Platforms · Data · 02 of 02

Where your data goes from landed to loved.

Warehouses that answer questions, lakehouses that hold the long tail, engines that crunch petabytes, and transformations that ship every commit. We pick the platform for the workload — not the workload for the platform.

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180+Data pipelines in production
4Modern data platforms
−62%Avg. warehouse-cost cut
Four platforms · one data team

A modern data stack, picked piece by piece.

No one platform wins on every axis — and only one team will tell you that without a quota attached.

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Snowflake

The warehouse that scaled the elastic-compute idea. We design for separation of compute and storage — and the cost discipline it deserves.

  • Multi-cluster warehouses
  • Snowpark & Snowpipe
  • Iceberg tables & sharing
  • Cost guardrails
Select Services Partner
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Databricks

The lakehouse — one platform for ETL, ML, and now AI agents. Unity Catalog as the spine, Delta as the storage truth.

  • Delta Live Tables
  • Unity Catalog governance
  • MLflow & Mosaic AI
  • SQL Serverless
Consulting Partner
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Apache Spark

The engine under it all. We tune real Spark jobs at real scale — without inheriting the cluster-management headaches you came here to escape.

  • Structured Streaming
  • PySpark & Spark SQL
  • Performance tuning
  • Petabyte-scale jobs
Open-source contributors
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dbt

SQL transformations as software. Tests, version control, lineage and CI — the workflow that turned the data team into a real engineering team.

  • Models · macros · seeds
  • Tests & data contracts
  • Exposures & lineage
  • dbt Cloud & Core
Premier Services Partner
End-to-end stack

The five layers of a data platform that earns its name.

Each layer gets the platform it deserves. Together they form a stack that's tested, governed, and recoverable.

L1 · Ingest

Land it once, land it well.

CDC, streaming and batch ingestion with idempotency and replay built in. Schema evolution that doesn't page anyone.

FivetranAirbyteKafkaSnowpipe
L2 · Store

Cheap, queryable, durable.

Lakehouse on object storage with open table formats. Cold storage tiered, hot tables vacuumed, partitions sane.

IcebergDeltaHudiS3
L3 · Transform

SQL as software, finally.

Modeling layer with tests, contracts, version control and CI. Lineage you can show the auditor without flinching.

dbtSQLMeshDataform
L4 · Serve

Fast queries, sane cost.

Warehouse compute sized to workload, materializations chosen with intention, semantic layer between BI and tables.

SnowflakeBigQueryDatabricks SQL
L5 · Govern

Trust, by design.

Catalog, lineage, access policies and PII tagging that survive the next reorg. Cost & quality observable in one pane.

UnityAtlanMonte Carlo
Which platform, when

The honest picker — not the vendor brochure.

A starting heuristic. Every project warrants a real architecture review — we'll do that on the discovery call.

Workload
Snowflake
Databricks
Apache Spark
dbt
BI & analyticsReporting, dashboards, ad-hoc SQL
Strong default
SQL Serverless
Overkill
Pairs with both
ML trainingNotebooks, distributed training
Snowpark
Best-in-class
Capable
Feature prep only
Streaming pipelinesSub-minute latency
Snowpipe
Structured Streaming
Foundation
Not designed for
Petabyte ETLHeavy daily batches
Works, watch spend
Sweet spot
Native
Orchestrates it
Transformation disciplineTests, CI, lineage
With dbt
With dbt or DLT
DIY
First choice
Our data methodology

Modeling first. Tools second.

A data platform is only as good as the contract between source and consumer. We start there — the platform comes next.

01 · Model

Dimensional & contract-first.

Kimball where it earns its weight, Data Vault when sources are noisy, contracts at every boundary. Designed before a row moves.

  • Conformed dimensions
  • Producer / consumer contracts
  • Versioned semantic layer
02 · Test

Treat data like code.

Every model is tested. Every PR runs the test suite. Every breaking change is detected before it reaches the executive dashboard.

  • dbt + Great Expectations
  • Schema & freshness SLAs
  • Anomaly detection
03 · Govern

Lineage you can show an auditor.

Catalog, classification, access control and audit logs that survive personnel change. PII tagged at column-level, not in a wiki.

  • Unity Catalog / Atlan
  • PII tagging at column-level
  • Row-level & masking policies

The data platform conversation you actually want.

Book 30 minutes. Bring your stack diagram, your warehouse bill, or both. We'll either point at the cheapest next step — or tell you the platform isn't the problem.

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