Big data &
analytics services.

Data lakes, lakehouses, streaming pipelines, semantic layers, and self-serve analytics — for organisations that have outgrown spreadsheets.

Brief us See work
What we build

We deliver big data and analytics services — data engineering, lakehouses, streaming pipelines, semantic layers, and self-serve analytics. On Snowflake, Databricks, BigQuery, and Redshift.

Problem · approach · outcome.

How we run this kind of work
01 · Problem

Most analytics programs stall on data quality.

A new dashboard is easy; trustworthy data feeding it is hard. Most analytics programs spend 70% of their effort on cleaning, modelling, and reconciling data — and only 30% on the visible analysis.

02 · Approach

Lakehouse + semantic layer + governance.

A lakehouse as the storage layer, a versioned semantic layer (dbt or LookML) as the contract, and governance built in (lineage, access, freshness SLAs). Then dashboards on top of a foundation that won't collapse on the next reorg.

03 · Outcome

Analytics the rest of the org trusts.

A lakehouse with proper governance. dbt-driven semantic layer. Fresh, lineage-traced data. Self-serve for the analyst population. Engineering team building capability, not firefighting tickets.

What we ship.

6 modules · extensible
F-01

Data lakes & lakehouses

Snowflake, Databricks, BigQuery, Redshift — architected for cost, scale, and governance.

F-02

Streaming pipelines

Kafka, Kinesis, Pub/Sub — event ingest and stream processing with proper schema management.

F-03

dbt & semantic layer

dbt for transformation, with versioned semantic layer (dbt Semantic Layer, Cube, LookML).

F-04

BI & dashboards

Looker, Tableau, Power BI, Metabase — on top of a properly modelled semantic layer.

F-05

Data governance

Lineage (OpenLineage), access (privacera, immuta), freshness SLAs, and data contracts.

F-06

Migration

On-prem to cloud, Teradata / Netezza / Hadoop to lakehouse — with parallel-run reconciliation.

Tech stack.

Production-tested
Warehouses
SnowflakeDatabricksBigQueryRedshift
Streaming
KafkaKinesisPub/SubFlink
Modelling
dbtCubeLookMLIcebergDelta
BI
LookerTableauPower BIMetabase

Data swamp or
lakehouse?

Data practice · lakehouse-led
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Big data & analytics FAQs.

Q-01Snowflake or Databricks?
Both. Snowflake for SQL-heavy analytics; Databricks for ML-heavy and unstructured data. Lakehouse architectures often use both.
Q-02Do you build streaming?
Yes — Kafka, Kinesis, Pub/Sub for ingest; Flink, Spark Structured Streaming for processing.
Q-03dbt setup?
Yes — dbt is our default transformation framework. We set up the model layer, CI/CD for dbt, semantic layer, and documentation.
Q-04Can you migrate from Hadoop / Teradata?
Yes — to Snowflake, Databricks, or BigQuery. Parallel-run reconciliation included.
Q-05BI tool selection?
We work with Looker, Tableau, Power BI, Metabase, Sigma — selection driven by team capability and use case.

Related across the cluster.

Parent & siblings→ 14

Cross-vertical→ 4

Proof & resources→ 4