Delta Live Tables

Beyond the Dashboard: Why AI Data Assistants Fail Without Unity Catalog Governance

For years, the promise of self-service BI has been a moving target. We gave business users drag-and-drop tools, semantic layers, and natural language query bars. Yet, time and again, we hit the same wall: trust. When a business user asks an AI tool, "What was our fleet downtime last quarter?" , the AI is only as good as the underlying data fabric. If your data is trapped in fragmented silos, uncatalogued, and un-governed, conversational AI quickly becomes an expensive generator of confident misinformation. This is where the paradigm is shifting. With the introduction of tools like Databricks Genie , we are moving past rigid dashboards into the era of conversational analytics. But as Solution Architects, our job isn't just to look at the UI layer—we have to look at the spine of the architecture. Here are three architectural realities I’ve learned while implementing modern Lakehouse patterns: 1. AI Without Governance is Just Automated Chaos You cannot point an LLM at a raw storage bucket and expect accurate insights. Genie relies entirely on the metadata, table descriptions, and foreign key relationships defined in Unity Catalog . If your governance layer is messy, your AI's answers will be messy. Unity Catalog isn’t just a security tool anymore; it is the semantic foundation for enterprise AI. 2. The Shift from "Building Pipelines" to "Curating Context" Tools like Lakeflow Connect and automated ingestion frameworks are taking over the heavy lifting of ETL. As data engineers and architects, our value is no longer measured by how many custom Spark scripts we write to move data. Our value is measured by how well we curate the Gold layer —structuring tables, defining clear column descriptions, and setting up quality expectations so that tools like Genie can actually understand the domain context. 3. Real-Time Trust Beats Real-Time Speed In heavy industries—like managing IoT telemetry for gas turbines—getting data in 5 seconds doesn't matter if the data quality is flawed. Integrating Delta Live Tables (DLT) Expectations as a quality firewall before data hits the consumption layer ensures that both human analysts and AI assistants are querying verified, pristine data. The Takeaway Conversational AI and natural language agents like Genie are the future of data consumption. But they are built on a simple architectural truth: Garbage in, hallucination out. If we want business users to trust AI-driven insights, we have to start by modernizing our foundations with robust governance, automated pipelines, and strict data quality gates. Curious to hear from other architects out there: Have you started deploying conversational AI tools to business users yet? What are the biggest governance or data trust hurdles you've had to overcome? submitted by /u/SalamanderOpen859 [link] [comments]

Change in DLT Pipeline Deletion Behavior associated Tables Not Being Deleted

Avoid DLT infinite loops by targeting specific table names

Hi, Databricks Engineer at Abilytics here. If you use Delta Live Tables with wildcard streaming reads like spark.readStream.table("events_*"), your pipeline can trigger unexpected full refreshes or infinite dependency loops when DLT infers schema evolution on newly matched upstream tables. Instead, explicitly declare your dependencies using the live schema namespace. Reference tables directly by their exact identifier, such as spark.readStream.table("live.events_bronze"). This ensures the DAG resolver builds a deterministic execution graph without misinterpreting wildcard patterns as dynamic schema changes during optimization. If this is useful, there's more on Databricks | Platform Engineering | AI at Abilytics — AI Systems, Data Engineering & Platform Engineering Services. submitted by /u/AbilyticsEng [link] [comments]

PSA: old Databricks names vs new ones. Which rename got you?

Databricks has renamed a lot of features over the past year, and a lot of tutorials, blog posts, Stack Overflow answers and even AI assistants still use the old names. If you search the docs for the old name, the results can be confusing, and code samples can look unfamiliar even when you know the concept cold. The ones I see trip people up most: Delta Live Tables / DLT → Lakeflow Spark Declarative Pipelines (often shortened to Lakeflow Declarative Pipelines) APPLY CHANGES INTO → AUTO CDC APIs Databricks Asset Bundles / DABs → Declarative Automation Bundles The concepts didn't change much, but the vocabulary did. Two questions for the sub: Has a rename caught you out, at work, in docs, or in a code review? Did I miss any? I'm sure there are more, especially on the ML / GenAI side. I'll keep this list updated with whatever people add. Edit: removed Liquid Clustering from the list. Fair point from a commenter that it's a change in recommendation, not a rename. Still worth knowing though: older material treats partitioning + Z-Order as the default, and current docs recommend Liquid Clustering for new tables. submitted by /u/UnluckySugar610 [link] [comments]

Data Quality in Microsoft Fabric – Native features vs. external tools (like Databricks Expectations)?

Hey everyone! I’ve been working a lot with data quality frameworks in Databricks (leveraging things like Delta Live Tables expectations or custom validation notebooks), and I'm curious about how the community is handling Data Quality in Microsoft Fabric. Does Fabric have a native, built-in feature for data quality/expectations (similar to what Databricks offers), or are most of you relying on external tools, libraries (like Great Expectations/Soda), or custom PySpark/SQL validation notebooks inside Data Engineering pipelines? How are you currently enforcing quality checks before data hits your Gold/Semantic layers in Fabric? Would love to hear your approaches and best practices! I read this documentation here: https://learn.microsoft.com/en-us/purview/unified-catalog-data-quality-fabric-lakehouse?WT.mc_id=510336 , but it doesn't say much. submitted by /u/SuperbNews2050 [link] [comments]

Live Table Vs custom made framework

Hi everyone. I’d like to know how you’ve been approaching your new Databricks projects, especially regarding Delta Live Tables. For new data environments, are you implementing projects using Delta Live Tables, or do you continue to develop using your own custom Spark code? Late last year, I completed a project using Delta Live Tables. There were a few things that bothered me, such as: - not being able to use conditional logic within the Live Table code; - materialized views—despite offering incremental refresh capabilities—often ended up performing full refreshes anyway; - the inability to specify which metadata columns should be updated during the merge process. I’m about to define the architecture for some clients who use Databricks, and I’m torn between using my custom framework built with Spark code or migrating to the industry-standard approach of using Live Tables. submitted by /u/warleyco96 [link] [comments]

DLT Pipeline - Overwrite except for one Append table