The Autopsy of a Digital Graveyard

In this video, I talk about something I’m seeing more and more across data platforms —
we’ve invested heavily in tools like Snowflake, Databricks, and Kafka… but we’re still running them manually.

And that’s the real gap.

This is where the idea of an Autonomous Data Platform comes in — shifting from reactive, manual data engineering to systems that can monitor, fix, and optimize themselves with AI support.

Here’s what I break down in the video:

🔹 Where transformation actually happens
It’s not just about tools — it’s about how you use them.
I walk through how teams are using:

AI-assisted pipeline monitoring

Automated orchestration (Airflow, dbt, Fivetran)

Data observability platforms (Monte Carlo, Databand)

…to catch issues before they become incidents.

🔹 What “good” really looks like in operations
A lot of teams say their platform is stable — but very few measure it properly.
We talk about:
Why DataOps + CI/CD should be non-negotiable
How to define real SLAs
What strong KPIs look like in practice:
✔️ 99% pipeline success rate
✔️ 98% data quality
✔️ 30 mins incident resolution
🔹 Where things are heading globally
This space is moving fast.We’re seeing:
Zero-ETL architectures reducing data movement
AI agents managing pipelines
Natural language analytics (like Snowflake Cortex, Power BI Copilot) replacing traditional dashboards

And in many cases, this is cutting operational effort by 60–80%.

💡 The bottom line:
The future isn’t more pipelines.
It’s fewer manual interventions.

Platforms where:

Issues are detected automatically
Fixes are triggered without human involvement
Teams focus on outcomes, not firefighting
If you’re working on modern data platforms, this shift is already happening.
Curious to hear — how is your team moving towards automation today?

Видео The Autopsy of a Digital Graveyard канала DATA_DELIVERY AND TRANSFORMATION