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
Get a quote

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.