Snowflake, Databricks, or BigQuery: which do you recommend?

Snowflake for the fastest operational simplicity and strong governance. Databricks when heavy ML/Spark and a true lakehouse matter. BigQuery when you're already deep in Google Cloud. We recommend based on your existing stack, team skills, and AI roadmap, not a vendor relationship.

How does this connect to knowledge graphs and GraphRAG?

The modernized warehouse becomes the governed source of entity data. The graph layer sits alongside it holding relationships, and vector indexes hold unstructured text keyed by the same entity IDs. That's what lets GraphRAG answer from one trusted foundation instead of stale exports.

How do you handle governance and compliance during migration?

Data lineage, column-level access controls mapped to your IAM, and PII tagging are part of the migration, not an afterthought. Everything runs inside your cloud boundary, and each migrated workload ships with its lineage and control documentation.

How long does a modernization program take?

A first workload typically lands in 6 to 10 weeks (assessment, landing zone, and the first migrated pipeline). Full-estate programs run in quarters, phased so value ships continuously rather than at the end.

How do you scope and price a modernization engagement?

We start with a fixed-scope assessment and landing-zone phase, then price each migration slice against a defined set of workloads rather than open-ended time-and-materials. The first workloads run as a fixed-scope pilot (typically 6 to 10 weeks) backed by our delivery guarantee; full-estate programs are phased so budget maps to value shipped. We scope precisely against the size and state of your estate rather than quoting a rate card.

AI & Machine Learning

Enterprise Data Warehouse Modernization

Move off Teradata, Netezza, and on-prem Oracle onto a cloud lakehouse your AI can actually query. Governed, streaming, and vector-ready: migrated in phases, in your VPC.

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What is enterprise data warehouse modernization?

The Answer

Enterprise data warehouse modernization is the migration of legacy warehouses (Teradata, Netezza, on-prem Oracle/SQL Server) to a cloud lakehouse — Snowflake, Databricks, or BigQuery — that can actually feed AI. It replaces overnight batch ETL with streaming and ELT, adds a governed semantic layer, and makes the warehouse vector- and graph-ready so GraphRAG and analytics read from one trusted source instead of drifting copies.

What you get

Three outcomes we commit to before we start.

Phased migration, no big-bang cutover

We move workloads in slices behind a routing layer, so the old warehouse and the new lakehouse run in parallel until each report and pipeline is verified. No weekend cutover, no frozen quarter.

A semantic layer everyone reads against

Metrics are defined once (dbt / a governed semantic layer) so finance, analytics, and AI all read the same definition of revenue, churn, or exposure. The warehouse stops being a source of arguments.

AI-ready by design

The modern lakehouse holds structured tables, vector embeddings, and the entity keys a knowledge graph needs, so GraphRAG and analytics read from one governed foundation instead of exported copies that drift.

The Guaranteed Production Pilot

Fixed scope · Written target

A production Data Warehouse Modernization system in your VPC: audited, documented, owned by your team.

Not a slide deck and not a sandbox demo: a working Data Warehouse Modernization deployment inside your own cloud boundary, mapped to your compliance controls and handed over with the schema, the eval harness, and the runbook.

Speed

Architecture and success criteria signed off in week one. First working slice running in your environment inside 30 days.

Zero effort

Fully done for you. Our senior squad owns ontology, build, evals, and compliance mapping: your team reviews and signs off, nothing more.

Risk reversal

Fixed scope, fixed price, and a measurable success target agreed in writing before we start. Miss the target and you don't pay for the pilot.

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