We design and build the data foundation your AI models need — real-time ingestion pipelines, clean data warehouses and lakehouses, and self-serve analytics layers that give every team access to reliable, governed data without engineering bottlenecks.
Kafka · Apache Spark · dbt · Snowflake · BigQuery · Airflow · Databricks
Batch pipelines that deliver data hours later can't power real-time AI decisioning. We build streaming-first data architectures using Kafka and Spark Streaming that process, enrich, and route data in sub-second windows — enabling fraud detection, live recommendation, and operational AI that responds to what's happening now.
We design Kafka topics, partition strategies, consumer group configurations, and exactly-once delivery guarantees for your event-driven data flows. For AWS environments, Kinesis Data Streams provides similar capabilities with managed infrastructure. Replication, retention policies, and schema registry are all configured to match your SLAs.
Raw events are rarely in the shape AI models need. We build Spark Streaming, Flink, or Kafka Streams processors that join live events with reference data, apply business logic, detect complex event patterns, and output enriched records to downstream consumers — all within millisecond latency windows and with full backpressure handling.
A pipeline that silently fails or passes bad data is worse than no pipeline at all. We build data quality checks at every ingestion stage — schema validation, null checks, statistical profiling — with automatic circuit breakers that halt processing and alert on-call engineers when data anomalies are detected. Lineage is tracked end-to-end.
Fragmented data across spreadsheets, legacy databases, and SaaS tools means analysts can't trust their numbers and ML engineers can't train reliable models. We design unified data warehouse and lakehouse architectures that consolidate all your data with a single transformation layer everyone can rely on.
We design the right warehouse topology for your workload mix — Snowflake for SQL-heavy analytics, BigQuery for massive-scale event data, Databricks Delta Lake for unified batch/streaming and ML training. Database design includes domain-specific schemas, cost-optimised clustering, and materialized views for common analytics patterns.
dbt brings software engineering discipline to SQL analytics — version-controlled models, automated testing, rich documentation, and dependency graphs that make transformations auditable and maintainable. We build modular dbt project structures following the staging → intermediate → mart pattern, with full test coverage on critical business metrics.
Reliable data warehouses enable self-serve analytics — where product managers, finance teams, and operations leads can answer their own questions without filing data requests. We build semantic layers and BI dashboards that surface the right metrics, with governance controls preventing metric proliferation and definition drift.
We build production BI solutions on your chosen platform — semantic models in Power BI with row-level security, Looker Explores with custom LookML, or Tableau data sources with extracts optimised for your query patterns. Each implementation includes a governance layer that defines certified metrics centrally so every dashboard shows the same numbers.
As data volumes grow, discoverability and trust become the bottleneck. We implement data catalogues (Datahub, Collibra, or Atlan) that document table schemas, column descriptions, owners, lineage, and freshness SLAs — so any analyst or engineer can find, understand, and trust a dataset without asking the data team. Access policies and PII classification are enforced automatically.
Book a free data architecture session. We'll review your current data landscape, identify gaps that will block your AI ambitions, and produce a target architecture recommendation.