Data Analytics
Modern enterprise data platforms that the business can trust
We design and build governed data platforms on Google Cloud — ingestion, lake, warehouse, transformation and semantic layer — so analytics stops being an argument about whose number is right.
Business challenges
What usually brings teams to us
These are the recurring conditions we are asked to resolve. If several look familiar, an assessment is the efficient starting point.
Contested metrics
Every team maintains its own version of revenue, churn or utilization, and reconciliation consumes the analysts' week.
Brittle pipelines
Overnight jobs fail silently, dependencies are undocumented, and nobody is confident enough to change them.
Warehouse sprawl
Years of ad-hoc tables and views with no ownership, no tests and no lineage back to a source system.
Governance after the fact
Access control and classification were added late, so sensitive data sits in places nobody can fully enumerate.
Latency mismatch
Operational decisions need minutes; the platform delivers yesterday's batch and cannot be pushed further.
Cost drift
Compute spend grows faster than usage because query patterns, partitioning and storage tiers were never revisited.
Our approach
A governed platform, modelled deliberately
We treat the data platform as a product: versioned transformations, tested models, documented contracts with source systems and a semantic layer that defines each metric exactly once.
- Target architecture agreed before build, including ingestion patterns, storage zones and modelling conventions.
- Transformations in code, under review, with automated tests and data-quality assertions on critical tables.
- Governance designed in: classification, lineage and least-privilege access via Dataplex and IAM.
- Streaming introduced only where the decision genuinely needs it, keeping batch where batch is correct.
- Cost controls — partitioning, clustering, storage tiering and slot strategy — treated as design decisions.
- A semantic layer in Looker so a metric has one definition across every dashboard and downstream consumer.
Core capabilities
What we deliver
- BigQuery data warehousing
- Modelling, partitioning, clustering and slot strategy for petabyte-scale analytics.
- Cloud data lakes
- Zoned Cloud Storage lakes with clear raw, refined and curated boundaries.
- Data lakehouse architectures
- Open table formats and external tables where workloads span lake and warehouse.
- Data Mesh
- Domain ownership, published data products and federated governance where the operating model supports it.
- ETL / ELT
- Declarative, version-controlled transformation pipelines with automated testing.
- Data pipelines
- Batch and streaming ingestion built on Dataflow and Pub/Sub with retry and replay semantics.
- Data quality
- Freshness, volume, schema and business-rule assertions surfaced before consumers notice.
- Data governance
- Catalogue, classification, lineage and least-privilege access with Dataplex and IAM.
- Analytics engineering
- Dimensional and wide-table modelling, documentation and a reviewed change process.
- Looker
- Semantic modelling in LookML, governed explores and embedded analytics.
- Real-time analytics
- Streaming ingestion into BigQuery for operational dashboards and event-driven decisions.
Technology stack
Google Cloud services we work with
Only services that are genuinely relevant to the capabilities described above.
BigQuery
Serverless analytical warehouse and the platform's query engine.
Cloud Storage
Zoned object storage backing the data lake.
Dataflow
Managed batch and streaming transformation pipelines.
Dataproc
Managed Spark for existing Spark workloads and heavy processing.
Pub/Sub
Event ingestion and decoupling between producers and pipelines.
Dataplex
Catalogue, classification, lineage and governance across lake and warehouse.
Looker
Semantic layer, governed BI and embedded analytics.
BigQuery ML
In-warehouse modelling where the data never needs to leave BigQuery.
Architecture
Reference data flow
01
Data sources
- Applications
- Operational databases
- SaaS and partner feeds
02
Ingestion
- Pub/Sub
- Dataflow
- Batch loads
03
Data lake
- Cloud Storage
- Raw and refined zones
- Schema capture
04
Transformation
- ELT in BigQuery
- Tests and assertions
- Lineage
05
BigQuery
- Curated models
- Partitioned marts
- Access policies
06
Consumption
- Looker
- AI / ML features
- Applications
Business outcomes
What changes as a result
Faster time to insight
New questions are answered from existing models rather than a new pipeline project.
Trusted metrics
A single semantic definition per metric, applied consistently across every consumer.
Lower maintenance load
Tested, documented transformations that a new engineer can safely change.
Governed access
Classification and least-privilege policy applied at the platform level, not per dashboard.
Predictable cost
Query and storage patterns designed for the workload, then monitored for drift.
AI-ready data
Curated, well-described tables that AI initiatives can be grounded on immediately.
FAQ
Common questions
Do we need to replace our existing warehouse to start?
No. Most engagements run incrementally: we stand up the target patterns alongside the current warehouse, migrate domain by domain, and decommission only once consumers have moved.
Do you work with tools outside Google Cloud?
Yes. Source systems, orchestration and BI often sit elsewhere, and we integrate with what you run today rather than requiring a single-vendor estate.
How do you approach data quality?
Quality assertions are written alongside the transformations they protect — freshness, volume, schema and business rules — and failures alert the owning team before consumers see the data.
Can you help our team take over afterwards?
That is the intended end state. Delivery includes documentation, code review conventions and role-based enablement so ownership transfers cleanly.
Start a conversation
Modernize your data platform.
Start with a data maturity assessment, or bring us a specific platform problem and we will scope the shortest credible path to a fix.