Enterprise Databricks Execution

Databricks is deployed.
Now make it work
at scale.

We stabilize pipelines, improve data reliability, and move real workloads into production. From legacy platforms to Databricks production. Including Netezza migrations delivered in real environments.
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A consulting partner embedded in your team, focused on execution, not theory.
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Start a Discussion See the 90-Day Pilot

Canadian flag Active in enterprise Databricks environments across Canada

Where Databricks Programs Stall

Most teams get stuck in POCs. We take a real use case to production with reliable pipelines and embedded data quality. Including migrations from legacy platforms like Netezza.

Use cases in production bring the real value. How can we help you remove friction in getting there?

The patterns are consistent

Unreliable pipelines

Not production-grade. Constant rework.

Weak gold layer

Poor modeling limits consumption.

Manual validation

Data is trusted too late.

Late governance

Introduced after scale, slows everything down.

Migration Experience That Actually Ships

Legacy platforms don't translate cleanly into Databricks.

SQL dialect differences, pipeline redesign, performance tuning.

Most teams underestimate the rework required.

What this means in practice

Netezza SQL → Spark / Databricks SQL translation

Pipeline re-architecture (not lift-and-shift)

Data validation embedded in pipelines (AutoDQ)

Performance tuning for cost and reliability

Cutover planning and parallel run

Netezza → Databricks Migration

Migrated legacy warehouse workloads to Databricks with rebuilt pipelines, embedded data validation, and production-ready orchestration.

Result: stable pipelines, trusted data, and a scalable foundation for AI use cases.

Planning a migration to Databricks?

Let's assess your Netezza or legacy platform and define a production path.

Assess My Migration

Our focus

Where We Intervene

Production acceleration

Move priority workloads into production with a clear execution path.

Data reliability in pipelines

Validation embedded early to prevent downstream issues.

Platform structure and governance

Structure introduced without slowing delivery.

Expansion of real workloads

Identify and scale use cases that drive actual platform usage.

The Databricks Production Acceleration 90-day Pilot

A fixed-scope engagement to move Databricks workloads into production at scale.

For teams with Databricks in place but limited production scale.

Get more information

What this pilot delivers

Expansion opportunities identified

Clear, prioritized use cases tied to DBU growth.

Production pipeline reliability improved

Critical pipelines stabilized and hardened.

Execution layer strengthened

Better orchestration, testing, and operational discipline.

Path to scale defined

Concrete plan to move additional workloads live.

Case summary

What This Looks Like in Practice: a class-1 railroad

Problem

At a Class I railway in Canada, the issue was not access to data, but trust in the outputs.

Teams were spending significant time validating results before using them.

Solution

KData focused on improving reliability in the pipeline layer and reducing manual validation effort.

Result

More stable pipelines, faster production readiness, and a clear path to expanding workloads.

AutoDQ

Data reliability built into your pipelines.

AutoDQ embeds data validation directly into Databricks pipelines, so issues are caught before they impact downstream use cases.

It reduces manual rule definition and gives teams clear visibility into data quality at every stage.

Used where it drives value. Not layered on for show.

Get more information

Validation at ingestion and transformation

Rules applied where data enters and evolves.

Automated rule generation

Reduced manual effort, faster coverage.

Pipeline-level visibility

Clear signal on data quality before consumption.

Native to Databricks workflows

No external tooling overhead.

Canadian enterprise focus.
Deep presence in Quebec.

We work with enterprise teams across Canada, with a strong footprint in Quebec.

Fully comfortable operating in both English and French environments.

Grounded in the realities of local enterprise execution.

Move from experimentation to production-scale data

We work with teams that have already deployed Databricks but are not scaling as expected.

The focus is simple: stabilize pipelines, improve data reliability, and move more workloads into production.

Production-focused delivery

Not POCs. Real workloads, live environments.

Pipeline reliability and data quality

Issues addressed at the source.

Structured path to scale

Clear next steps tied to business impact.

Hands-on execution

Embedded with your team, not advisory-only.

Discuss Your Databricks Environment