Home / Practice / Data Platforms
— Practice 01 · Foundations

Data platforms,
the boring substrate
that makes everything else fast.

Lakehouses, governed warehouses, streaming backbones, and the catalog layer that holds them together. The single source of truth your reports, models, and products all sit on top of.

// 01
120+
Pipelines in production
// 02
9 PB
Largest deployment under management
// 03
4×
Avg. query-latency improvement on migration
// 04
70%
Avg. infra-cost reduction post-rebuild
— The thesis

One place for the data.
One truth to argue from.

A data platform is the ecosystem that integrates the tools, technologies, and processes to manage the entire data lifecycle, from ingestion and storage through processing, analysis, and visualisation. It is the centralised hub where data from diverse sources is collected, cleansed, and organised, ready for reporting and decision-making.

We build them like products: with version control, contracts between producers and consumers, observability on every hop, and a FinOps model that holds up in finance review. Not a swamp; not a warehouse-of-warehouses. A platform.

The boring outcome: when someone asks "what was revenue last month?" there is exactly one answer, and the dashboard, the model, and the analyst's notebook all derive from the same lineage.

— What's inside

Anatomy of a platform

Five layers, each independently swappable, none optional. We've built and operated this stack in production for banks, telcos, retailers, and the public sector.

// 01

Ingest & integrate

Batch and streaming ingestion from operational systems, SaaS, files, and event buses. CDC where it matters; reverse ETL where it's earned.

Kafka Confluent Debezium Fivetran DLT ADF
// 02

Store

Open-format lakehouse on object storage with Iceberg / Delta or the right cloud-native warehouse when batch SQL is the actual job.

S3 + Iceberg Delta Lake OneLake Redshift Synapse
// 03

Process & transform

Spark, SQL, and stream processors with a clear medallion shape, bronze landing, silver conformed, gold modelled. Tested in CI before merge.

Spark dbt Airflow Glue Flink
// 04

Govern & secure

Catalog, lineage, and policy enforcement that's actually enforced. Row-level, column-level, audit-trailed. Privacy-by-design isn't a slogan.

Unity Catalog Purview Lake Formation Schema Registry
// 05

Serve & analyse

BI, semantic models, ML feature stores, and APIs. The same governed data, the same numbers, exposed for the way each consumer wants to use it.

Power BI Lakeview QuickSight Feast FastAPI
— Capabilities

What we actually do

Six core capabilities, mapped to what most engagements need. Pick one, pick all of them — most customers start with foundations and pipeline automation.

// 01

Data collection & integration

Wire in operational, SaaS, file, and streaming sources without rewriting your source-of-truth.

  • Batch & streaming ingestion
  • Change-data-capture (CDC)
  • API & SaaS connectors
// 02

Data storage

Lakehouse on Iceberg / Delta, or a governed warehouse — whichever fits the workload, sometimes both.

  • Lakehouse on S3 / ADLS
  • Cloud DW (Redshift, Synapse, Fabric)
  • Open table formats & partitioning
// 03

Data processing

Spark, SQL, and streaming compute organised into a medallion architecture, version-controlled and tested in CI.

  • Spark & serverless ETL
  • dbt models & semantic layers
  • Stream processing (Flink, ksqlDB)
// 04

Data quality & governance

Tests, contracts, and policy enforcement so producers can't accidentally break consumers in production.

  • Data contracts & expectations
  • Catalog & lineage (Unity, Purview)
  • Row / column-level security
// 05

Pipeline automation

Orchestration with retries, observability, and SLAs. Pipelines that don't break at 3am and tell you when they do.

  • Airflow / Workflows / ADF
  • CI/CD for data (sqlmesh, dbt cloud)
  • Alerting & on-call runbooks
// 06

Scale & performance

Right-sizing, query tuning, partitioning, and warehouse cost models that survive the next billing cycle.

  • Workload & cluster sizing
  • Query optimisation & indexing
  • FinOps reviews & chargeback
— Platforms

The technologies we build on.

Vendor-savvy, never vendor-led. We hold partner-tier certifications across every platform on this list. See the Partners page.

AWS

Amazon Web Services

Advanced Tier Services Partner

Lakehouses on S3 + Iceberg + Glue Catalog, EMR / serverless Spark, MSK / Kinesis for streaming, and Redshift where SQL is the right answer.

AZ

Microsoft Azure

Solutions Partner · Data & AI

Microsoft Fabric on OneLake, Synapse SQL pools and Spark, Azure Databricks with Unity Catalog, and Purview wired into the lot.

DB

Databricks

Consulting Partner

Workspace setup, Unity Catalog rollouts, Delta Live Tables, and SQL Warehouses. Whether on AWS or Azure, the lakehouse done right.

CF

Confluent

Data Streaming Partner

Kafka, Flink, Connect, and Schema Registry as the streaming backbone of your platform, including for real-time decisioning workloads.

— A typical engagement

From nothing to shipped.

Most platform engagements run on this rhythm. We've shipped this exact shape for retail banks, insurers, and a continental telco.

STEP 01 ·

Discovery & fit

Listen, ask hard questions, map the data estate. Output: a one-page architecture sketch and a candid view on platform fit.

→
STEP 02 ·

Foundation & landing zone

Accounts, networking, identity, IaC, catalog, and the FinOps model, locked before a single workload lands.

→
STEP 03 ·

Build & ship

Sprints with measurable outcomes per fortnight. We pair with your team - knowledge transfer is part of the deal.

→
STEP 04 ·

Operate & evolve

Hand over, run alongside, or run for you. The platform stays operable either way — that's the only acceptable outcome.

— Managed service

Business Insights Engine

Our managed data solution that runs your entire data platform — collection from multiple sources, pipeline operation, and the optimal performance of Power BI and downstream reports.

We believe every business, regardless of size, should have the ability to leverage their data. The Insights Engine makes that possible at a fraction of the cost of building and retaining an in-house data team.

  • Multi-source ingestion
  • Pipeline operation & SLAs
  • BI & report performance
  • Cost & performance reviews
  • On-call & incident response
  • Quarterly platform roadmaps
// platform · live
Insights Engine
INGEST
PIPELINE
QUALITY
BI
// SLA 99.9% REGION · ZA
— Signature engagement

Data Services
Hadoop Data Platform Migration.

Our team assisted the customer in refreshing their bank-wide on-premise Hadoop data platform by migrating their HDFS data onto S3-compatible storage, whilst minimising the impact on production workloads. We further assisted the customer to meet their strategic objective by creating a low-touch tool-as-a-service for file-based replication, anywhere.

/// problem

The central Hadoop data platform is successfully used across the organisation for many varied and critical workloads. However, the software and hardware have become out-of-date, constrained, and difficult to integrate with and prevent this customer from leveraging more modern, elastic cloud-based technology and analytical tools. HDFS was identified as a particular bottleneck because of limited capacity on the Hadoop Namenodes and ageing storage infrastructure.

Secondly, the customer required a way to manage data replication between several environments — on-premise, multi-cloud, and to/from potential PaaS solutions as well. Replication must be done as low-touch and seamlessly as possible — close to real-time, fully automated and elastic, and yet still simple to observe and maintain.

/// solution

We developed a tool to move data out of HDFS and onto S3-compatible storage, which leveraged cloud computing rather than the Hadoop compute. The tool integrated with the customer's bespoke Hadoop environment, ensured data was transferred accurately, and switched Hive tables and partitions iteratively as transfers completed. The tool required capability for recovery, observability, and auditing to ensure certainty as to what was copied where, as well as restore/revert functionality. Eventually, we migrated hundreds of millions of files, thousands of Hive tables and (in some cases) decades of data — while the majority of consumer or producer workloads were unaffected.

Then, to enable the customer's multi-cloud/hybrid/PaaS use cases, we developed an automated, elastic, serverless tool to replicate data between 70+ storage engines (including AWS, Azure, local storage, SMB, HDFS, and many more). With S3 targets, the tool performs 10–50% better than AWS S3 sync, including checksums. When data lands, it is possible to begin replicating it in less than a second. The tool can perform near real-time backups, which can be directly used for analytics and backup/restore. Time travel against S3 versioned buckets is also possible, allowing for point-in-time restore.

100M+
Files migrated
70+
Storage engines
10–50%
Faster than S3 sync
HDFS S3 REPLICATE RESTORE
— Bring us a data problem

Bring us the workload.
We'll bring the platform for it.

Architecture review, migration plan, or a hard greenfield build. Let's start with a 30-minute call.

Book a platform call See selected work