Big Data Development Services
Architect, process and analyse data at any volume
What are Big Data Services?
Most organisations do not have a data shortage. They have data spread across a billing system, a CRM, a warehouse management tool and a dozen spreadsheets, with no single place where those sources agree with one another. Reports take days to assemble, numbers differ depending on who produced them, and decisions get made on instinct because the evidence arrives too late to be useful.
We build the layer that fixes that. A data lake that holds raw and processed data together, pipelines that move records continuously instead of overnight, and a modelling layer that defines once what a customer, an order or a shipment actually means. The result is one source of truth that finance, operations and leadership can all query without arguing about whose number is right.
What We Deliver
Data Lake Architecture
Centralised storage on AWS S3, Azure Data Lake or Google Cloud Storage, organised into raw, cleaned and curated zones. We define partitioning, file formats and retention so queries stay fast and storage costs stay predictable as volume grows.
Real-Time Stream Processing
Streaming pipelines built with Apache Kafka, Spark Streaming and Flink for data that cannot wait for a nightly batch - transaction monitoring, sensor telemetry, live inventory. Events are processed in seconds, with replay and exactly-once guarantees where correctness matters.
ETL and Data Integration
Automated extraction from ERP, CRM, IoT devices, flat files and third-party APIs, with transformation, validation and deduplication built into the pipeline. Failed loads are logged, alerted and re-runnable rather than silently dropping records.
Data Warehousing
Dimensional models on Snowflake, BigQuery, Redshift or Databricks, designed around the questions your business actually asks. Clear fact and dimension tables mean analysts write simple queries instead of reverse-engineering source system quirks.
Analytics and Visualisation
Dashboards in Power BI, Tableau or Metabase, plus custom interfaces where an off-the-shelf tool does not fit. Every metric traces back to a defined calculation, so a number on a dashboard can always be explained and audited.
Data Governance and Quality
Automated quality checks, lineage tracking and access controls so you know where each figure came from and who can see it. Essential where data crosses regulated boundaries or feeds external reporting.
Problems This Solves
“Reports take days to produce”
Analysts spend more time collecting and reconciling data than interpreting it. Automated pipelines remove the manual assembly step entirely.
“Different systems give different answers”
A shared modelling layer defines each metric once, so revenue means the same thing in every report.
“Storage and query costs keep climbing”
Partitioning, compression and tiered storage typically cut cloud spend substantially without reducing what is retained.
“Nobody trusts the numbers”
Lineage tracking and automated quality checks make every figure traceable to its source, which is usually what restores confidence.
How We Work
Data Audit
Map every source, its volume, update frequency and quality issues.
Architecture Design
Choose storage, processing and modelling approach against your real query patterns.
Pipeline Build
Implement ingestion, transformation and validation incrementally, source by source.
Enable and Hand Over
Dashboards, documentation and training so your team can run and extend it.
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