Data engineering has long been a grind. Pipelines stitched together by hand, SQL queries tuned one at a time, integrations glued with fragile scripts. Engineers carry pagers for 2 AM failures, analysts wait days for queries, and new hires spend weeks decoding undocumented jobs.
[.c-warningbox]The result? Slow delivery, hidden tribal knowledge, and mounting pressureas businesses demand faster insights.[.c-warningbox]
Enter the Data Engineering Agent — AI copilots embedded in platforms like Keboola, Snowflake, Databricks, Matillion, and Boomi. They promise to turn natural-language requests into reliable pipelines, SQL, and docs — all inside governed environments. No shadow AI, no shortcuts, just faster delivery with control.
This isn’t about replacing engineers. It’s about changing the rhythm of work: from firefighting and manual toil to guided collaboration, where AI clears the grunt work and humans focus on strategy, design, and quality.
So what do these agents actually look like in practice? Let’s take a closer look at the leading players — one by one — and compare their strengths, focus areas, and use cases. Only then can we understand where they overlap, where they differ, and which might fit your team best.
We’ll start with Keboola, one of the few platforms trying to cover the full spectrum — from pipelines to analytics — with governance at the core.
Picture the all-too-familiar scenario: a mission-critical pipeline fails overnight. Logs are cryptic, Slack is on fire, and the CFO’s dashboard is red.
Keboola Data Agent turns that nightmare into a quick conversation. Ask: “Why did the Salesforce-to-Snowflake pipeline fail yesterday?” The agent reads the logs, identifies the error, and even drafts a safe fix in dev. Documentation updates automatically, capturing the root cause so tribal knowledge doesn’t disappear.
But it’s not just a firefighter. When you need new pipelines, just describe them: “Ingest HubSpot and Salesforce, join by company ID, calculate CAC by cohort, refresh weekly.” Minutes later, a full pipeline is ready for review — transformations, schedule, lineage. Analysts can ask questions directly (“What’s Q3 revenue by region?”) and get charted results with SQL included.
[.c-basicbox]Why it matters: Keboola’s agent combines breadth (pipelines, debugging, docs, analytics) with deep governance — every action is versioned, approved, and auditable. It empowers engineers and analysts, collapsing silos into one governed workspace.[.c-basicbox]
While Keboola emphasizes end-to-end pipelines, Snowflake has taken a different approach: keeping it simple, focusing on SQL, and doubling down on analyst productivity.
In many organizations, SQL is the bottleneck. Analysts have ideas but queue requests; engineers spend cycles translating questions into queries.
Snowflake Copilot closes that gap. Within Snowflake Studio, business users can ask: “Show Q3 revenue by product compared to last year.” Copilot generates SQL, executes it, and shows results. Follow-ups refine the query conversationally. It explains code, suggests optimizations, and answers syntax questions directly from Snowflake docs.
All of this happens inside Snowflake, respecting RBAC and security boundaries. No data leaves, no compliance headaches
[.c-basicbox]Why it matters: Snowflake Copilot is the analyst’s accelerator. It won’t orchestrate pipelines across systems, but it democratizes data analysis within Snowflake — empowering analysts while freeing engineers from ad-hoc ticket queues.[.c-basicbox]
Databricks, on the other hand, approaches the challenge from a developer’s perspective — turning its copilot into a code-focused pair programmer.
New engineers entering Databricks often face sprawling notebooks filled with PySpark they don’t fully understand. Onboarding and debugging are slow, and code reviews take extra effort.
Databricks Assistant changes the dynamic. Highlight code and ask “Explain this” — it breaks down each step in plain English. Paste an error trace, and the Assistant suggests fixes. Need a job scaffolded? “Read CSVs from S3, join with user table, calculate weekly DAUs” produces runnable PySpark.
Because it uses Unity Catalog context, it knows your data assets, reducing mismatches and guesswork. Everything runs inside Databricks, respecting workspace security.
[.c-basicbox]Why it matters: Databricks Assistant isn’t an orchestrator. It’s a developer productivity copilot — reducing debugging time, accelerating coding, and helping teams onboard faster inside the Lakehouse.[.c-basicbox]
And for teams already building jobs visually, Matillion adds a conversational layer to its familiar ETL canvas.
Traditional ETL job design means dragging components, configuring them step by step, and hoping it holds together when requirements shift.
Matillion Copilot (Maia) makes it conversational. Ask: “Load monthly S3 sales data, join Redshift products, calculate growth, publish to Tableau.” Copilot creates the job visually — connectors, joins, aggregations, outputs — all wired correctly. Want to embed sentiment analysis? It adds the AI step at the right place.
Each pipeline comes with an explanation and requires your approval before running, balancing acceleration with control. It proactively adds error handling and quality checks if needed.
Why it matters: Matillion Copilot is agentic ETL — pipelines explained, validated, and governed. For Matillion users, it’s a leap in speed without compromising control.
[.c-basicbox]Why it matters: Matillion Copilot is agentic ETL — pipelines explained, validated, and governed. For Matillion users, it’s a leap in speed without compromising control.[.c-basicbox]
Finally, Boomi broadens the horizon — thinking beyond pipelines and BI, and positioning its AI as an automation fleet for enterprise-scale integration.
Large enterprises often struggle not with one pipeline, but with integration sprawl — thousands of APIs and processes stitched together.
Boomi AI addresses that with a suite of agents:
All governed through AgentStudio, a control tower where IT sets policies, audits activity, and manages lifecycles. Boomi supports open standards like MCP, making its agents interoperable beyond its own platform.
[.c-basicbox]Why it matters: Boomi is about scale and governance. It’s not just one assistant, but an AI automation fleet, best for enterprises aiming for hyperautomation across integration, APIs, and data.[.c-basicbox]
All of these copilots are moving fast, but the story doesn’t end here. The next chapter in data engineering isn’t just about speed — the next frontier is trust, reproducibility, and portability.
That’s where Osiris comes in — Keboola’s open-source deterministic compiler for AI-native pipelines.
With Osiris, you simply describe your desired outcome in plain English. It then compiles that intent into a fingerprinted, production-ready manifest that behaves the same everywhere — in local development, Keboola, or runtimes like Airflow and Prefect.
So where does this leave us? With a set of powerful copilots, each excelling in different contexts. The real question isn’t which is best on paper — but which works best for your team, with your data, in your stack.
But here’s the truth: no online demo or slick launch video can tell you how these copilots will feel inside your workflows. The only way to know is to test them side by side. Spin up a pilot, throw real-world messy data at them, and see which copilot genuinely reduces toil for your engineers and analysts.
👉 Go beyond the marketing. Compare them in practice. Ask each copilot the same questions, run the same pipelines, and stress-test them with your business use cases. You’ll quickly see which tools are ready for daily production — and which still feel like prototypes.
The smart move isn’t waiting for perfection — it’s experimenting now, learning what fits, and building the governance practices that let you harness AI safely and at scale.
