You've chosen Databricks. Now let's get your investment to work: pipelines live, data ready, and your first workload in production within 90 days.
NewClaude implementation as a registered Claude partner · Informatica CDGC & CDQUse cases in production bring the real value. We take one real use case all the way up, with reliable pipelines and data quality embedded from the start.
We assess your environment, prioritize use cases, scope the data tightly and select one realistic, high-impact use case. Output: a clear execution plan with controlled scope.
Pipelines built through the medallion layers, reliability issues fixed at the source and performance optimized. Output: working, validated pipelines.
AutoDQ is included in the engagement, free. It runs natively on Databricks with Unity Catalog, generates and enforces rules automatically, and catches issues before downstream impact.
Validated with business users, with a clear path to scaling usage. Not a POC. A live workload.
KData is a registered Claude partner. We help enterprise teams put Claude to work: choosing the right use cases, connecting Claude securely to their data and tools, and rolling it out with governance and adoption built in.
Find where Claude creates measurable value, from document-heavy work to analysis and engineering.
Connect Claude to your data and tools with the right access controls in place.
Usage policies, data handling and human review designed in from the start.
Enablement and rollout so teams keep using Claude well, beyond the pilot.
Already on Databricks? We implement Claude alongside your governed lakehouse, so AI works with the same trusted data, definitions and access controls as the rest of your platform.
Plan a Claude pilotKData is a Canadian, Databricks-focused consulting partner, based in Toronto and Montréal, that helps enterprises move from pilots to production. We build reliable pipelines, governed lakehouse architectures and production-ready use cases directly in our clients' environments, delivering real outcomes, not just prototypes.
The patterns are consistent. We clear these first.
Not production-grade. Constant rework.
Poor modeling limits consumption.
Data is trusted too late.
Introduced after scale, slows everything down.
Fully comfortable operating in English and French environments.
We stabilize pipelines, improve data reliability and move real workloads into production. A consulting partner embedded with your team, focused on execution, not theory.
Put Claude to work across your enterprise: choose the right use cases, connect Claude securely to your data and tools, and roll it out with governance and adoption built in, including Claude on Databricks.
Implement Informatica CDGC: metadata scanning, business glossary, lineage and a searchable catalog, connected to your lakehouse and Unity Catalog.
Implement Informatica CDQ: profiling, quality rules, scorecards and remediation, aligned with the governance you define in CDGC.
Legacy SQL, ETL and warehouse logic translated, validated and optimized for Databricks.
Pipelines tuned for cost and reliability, with stronger orchestration, testing and operational discipline.
Platform structure and governance introduced without slowing delivery, tied to real platform ROI.
Private LLMs, RAG and governed AI agents on Databricks, from strategy to production.
Databricks-certified engineers who join your team and work hands-on, not advisory-only.
Plus Snowflake, Redshift, Hadoop, Oracle and 15+ more with AI-powered migration tooling.
Every SQL workload and ETL dependency inventoried before scoping. Pipelines sequenced by lineage, not convenience.
BTEQ scripts, stored procedures and macros converted and refactored, so Teradata-specific patterns stay behind.
Schema, SQL, stored logic and ETL reassessed, so obsolete, low-value workloads don't make the trip.
T-SQL, stored procedures, SSIS packages and SQL Agent jobs refactored, including identity columns and dynamic SQL.
Macros and DATA steps refactored for distributed execution, with decimal precision and compliance mapping checked.
Architecture before deployment: operating model, service accounts and Unity Catalog governance designed first.
Once data is reliable in production, it can power AI.
Build, tune and serve private LLMs with Mosaic AI, Unity Catalog, Delta Live Tables and Databricks Workflows, with MLOps and AutoDQ for responsible delivery.
Agent Bricks and Vector Search reasoning over your Delta data, with AI Gateway, lineage, access controls and evaluation pipelines implemented end to end.
A fixed-scope program and free accelerators, built from real enterprise delivery experience.
One use case, end to end, live in production. Fixed scope with a team of 2–5 senior experts.
$34,495 · See the route → AcceleratorAI-powered data quality automation, native to Databricks, with 200+ pre-built rules.
Free with Go-Live → AcceleratorTurns a business requirements document into a production-ready test suite, entirely on your own infrastructure.
On-premises AI → Free eBookProduction-ready use cases for Databricks, and what breaks when teams operationalize them.
Get the eBook →For organizations that have already chosen Databricks and are ready to go live. One use case, end to end, delivered by a team of 2–5 senior experts.
Assess the environment, prioritize use cases, scope data tightly and pick one realistic, high-impact use case.
Output: clear execution planBronze → silver → gold pipelines, reliability fixes, AutoDQ embedded and performance optimized.
Output: working, validated pipelinesDeploy to production, validate with business users and define the next use cases.
Output: live workload and roadmapFixed scope, 90 days. Aligns with Databricks' Pilot to Production initiative, with co-selling and co-funding options available.
See if you qualify
AutoDQ automatically generates data quality rules, monitors your pipelines in real time and explains issues in plain language. Teams validate data in hours, not weeks.
Continuous anomaly detection and alerting across your critical pipelines.
Schema, format, business-rule and cross-table referential checks.
GenAI explains issues and suggests fixes, with approval workflows and full audit logs.
No new software to install. Scales with your lakehouse and Unity Catalog.
Upload a business requirements document and get a complete, production-ready test suite for UI, APIs, databases and business logic. It runs entirely on your own infrastructure: no cloud, no external APIs.
Recent KData work in finance automation, fleet analytics, platform migration and data quality, plus an i-QA product story. Client names are withheld unless the client shares a named success story.
Keep invoice processing moving without relying on manual handoffs or an employee's laptop. KData helps organizations connect invoice intake, document handling and processing workflows through a centrally hosted solution.
Talk to us about finance automation
Invoices often arrive through disconnected channels, leaving finance teams to collect attachments, organize documents and move information between systems. Desktop-dependent automation adds another limit: processing stops when the application closes or the device goes offline.
We design invoice automation around your existing systems, processing requirements and approval policies, connecting incoming invoice channels with document storage and downstream workflows. Repetitive handling goes down, and people stay involved where judgment is required.
The scope can extend from automating a single repetitive step to connecting a broader invoice workflow.
“The client's own words on the outcome will appear here.”
Less manual administration, more consistent document handling, and clearer visibility into invoices requiring action.Results depend on the systems connected, invoice complexity and the level of automation implemented.
Turn vehicle data into clear, interactive insights. KData helps organizations bring fleet information together through dashboards and conversational analytics, making it easier to monitor performance, explore trends and answer operational questions.
Talk to us about AI/BI dashboards
Fleet data often sits across disconnected sources and technical reports. Business users depend on analysts to prepare summaries, investigate changes and explain what the numbers mean. Static dashboards show what happened while leaving follow-up questions unanswered.
We design AI/BI dashboards around the decisions your teams need to make. Curated data, consistent business definitions and natural-language exploration help users move from a fleet-wide overview to the detail behind a trend, on a governed Databricks foundation that aligns access, metric definitions and reporting across teams.
“The client's own words on the outcome will appear here.”
Faster access to insights, less repetitive reporting work, and more independent exploration by business users.AI-generated answers stay traceable to the underlying data and available for review.
Modernizing a data platform takes more than converting code. KData helps organizations plan and deliver Netezza-to-Databricks migrations around trusted data, business continuity and manageable delivery stages.
Assess my Netezza environment
Legacy platforms accumulate complex dependencies, undocumented business rules and processing patterns that need careful redesign. Moving workloads without understanding these details can introduce reporting differences, unexpected rework and uncertainty at cutover.
A successful migration needs to establish that the new platform supports the business processes and data its users rely on.
KData structures migration into prioritized waves, with clear quality checks and acceptance criteria throughout delivery.
Quality built into deliveryKData's AutoDQ and i-QA accelerators can support pipeline-level data quality checks and dataset test generation. Their use is scoped to the migration's requirements, helping teams make validation repeatable across workloads.
“The client's own words on the outcome will appear here.”
A controlled transition, earlier visibility into migration risks, and stronger evidence for go-live decisions.The resulting Databricks foundation can support governed reporting, AI/BI dashboards and future analytics initiatives.
Reliable analytics starts with reliable data. AutoDQ, KData's data quality accelerator, brings profiling, validation and monitoring into the Databricks environment, helping teams identify issues before they affect dashboards, reporting and AI applications.
Explore AutoDQ
As data pipelines grow, manual validation becomes difficult to maintain. Teams write checks individually, investigate recurring discrepancies and spend valuable time explaining why reports disagree.
Without consistent quality controls, problems can travel through multiple transformations before business users discover them.
AutoDQ embeds data quality checks into everyday pipeline operations. AI-assisted rule generation helps teams establish validation coverage, while monitoring and plain-language explanations make issues easier to understand and investigate.
Running natively within Databricks, AutoDQ brings these capabilities closer to the pipelines and datasets teams already manage.
“The client's own words on the outcome will appear here.”
Less repetitive rule-authoring work, earlier visibility into data problems, and more consistent validation across datasets.These controls help establish a stronger foundation for trusted AI/BI dashboards and downstream applications.
Turning business requirements into comprehensive tests takes time. i-QA, KData's AI-powered test-generation platform, helps teams translate Business Requirements Documents into structured test cases, automation files and supporting documentation within their own infrastructure.
Product solution story · not a completed client engagement
QA teams must interpret requirements, map them to application behavior, and create tests across interfaces, APIs, databases and business logic. Manual authoring can lead to inconsistent coverage and repeated work whenever requirements change.
For organizations handling sensitive information, AI also raises a deployment question: how can teams automate test generation while keeping requirements, schemas and business rules within their controlled environment?
i-QA uses locally deployed AI models to analyze requirements and generate test assets. Its core processing runs on the organization's infrastructure without requiring external AI services.
The workflow connects requirements analysis, test generation and documentation, giving QA teams a structured starting point for review and execution.
Human review remains part of establishing that generated tests accurately reflect business intent and are suitable for the target environment.
Local processing helps organizations retain control over sensitive project information. Execution history and analyst attribution support traceability, while multi-user access lets teams manage concurrent generation work.
Deployment should reflect the organization's connectivity requirements. Optional Microsoft Teams notifications require connectivity to Teams and would be excluded from a fully air-gapped configuration. Audit records can support compliance processes; they do not independently establish regulatory compliance.
Less repetitive test-authoring work, more consistent coverage across application layers, and clearer connections between requirements and testing.Actual time savings and coverage improvements should be measured against the team's existing QA process.
Production migrations to Databricks from Teradata, DB2, SAS and SQL Server, plus Databricks on Google Cloud. Open a card for what we did and the outcome.
A major North American organization ran a large-scale Teradata environment for critical reporting, operations and planning, until it became a bottleneck.
A major North American railroad ran a large DB2 environment for critical reporting, operations and planning workflows that had become a bottleneck.
A large enterprise ran an extensive SAS environment for critical ETL, reporting and analytics across multiple business domains.
A large enterprise ran a complex SQL Server data warehouse supporting critical reporting, analytics and operational workflows.
A major North American railroad implemented Databricks on GCP to modernize its data platform for analytics and AI, within GCP's specific constraints.
Short walkthroughs of KData's accelerators, running natively in Databricks or on your own infrastructure.
YouTubeWatch the AutoDQ overview
Automated data validation, monitoring and remediation, natively within your Databricks environment.
Request a demo
YouTubeWatch the Excelfore demo
Predict vehicle failure from your dashboard: battery health, risk scores and AI-recommended actions across a connected fleet, built on Databricks.
Request a demoJoin a team transforming how organizations unlock the power of their data, working with leading AI and data technologies alongside industry experts.
We embrace leading data and AI technologies and encourage creative solutions to complex challenges.
Great ideas come from working together, in an environment where every team member thrives.
We invest in our people through continuous learning and career development.
Send your resume and a cover letter on why the role excites you.
A phone or video call with our talent team.
Meet team members and hiring managers to go deep on your experience.
Events, guides and announcements from KData.
One of the applied AI events on KData's calendar, at the Palais des congrès de Montréal.
All eventsHosted by KData and Databricks at Google Toronto, on the Data Intelligence Platform on Google Cloud.
All eventsA risk-managed approach: discovery, factory model, medallion architecture and a living risk register.
Read the guideUse cases that actually deliver business value on Databricks, and how to get them into production.
Get the eBook
KData's founder, practice leads and engineers behind its Databricks, Informatica and AI work.
Karthik MuthuramanFounder
Srikanth ASr. Databricks ML & AI Engineer
Shilpa SathishData Engineer
Janavi RamakrishnanAI Field EngineerKData is a Databricks consulting partner and a registered Claude partner, and implements Informatica's cloud governance and quality platform.
We implement Claude for enterprise teams, from the first use case to a governed, organization-wide rollout.
See Claude implementationSuccess storyEndurance Environmental: invoice management with Claude AI →Implementation of Informatica Cloud Data Governance & Catalog (CDGC) and Cloud Data Quality (CDQ).
See CDGC & CDQFrom manual invoice processing to intelligent, AI-powered financial workflows.
Endurance Environmental needed a smarter, more efficient way to manage supplier invoices, streamline approvals and gain better visibility into financial performance.
Manual invoice processing, disconnected systems and limited visibility into billing discrepancies made it hard to keep accurate records, track profitability and identify potential revenue leakage.
KData developed an intelligent invoice management and financial reconciliation solution using Claude AI automation to streamline workflows, reduce repetitive manual tasks and improve financial visibility.
The application connects Outlook, OneDrive and QuickBooks, bringing invoice intake, document processing, approvals and financial analysis into one connected workflow.
KData helped Endurance Environmental establish a more connected and intelligent invoice management workflow, bringing together automated document handling, structured approvals, financial reconciliation and exception detection. Repetitive administrative work went down, and invoice processing and profitability became easier to see.The result: a scalable, AI-powered foundation for smarter accounts payable management and financial decision-making.
Go-Live in 90 Days aligns with Databricks' Pilot to Production initiative, with co-selling and co-funding options available.
Databricks on GCP implementations, and executive events co-hosted with Databricks at Google Toronto.
We attend and host AI and data events across Canada and the US. Planning one? We'd like to hear about it.
Request KData at your eventPlanning a migration or stuck short of production? Let's assess your Netezza or legacy platform and define a production path.
KData