- If/when you should migrate to Apache Iceberg
- The benefits of revisiting design decisions (and why you should do it often)
- How constraint-heavy environments can foster engineering ingenuity
open.substack.com/pub/dataespr...
1/4

Now that the semantic layer is back in the data space's spotlight, it's definitely worth revisiting adjacent concepts like metric trees. If you're unfamiliar with the term, Trace has a fantastic (and brief) intro that's worth your time: www.hellotrace.io/blog/introdu...

Ever since I started working in data back in 2017, I've heard the same prediction every year: "next year will be the year of streaming!" - years came and went but the much-hyped real-time analytics revolution never materialized. However, I genuinely think this may be THE year - hear me out:

What they don't tell you about build vs buy:

Great news for dbt users (esp. dbt Core users who haven't gotten a "significant" upgrade in a long while) - many workflows & use cases will become possible or much easier to implement.

Now it's a matter of execution & focusing on the right issues - but data space is starting 2025 on a high note!

Earlier this year, I transitioned to Product Management after 7 years in Data Engineering. What surprised me the most when making the switch is the lack of content on navigating career pivots within tech, even though there's an abundance of "tool X vs. Y" content. 1/4

Data folks as we join Bluesky en masse this week 🥹