Data/IoT
Data infrastructure for organisations that have outgrown relational databases — data lakes, warehouses, and lakehouse architectures that make large datasets queryable and useful.
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Years in production
What it is
Big data management involves designing storage, processing, and query infrastructure for datasets that exceed the practical limits of traditional relational databases — typically characterised by high volume, velocity, or variety — using distributed systems, columnar storage, and MPP query engines.
What you get
A data lake that nobody can query is just an expensive storage bucket. The goal of big data infrastructure is not to store large volumes of data — it is to make that data accessible, queryable at speed, and governed well enough that people trust the outputs. We design with the analyst, the data scientist, and the downstream application as the primary users.
Modern lakehouse architectures (Delta Lake, Apache Iceberg) unify batch and streaming, support ACID transactions on object storage, and allow schema evolution without breaking downstream consumers. We build data warehouses on Snowflake, BigQuery, or Redshift depending on your query patterns, team expertise, and cost profile.
Data quality and governance are built in from day one: column-level lineage with dbt, data quality checks in the pipeline with Great Expectations, data catalogue integration (Datahub, Atlan), and role-based access control at the column level for sensitive data. A data platform that people do not trust is not used.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Data quality checks and validation with Great Expectations
Data catalogue and metadata management (Datahub, Atlan)
Spark and Trino for distributed query processing
Column-level access control for sensitive and PII data
BI connectivity (Looker, Metabase, Tableau, Power BI)
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
When row counts exceed ~100M rows in a single table and query performance degrades, when you need to join data from multiple source systems at scale, when your analytics workloads are impacting production database performance, or when you need to retain and query years of event data economically. Many businesses benefit more from a well-tuned PostgreSQL database than from a premature data lake.
A data lake stores raw data in its original format in cheap object storage (S3, GCS). A data warehouse stores structured, transformed data optimised for query. A lakehouse architecture (Delta Lake, Iceberg) provides warehouse-quality query performance directly on the data lake, with ACID transactions and schema enforcement, avoiding the need for a separate warehouse tier for many use cases.
Column-level masking in Snowflake or BigQuery to anonymise sensitive columns for analysts without sufficient clearance, row-level security for multi-tenant data, data classification tagging in the catalogue, audit logging of all data access, and defined data retention policies with automated deletion. GDPR and CCPA right-to-erasure requirements are built into data model design from the start.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
