Data Engineering and Database Management
Database architecture, migration, performance and data pipelines across MySQL, PostgreSQL, MongoDB and Azure Cosmos DB.
Overview
Data outlives the applications that create it. We design databases and data flows so information stays accurate, recoverable and usable as systems change around it.
We work with relational databases such as MySQL and PostgreSQL, including PostgreSQL extensions and managed PostgreSQL services, and with document databases such as MongoDB and Azure Cosmos DB where a flexible schema fits the domain better.
Engagements range from schema and index tuning on a single database to planning a migration between engines, building processing pipelines, and putting backup, retention and access policies on a sound footing.
Problems we address
- Queries that slow down as data grows and nobody knows why
- Backups that exist but have never been restored
- Migrations stalled by fear of data loss or downtime
- Data copied between systems by hand or brittle scripts
- Unclear ownership of who can read or change sensitive data
What we deliver
- Database architecture
- Engine selection, schema design, replication and high-availability topology.
- Performance optimisation
- Query analysis, indexing, connection management and capacity planning.
- Data migration
- Engine and platform migrations with validation, reconciliation and rehearsed cut-over.
- Backup and recovery
- Point-in-time recovery, retention policy and restore tests on a schedule.
- Data pipelines
- Batch and streaming ingestion, transformation and delivery between systems.
- Governance and lifecycle
- Access control, retention, archival tiers and data classification.
- Document database engineering
- Partitioning, consistency choices and cost-aware design for MongoDB and Cosmos DB.
Technology
Typical tools for this work. We choose per engagement; listing is not a claim of vendor partnership.
- MySQL
- PostgreSQL
- MongoDB
- Azure Cosmos DB
- MariaDB
- Redis
- Apache NiFi
Example use cases
Illustrations of the kind of work this covers.
- Moving a self-managed database to a managed cloud service with minimal downtime
- Diagnosing and fixing slow reporting queries
- Building a pipeline that reconciles orders between an ERP and an e-commerce platform
- Archiving historical records to low-cost storage with defined retention
Relevant industries: FMCG, Goods and Distribution, Manufacturing and Industrial
Delivery approach
Discover
Understand the business goal, the systems in place and the constraints: budget, compliance, skills and timelines.
Design
Agree an architecture and delivery plan, with the trade-offs written down and the risks named early.
Build
Deliver in small, reviewed increments with automated tests, infrastructure as code and regular demonstrations.
Launch
Release through pipelines with monitoring, rollback paths and runbooks in place before go-live.
Operate and improve
Run, measure and refine, or hand over fully documented to your own team.
Related services
- AI, ML, MLOps and AIOpsThe infrastructure and pipelines that take machine learning from a notebook to a monitored production service, and AI applied where it measurably helps.
- API and Integration ManagementAPI design, gateways, authentication and event-driven integration that connect your systems without turning them into a tangle.
- Cloud EngineeringArchitecture, migration and operations on Azure, AWS, Google Cloud, Oracle Cloud and IBM Cloud, designed for resilience and cost you can explain.
Talk to us about data & databases
Share the problem, the systems involved and your timeline. An engineer will reply to discuss scope and next steps.
Request a consultation