Data & analytics
Batch and streaming data, from every system you run, landing in one governed lakehouse. Your teams get dashboards and models built on numbers they can trace back to source.
Snowflake · Databricks · Kafka · Spark · Airflow · dbt · Power BI
Architecture overview · 6 layers
Data & analytics architecture
A lakehouse that carries batch and streaming in one governed platform, with quality checks running the whole way through.
Diverse data sources
A sale rings through the till, an order updates in the ERP, a sensor sends a reading. Each of those is a record something downstream needs.
Smart ingestion
The record is picked up by whichever route suits its source: an overnight batch, a Kafka event, or a change captured off the database log.
Lakehouse storage
It lands raw and untouched, so the original is always there to go back to, then gets cleaned and standardized in the layer above.
Processing & modeling
dbt joins it to everything else that describes the same customer or product, and applies the metric definitions your teams agreed on.
Visualization & insights
It surfaces in a dashboard, a scheduled report, an alert when a threshold breaks, or a forecast the business acts on.
Data governance
At every step above, the record carries its lineage. Anyone can trace a number on a dashboard back to the row it came from.
Key capabilities
What the platform gives you
Data source integration
SAP, Salesforce, SQL and NoSQL, ServiceNow, Jira, POS, clickstream, IoT telemetry, files and Kafka streams, with an adapter written where no connector exists.
Data pipelines
Batch ETL and ELT, streaming, change data capture and API ingestion on Kafka, Spark, Airflow, Fivetran and dbt, one pattern per source rather than one for all of them.
Lakehouse storage
Raw, curated and aggregated layers on Snowflake, Databricks, BigQuery, Redshift or Delta Lake, so a change to a model never costs you the source data.
Data modeling
Logical and physical models in dbt, with metrics definitions, ML feature stores and a semantic layer your teams share across tools.
Streaming & real time
Live pipelines and aggregation for real-time dashboards, threshold and anomaly alerts, and event-driven analytics beside the batch workloads.
BI & visualization
Executive and operational dashboards, scheduled and ad-hoc reporting, alerts and forecasting in Power BI, Tableau, Looker or Grafana, all reading the same definitions.
Data quality & governance
Profiling, quality checks, lineage, catalog and discovery, access control, PII masking, and named ownership with SLAs per dataset.
Infrastructure & operations
Kubernetes, Terraform, GitOps and CI/CD, with Prometheus and Grafana watching ingestion, storage and processing, so a pipeline failing at 3am wakes somebody.
Technology stack
Storage, pipelines and BI
Chosen against your existing cloud investment, your scale and the tools your analysts already know.
Lakehouse and storage
Snowflake
DatabricksGoogle BigQuery
AWS RedshiftDelta LakePipelines and processing
Apache Kafka
Apache Spark
Apache Airflow
dbtFivetranBI and visualization
Power BI
TableauLookerGrafanaUse case · Retail
POS, web and loyalty data in one view
A retail chain consolidated its point-of-sale systems, web traffic and loyalty programme into a Snowflake warehouse, giving marketing a live picture of customer behaviour to work from.
Frequently asked questions
A lakehouse gives you both. We architect raw, curated and aggregated layers, so you keep the flexibility of a lake for varied data types alongside the query performance of a warehouse for analytics and reporting. It runs on Snowflake, Databricks, BigQuery, Redshift or Delta Lake, chosen against your existing cloud investment, your scale and your team's skills.
Give your teams one version of the numbers
Send us the systems holding your data and the questions the business keeps asking. We will come back with an approach and a realistic shape for the work.