AI is Taking Over Data Engineering (and That’s a Good Thing)
Welcome to the New Era of Data Engineering
If you’re working in data engineering, you’ve probably noticed how quickly things are changing.
Gone are the days when we just built ETL pipelines, dumped data into a warehouse, and called it a day. Today, we’re dealing with real-time streaming, AI-driven automation, and next-level governance. And let’s be honest—while it’s exciting, it can also be overwhelming trying to keep up.
But here’s the good news: recent advancements in Databricks, PySpark, Delta Lake, and Unity Catalog are making our jobs not just easier, but way more impactful.
We’re talking about self-optimising pipelines, real-time fraud detection, AI-powered governance, and data formats that just work across multiple platforms—no more vendor lock-in headaches.
So, what does all this mean for us as data engineers? Let’s break it down.
1. AI is Taking Over Data Engineering (and That’s a Good Thing)
For years, our job has been fixing pipelines, tuning performance, and dealing with unexpected failures. But now, AI is stepping in to do a lot of that work for us.
With AI-powered observability in Unity Catalog and Delta Tensor for AI-ready data storage, pipelines can now monitor themselves, detect issues, and even optimise performance dynamically.
How does this work?
AI can spot anomalies in data before they cause issues
Pipelines can self-adjust based on workload patterns
Data quality can be monitored and automatically corrected
AI can suggest governance policies for compliance
Real-World Example
Let’s say you work for an e-commerce company handling millions of orders daily. Instead of manually tweaking Spark jobs to improve performance, AI watches workload patterns and adjusts partitioning, caching, and clustering dynamically. The result? 60 percent faster queries and fewer failures.
2. Real-Time Streaming is Becoming the Norm
We all used to swear by batch processing. It was simple, reliable, and good enough. But now? Businesses expect real-time insights, and batch jobs just don’t cut it anymore.
With PySpark’s Arbitrary Stateful Processing and Delta Lake 4.0, we can now process and transform data in real time instead of waiting for scheduled jobs.
What’s changed?
Delta Lake 4.0 adds Identity Columns for efficient updates
Liquid Clustering optimises storage dynamically
New PySpark UDTFs make streaming transformations easier
Real-World Example
A bank using Delta Live Tables can now detect fraud instantly instead of waiting for batch reports. If an unusual transaction happens, an ML model flags it in real time, preventing fraud before money is even transferred.
3. Governance is Finally Built-In, Not an Afterthought
Let’s be honest—data governance has always been a pain.
It slows things down, creates roadblocks, and feels like something we do just to tick compliance boxes. But now, governance is becoming an enabler, not a burden.
With Unity Catalog’s new AI-driven governance features, we can automate access control, ensure compliance, and manage data across multiple platforms effortlessly.
What’s New?
ABAC (Attribute-Based Access Control): Set policies based on user attributes instead of fixed roles
Lakehouse Federation: Query data across Snowflake, Redshift, BigQuery, and more without moving it
Governed Business Metrics: Define key business metrics centrally, ensuring consistency across reports
Real-World Example
A healthcare company dealing with sensitive patient data can now automatically enforce HIPAA compliance across all datasets. AI monitors for violations in real time, preventing security breaches before they happen.
4. The Lakehouse is Finally Interoperable
One of the biggest pains in data engineering has always been data silos and incompatible formats. But that’s starting to change with XTable and Delta Kernel, which enable true format interoperability.
What This Means for You
Store data in Delta but query it with any engine (Trino, Presto, etc.)
No need to choose between Delta, Iceberg, or Hudi—they all work together
Reduce storage costs by avoiding unnecessary data duplication
Real-World Example
A global retailer can now have teams in different regions using different data formats but still query the same datasets without conversion overhead. That saves millions in storage and processing costs.
5. Open Source is Winning the War Against Vendor Lock-In
The industry is finally moving away from vendor lock-in, and it’s a huge win for us as engineers.
Databricks’ open-sourcing of Unity Catalog means we can now use it as a universal governance layer, even outside of Databricks.
Why This Matters
You can govern all data and AI assets, not just Databricks data
Your organisation gets full control over its metadata layer
Open standards mean easier integrations with new tech
Real-World Example
A media company tracking AI-generated content can now govern ML models, AI prompts, and traditional datasets in one place, without being locked into a single platform.
The Future of Data Engineering is Here—Are You Ready?
The role of a data engineer is evolving fast. We’re not just moving and transforming data anymore. We’re enabling real-time AI, self-optimising systems, and smarter governance.
Key Takeaways
AI is automating pipeline optimisation and anomaly detection
Real-time data streaming is now essential, not optional
Governance is shifting from a blocker to an enabler
Interoperability and open-source tools are taking over
So the big question is—are you ready to embrace this new future?