A Lakehouse Without Governance Is Just a Lake.

From Unity Catalog and AI-powered data quality to governed access management, and Microsoft 365 steward integration, Incept delivers the full governance picture. Not just the Lakehouse platform.

Talk to a Databricks Expert

[SERVICES]

Databricks Services
Built on a Governance Foundation

We implement Databricks with governance at the center, not as an afterthought. From Unity Catalog to AI-powered data quality and governed access management, we ensure your Lakehouse delivers trusted data at enterprise scale.

DATA ARCHITECTURE

Lakehouse Architecture and Design

End-to-end Lakehouse architecture design: medallion architecture, Delta Lake schema design, data zone strategy, and platform configuration. We build the structural foundation that makes your Databricks environment scalable, organized, and ready to govern.

DATA GOVERNANCE

Unity Catalog Implementation

Configure and implement Databricks Unity Catalog as your centralized governance layer: metastore setup, data access controls, object-level permissions, and attribute-based access control. We make Unity Catalog the active governance layer it was designed to be, not a default setting left unconfigured.

DATA QUALITY

Data Quality on Databricks

Implement data quality frameworks directly within your Databricks pipelines using Delta Live Tables, Great Expectations, and custom DQ rule engines. We enforce data quality at the point of ingestion so issues do not propagate downstream.

MIGRATION

Databricks Migration and Modernization

Migrate from legacy data warehouses, on-premise Hadoop environments, or other cloud platforms to Databricks. We handle schema migration, pipeline refactoring, workload optimization, and governance layer configuration as part of every migration engagement.

DATA ENGINEERING

Data Engineering and Pipeline Development

Design and build production-grade data pipelines using Delta Live Tables, Apache Spark, and Databricks Workflows. We engineer for reliability, observability, and data quality from the first commit.

AI & MACHINE LEARNING

AI and ML Data Platform Governance

Establish the governed data foundation for AI and ML workloads on Databricks. We implement MLflow governance, feature store management, model lineage tracking, and data access controls that ensure your AI initiatives start from trusted, documented data.

AI & DQ AGENT

AI-Powered Data Quality on Databricks

We build AI-native DQ frameworks on Databricks that use large language models to profile your Unity Catalog tables and generate executable SQL-based DQ rules automatically. Eight data dimensions profiled, rules executed on Spark, quality scored and visualized in real time. Backed by a working POC delivering 91.7% to 96.3% quality scores across enterprise data domains.

GOVERNED ACCESS

Governed Data Delivery and Access Lifecycle

End-to-end governance of who accesses your Databricks data, through what approval process, and when that access is automatically revoked. We implement a full governed data delivery lifecycle using CDMP, CAI, and ServiceNow, ensuring every data access request is validated, approved, delivered to Databricks via Unity Catalog, and tracked.

PLATFORM INTEGRATION

Databricks and Informatica IDMC Integration

Connect Databricks to Informatica IDMC for catalog scanning, metadata synchronisation, data lineage tracking, and governed ingestion pipelines. We design and implement the integration architecture that makes Databricks a governed node in your broader enterprise data management ecosystem.

DATA ENGINEERING

Databricks and dbt Integration

Implement dbt alongside Databricks for governed, version-controlled data transformation. We design dbt project structures, model lineage, and testing frameworks that integrate with Unity Catalog and your governance stack for documented, auditable transformations at scale.

[GOVERNED DATA DELIVERY]

A Full Lifecycle for Governed Access to Databricks

Deploying Databricks is only half the governance story. We implement an end-to-end governed data delivery lifecycle that controls who accesses your Lakehouse, through what approval process, and when that access is revoked.

Data Marketplace Request

Data consumers submit structured access requests through Informatica CDMP, the enterprise data marketplace. Every request captures the requester identity, the target Databricks dataset, the intended use, and the required access duration. No ad hoc access. No undocumented requests.

Governance Gates and Steward Approval

Each request passes through configurable governance gates within CDMP. Data stewards review requests against ownership policies, Unity Catalog data classification levels, and access standards before any data moves. The governance record is created and tracked in the catalog from this point.

CAI Validation and ServiceNow Ticket Creation

Informatica CAI (Cloud Application Integration) validates the approved request: identity verification, duplicate check, and policy compliance. A ServiceNow service ticket is automatically created for every validated request, establishing a full ITSM audit trail alongside the governance record in CDMP.

ServiceNow Approval Workflow

The ServiceNow workflow routes the request through the appropriate approval chain: manager approval, data owner sign-off, and security review where required. Both approved and rejected paths generate OAuth2 callbacks to CAI, updating the CDMP governance record in real time.

Governed Delivery to Databricks

For approved requests, CAI orchestrates governed delivery to the designated Databricks environment: Unity Catalog grants, cluster-level access controls, Delta Lake schema provisioning, and row and column-level security configuration where applicable. Delivery is scoped, documented, and fully traceable back to the original CDMP request.

Automated Access Revocation

Three triggers activate automated revocation: access expiry at the agreed end date, Active Directory leaver detection when a user exits the organisation, and manual revocation by a data steward or security team. Revocation removes all Databricks Unity Catalog permissions and updates the CDMP governance record. No orphaned access. No manual cleanup.

[INCEPT ACCELERATOR]

Incept's AI-Powered
DQ Agent for Databricks

AI profiling. Spark-native execution. Real-time governance visibility. All inside Databricks.

Incept's AI-Powered DQ Agent for Databricks is a pre-built, production-tested data quality accelerator built natively on the Databricks Lakehouse platform. It profiles your Unity Catalog tables using large language models, generates executable SQL-based DQ rules automatically, executes them at enterprise scale on Spark, and delivers real-time quality visibility through Power BI dashboards. The entire DQ engine runs inside Databricks. Your data never leaves the platform.

Component 1

AI Agent. Profile and Generate.

Selects tables from Unity Catalog. Large language models profile 8 data dimensions: null analysis, distributions, patterns, date ranges, duplicates, cardinality, format consistency, and referential integrity. Generates business metadata and column descriptions automatically. Recommends DQ rules with executable SQL for steward review and selection. Exports metadata to Informatica CDGC for catalog alignment (4 Excel files, bulk import ready).

Component 2

DQ Execution. Run and Monitor.

Executes approved rules on Spark across entire datasets, not samples. Pass/fail result per rule with record-level counts. Failed records available for detailed review and CSV download for investigation and remediation. Business exception management: mark records as excluded from the next run. Quality trend tracked over time across every execution cycle.

Component 3

DQ Dashboard. Scores and Trends.

Domain quality gauges showing real-time scores across all governed data domains. Dimension breakdown bars across completeness, accuracy, validity, consistency, timeliness, and uniqueness. Quality trend by run for longitudinal visibility. Failed rules summary table with filter by domain and dimension. All dashboards powered in real time from Delta tables. No stale exports.

Working POC: Live on Databricks

91.7% to 96.3% quality scores across four enterprise data domains

8 DQ dimensions profiled

AI-generated SQL
rules

Spark execution at scale

Real-time Power BI visibility

Microsoft 365 Integration: Stewards Stay Where They Work

The DQ Agent's output reaches your stewards inside the tools they already use. No application switching. No adoption friction.

Teams-Based DQ Approval Workflow | Power Automate

DQ rule failures trigger an adaptive card directly in Microsoft Teams. Stewards approve, reject, or reassign exceptions with a single click inside Teams. Approval executes SQL write-back to Databricks Delta automatically. Full audit trail maintained in Power Automate and Delta tables. No external tool access required for steward action.

Natural Language DQ Queries | Copilot Studio Agent

Stewards ask data quality questions in plain language inside Teams: 'Show me Vendor DQ issues this week.' The Copilot Studio agent queries Databricks SQL directly, returns domain scores, rule results, and anomaly counts in a structured response. SSO via Entra ID. No application switching. No dashboard navigation required.

Org-Aware Steward Routing
| Microsoft Graph API

DQ issues route to the right steward automatically. Domain configuration tables map data domains to departments. Microsoft Graph API finds the right people by role and department, checks availability via Presence API, and escalates to managers if unavailable. People data always live from Graph, never hardcoded in a config file.

Governance Document Management | SharePoint & Excel

CDGC governance exports auto-uploaded to SharePoint document libraries. Rejected records stored in domain-specific folders for investigation. Governance policies searchable via M365 Copilot across the entire document library. Scheduled DQ summary reports delivered via Power Automate on a defined cadence.

Phased Implementation

Phase A

Teams Notifications
2-3 weeks

Phase B

Graph API Routing
3-4 weeks

Phase C

Copilot Studio Agent
4-6 weeks

[Why Incept]

Most Firms Implement Databricks. We Govern It, Validate It, and Connect It.

Data engineering teams build fast. Governance discipline ensures what they build is trusted. Our AI-powered DQ Agent and governed access lifecycle deliver proof, not promises.

Governance Built In, Not Bolted On

The most common Databricks failure mode is deploying a high-performance Lakehouse with no metadata management, no data lineage, and no access governance. We prevent that from the start by making Unity Catalog and data quality frameworks part of the initial architecture, not a follow-on project.

AI-Powered DQ Agent

Our pre-built DQ Agent profiles Unity Catalog tables using large language models, generates executable SQL DQ rules automatically, and executes them on Spark across entire datasets. Backed by a live POC delivering 91.7% to 96.3% quality scores. Data quality at enterprise scale, without manual rule authoring.

Governed Access Lifecycle

We implement the full governed data delivery lifecycle on Databricks: CDMP marketplace request, governance approval, ServiceNow workflow, Unity Catalog delivery, and automated revocation. No orphaned access. Full audit trail from request to revocation.

Unity Catalog Depth

Unity Catalog is Databricks' native governance capability and one of the most underutilised features in live Databricks environments. Our practitioners implement Unity Catalog at the depth it requires: metastore hierarchy, fine-grained access controls, column-level security, and automated lineage capture.

Engineering & Governance in One Team

Most governance firms do not have data engineers. Most data engineering firms do not have governance practitioners. Incept has both, which means your Databricks engagement does not require coordinating two separate vendors to achieve a governed Lakehouse.

Platform-Agnostic Advisory

We are not a Databricks-only shop. Our platform-agnostic positioning means we recommend Databricks when it is the right fit and are honest when a different architecture serves the use case better. That independence gives our technical recommendations credibility.

[PROVEN AT SCALE]

Databricks at Enterprise Scale. Delivered.

[FEDERAL SYSTEMS INTEGRATOR]

1.4 PB

Enterprise Data Environment

Incept designed and implemented a Databricks Lakehouse governance framework for a 1.4 petabyte corporate data environment at a major federal systems integrator. The engagement covered end-to-end Lakehouse architecture on AWS, data catalog implementation enabling enterprise-wide discovery and metadata management, governed ingestion pipelines from diverse source systems, MDM framework development across three core data domains, data quality frameworks and validation processes, and enterprise data governance policies and standards. Delivered as prime contractor.

[CERTIFIED PRACTITIONERS]

Certified Across the
Databricks Platform

Incept practitioners hold active Databricks certifications across
the engineering, architecture, and data science tracks most
relevant to governed Lakehouse implementations.

Databricks Certified Data Engineer Associate

Foundational data engineering on Databricks: Delta Lake, Spark SQL, pipeline orchestration, and Databricks Workflows.

Databricks Certified Data Engineer Professional

Advanced data engineering: production pipeline design, performance optimisation, Delta Live Tables, and enterprise Lakehouse architecture patterns.

Databricks Certified Associate Developer for Apache Spark

Core Spark programming in Python and Scala for data transformation, processing, and pipeline development at scale.

Databricks Certified Machine Learning Professional

ML model development, MLflow tracking, feature engineering, and model deployment within the Databricks ecosystem.

Databricks Certified Data Analyst Associate

SQL analytics, data visualisation, and Databricks SQL endpoint management for governed analytical workloads.

[INTEGRATION ECOSYSTEM]

Databricks Within Your Broader Data Stack

We implement Databricks as part of a connected data architecture. Whether Databricks is your primary analytics engine, your AI foundation, or one component in a broader stack, we ensure it integrates cleanly with the tools around it.

[start with incept]

Ready to Govern Your Databricks Environment?

Whether you are starting a new Databricks implementation, migrating from a legacy platform, deploying AI-powered data quality, or trying to bring governance to an existing Lakehouse, our practitioners are ready to help.

Talk to a Databricks Expert

Contract #: 47QTCA25D008H
SIN: 54151S
UEI: ZRQCNGVLKSF5