Session

Genie ZeroOps for Data Quality and Compliance at Scale

Overview

Experience	In Person
Track	Governance & Security
Industry	Enterprise Technology
Technologies	Genie
Skill Level	Intermediate
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Organizations have more data than ever — but less confidence to use it. As data estates scale, manual quality checks and compliance rules can't keep up. A stale table feeding a revenue forecast isn't just a data problem, it's a problem feeding critical business decisions. PII in an unmonitored catalog isn't a governance gap, it's compounding compliance risk.

In this session, we'll introduce Genie ZeroOps: a background agent that watches your entire data estate so that when issues arise, a proposed fix is already waiting for you. Built on insights from Data Quality Monitoring and Data Classification, ZeroOps detects anomalies and PII across your assets, traces root cause through lineage, and drafts the fix with a validation loop.

In a live demo, you'll see a data engineer resolve a broken revenue dashboard in two minutes, and a governance lead close a PII exposure risk with a single confirmed policy. Both get centralized visibility across quality and compliance in the Governance Hub.

The agent detects, investigates, and fixes. All you need to do is review and accept. That's ZeroOps.

Session Speakers

IMAGE COMING SOON

Jacqueline Li

/Product Manager
Databricks

Viswesh Periyasamy

/Staff Software Engineer
Databricks

Full Summary

Scaling data quality and compliance with Genie Zero Ops

Enterprises now operate data estates so large that hand-written checks and scattered scripts cannot keep pace. Quality regressions break dashboards, compliance gaps propagate downstream, and engineers lose hours tracing root causes across complex lineage.

The session introduces a platform-native approach on the Databricks Platform that builds proactive data governance into daily operations, spanning detection and remediation. The Genie Zero Ops announcement blog covers the background agent in depth for teams evaluating how it fits into existing workflows.

Two estate-wide detection capabilities, data quality monitoring and data classification, establish continuous visibility without custom rules. A new background agent, Genie Zero Ops, closes the loop by correlating signals, proposing fixes, validating them in a sandbox, and surfacing projected impact for human approval.

Estate-wide monitoring built in, not bolted on

Relying on bespoke rules for a few critical tables leaves most of the estate unobserved. As tables, jobs, and downstream uses multiply, blind spots and audit effort grow. Databricks addresses this with detection that runs across catalogs, not table by table.

Data quality monitoring, in public preview, learns each table's historical behavior and flags staleness, missing rows, and related anomalies without manual thresholds. Scan cadence aligns to how data actually updates.
Data classification, generally available, scans new and changed data for PII and sensitive fields, maps detections to standards like GDPR, HIPAA, and CCPA, and supports custom classifiers for organization-specific tags. Smart, incremental scanning scales to large estates.

Both surface in a Govern menu within Catalog Explorer, giving a single view of healthy versus unhealthy assets, owners, root causes, and downstream impact. Built on Unity Catalog metadata and lineage, they adapt to real usage patterns instead of static schedules.

From alert to validated fix with Genie Zero Ops

Finding issues is only half the battle. Root causing failures across tangled dependencies and shipping safe changes is the time sink. Genie Zero Ops, entering private preview in July, runs as a platform-native background agent that continuously pulls detection signals, correlates them through lineage, and prepares a proposed remediation before anyone logs on.

The agent respects permissions, quantifies blast radius, and validates proposed changes in a sandbox. Engineers stay in the loop to review, iterate, and approve. Demonstrated scenarios included a null spike traced to a deprecated upstream column with guidance in column metadata, leading to a drafted code change that repopulates rows, and a compliance case where unmasked PII exposed to a large user base triggered a proposal to tag tables by sensitivity tier and apply an ABAC policy. The governance lead refined the policy live, excluding the security team, while seeing projected impact update before applying.

Governance visibility, grounded in platform context

Accuracy and speed come from platform-native context. Unity Catalog's metadata, lineage, governance policies, and historical failure patterns ground detection and remediation. A Governance Hub provides an account-level view across usage, compliance, security, and health, with Zero Ops insights bubbling up so leaders can track exposure and remediation progress.

Generic coding agents must be fed workspace and lineage details manually. Zero Ops inherits context by default, which enables more reliable root cause analysis, safer proposed fixes, and clearer impact estimates that help teams act with confidence.

What ships when

Data quality monitoring is moving to beta in the fall with quality checks by slice, then expanding by year end with more out-of-the-box checks and deeper integration with Zero Ops for root cause and impact analysis. Data classification will take custom classifiers to public preview by the end of summer, broaden compliance coverage to include the full breadth of HIPAA and additional regions, and continue adding built-in classifiers, with integration to Zero Ops by year end. Zero Ops enters private preview in July, while today's detection features are available in Catalog Explorer under Govern.

Automating trust without sacrificing control

Platform-native detection paired with human-in-the-loop remediation turns firefighting into review and approval. Estate-wide coverage finds issues early, and Zero Ops arrives with evidence, validation, and projected impact so teams can resolve overnight without ceding control.

FAQ

Genie Zero Ops is scheduled for private preview in July. Interested teams can join via the session's QR code. The detection features that feed Zero Ops, data quality monitoring and data classification, are already available through the Govern menu in Catalog Explorer.

No. The workflow keeps a human in the loop. Zero Ops drafts a fix, validates it in a sandbox, and shows projected impact, then an engineer or governance lead reviews, iterates if needed, and approves before creating a PR or applying a policy.

General agents require users to supply context about workspaces, assets, and lineage. Zero Ops is built into the Databricks Platform and uses Unity Catalog metadata, lineage, permissions, and historical patterns by default, which improves root cause accuracy and the safety of proposed changes.

No. Data quality monitoring is enabled at the schema or catalog level and learns historical patterns to flag anomalies like staleness or missing rows. Scans align to update frequency, reducing scheduling overhead while providing baseline coverage.

Yes. Custom classifiers let teams point to sample columns that contain values to tag, such as internal account numbers or device fingerprints. The system learns from data and metadata, and custom classifiers are moving to public preview by the end of the summer.