Pipeline engineering
Batch and streaming pipelines built as code with tests, retries and alerting from day one.
Data engineering & warehousing
From raw data to trusted decisions
We design, build and run the data platforms that feed your dashboards, models and applications, with quality and governance built in from the first pipeline.
Overview
Data you can set your watch by
Data teams lose most of their time to broken pipelines, conflicting numbers and access requests. We engineer data platforms that are reliable by design: every pipeline is version-controlled, tested and monitored, and every metric has one governed definition.
Whether you are consolidating ERP and CRM data, streaming events in real time or preparing data for AI, we build on proven lakehouse and warehouse patterns and operate them with the same discipline as your production systems.
What you get
- Batch ELT and real-time streaming with change data capture
- Bronze, silver and gold lakehouse layers
- Enterprise data warehouse with a governed semantic layer
- Automated data quality tests, lineage and cataloguing
- Cost per query monitoring and optimisation
How it works
The data path, engineered end to end
Data moves from your sources through validated ingestion and a layered lakehouse into one governed warehouse that serves every consumer.
Sources
ERP & finance
CRM & SaaS apps
Operational databases
IoT & event streams
Files & partners
Ingest
Batch ELTScheduled loads
StreamingKafka & CDC
ValidateSchema & quality checks
Lakehouse
BronzeRaw, immutable landing
SilverCleaned & conformed
GoldCurated business models
Warehouse
Enterprise data warehouseSemantic layer, metrics and access policies in one governed model
Consume
BI & dashboards
AI & machine learning
APIs & applications
Governed end to end
Data qualityLineageCatalogueAccess controlEncryptionCost per queryCapabilities
A complete data engineering practice
Lakehouse & warehouse design
Modern lakehouse and warehouse architectures on Snowflake, Databricks, BigQuery and more.
Data quality
Automated tests for freshness, completeness and accuracy that stop bad data before it spreads.
Governance & security
Lineage, cataloguing, classification and fine-grained access aligned to PDPL requirements.
AI-ready data
Feature-ready, well-documented datasets that make analytics and machine learning faster to deliver.
Platform cost control
Query and storage optimisation with clear cost per workload, team and dashboard.
Outcomes
What a trusted data platform delivers
15 min
Typical data freshness target for operational dashboards
1
Governed definition for every business metric
100%
Pipelines under version control, tests and monitoring
Figures are LoopStack’s standard service targets. Final service levels are agreed per workload and set out in each contract.
FAQ
Data engineering questions
Which data platforms do you work with?
We build on Snowflake, Databricks, Google BigQuery, Azure Synapse and Microsoft Fabric, Amazon Redshift and open-source stacks, using tools such as Kafka, Airflow and dbt. We recommend the platform that fits your data, skills and budget.
What is a lakehouse, and do we need one?
A lakehouse combines low-cost storage for raw data with warehouse-style tables for analysis. It suits organisations with large or varied data, streaming sources or AI ambitions. For smaller, structured needs, a warehouse alone may be enough, and we will tell you so.
How do you handle personal and sensitive data?
We classify data at ingestion, apply masking and encryption, restrict access by role and keep lineage records, so you can demonstrate how personal data is processed in line with the PDPL.
Can you take over and fix our existing pipelines?
Yes. We assess existing pipelines, stabilise the critical ones first, add testing and monitoring, and migrate the rest to a maintainable pattern in planned stages.
Related services
Let’s keep you running
Book a 30-minute resilience review. We’ll map your critical workloads, your current recovery objectives and the fastest path to closing the gap.