Your data is already valuable. The problem is you can't access it reliably.
MayuraSoft builds and delivers data infrastructure that turns raw, scattered, unreliable information into a clean, governed, always-available foundation — so your analytics, AI, and reporting always work from the truth.
Pipeline uptime SLA on managed infrastructure — vs. typical 94% on DIY pipelines
Faster query performance after warehouse optimisation — from hours to seconds on common queries
To first production-grade data pipeline from kickoff — Day 21 value, not month 6
Reduction in data team's pipeline maintenance burden — more time for analytics and product work
Core capabilities
What we deliver
End-to-end data infrastructure — from raw sources to decision-ready platforms.
Data ingestion
Collect data from source systems in both batch schedules and real-time streams, with monitoring and failure recovery built in.
Transformation and processing
Clean, join, and reshape data into consistent formats suitable for analysis, reporting, or downstream systems.
Storage and modelling
Design and implement data lakes for flexible storage and data warehouses optimized for structured query workloads.
Data quality and governance
Apply validation rules, lineage tracking, and access controls to ensure data is accurate, traceable, and appropriately managed.
Platform engineering
Build scalable, maintainable infrastructure using infrastructure-as-code, CI/CD pipelines, and automated deployment practices.
Reference architectures
Four proven data infrastructure patterns
Select a pattern to see the recommended stack and when to use it.
Modern lakehouse
Recommended for most organisations — balances cost, flexibility, and analytical power. Supports both batch and streaming, scales without a large upfront warehouse commitment.
Architecture pipeline
Data warehouse platforms
We work across all major platforms — and help you choose the right one
We're platform-agnostic. We recommend based on your workload, team, and cost profile — not partnerships.
Snowflake
PartnerGoogle BigQuery
CertifiedDatabricks
CertifiedData maturity levels
Where does your organisation sit? Click your level to see what it means and what to do next.
Most organisations who come to us are at Level 1 or 2. We meet you where you are and build toward Level 3 or 4.
What we build
Six data engineering capabilities
Click any service to explore deliverables and tools.
The transformation
What changes when your data infrastructure works properly
Switch between common scenarios to see the before and after.
A mid-size company has finance data spread across four different systems — an ERP, a payroll platform, a billing tool, and a CRM. Every Friday, the finance team manually exports from each system, reconciles in Excel, and spends Monday morning debating which numbers are correct.
The CFO asks for a weekly report every Monday. By the time the data is compiled, it is already out of date. Different teams report different revenue figures depending on which system they pulled from.
Manual, slow, inconsistent
- ProcessFinance pulls data from 4 systems manually every Friday. Takes 3 hours to reconcile and 2 more to format the report. Delivered Monday morning.
- AccuracyRevenue figures differ between finance, sales, and marketing by up to 8%. Every board meeting starts with a 20-minute argument about which number is correct.
- LatencyDecision-makers are looking at last week's data — by the time it arrives, the situation has already changed.
- Trust"I don't trust this number" is said in every data-related meeting. Teams make decisions on gut feel because they don't trust the data.
Automated, real-time, trusted
- ProcessAutomated pipeline runs at 6 AM every day. Report is in every stakeholder's inbox before they open their laptop. Zero manual intervention.
- AccuracySingle source of truth in the warehouse. Finance, sales, and marketing all see the same number because they're all pulling from the same governed data model.
- LatencyDecision-makers see yesterday's data today. For critical metrics, near-real-time streaming shows data updated within minutes.
- TrustData trust score (measured quarterly) moves from 40% to 87% within six months of platform launch. Teams act on data instead of debating it.
How to engage
Three ways to build your data foundation
Every engagement starts with a free data audit — we assess your current state before recommending a scope.
- Source-to-target pipeline design
- Data quality rules & monitoring
- Error alerting & retry logic
- Documentation & runbook handover
- Data architecture design & platform selection
- Ingestion layer across all your sources
- dbt transformation model build
- Data governance & cataloguing setup
- BI-ready semantic layer delivery
- Team training & knowledge transfer
- 24/7 pipeline monitoring & alerting
- Monthly performance & cost review
- New source onboarding (included)
- Quarterly architecture review
Common questions
What data and engineering teams ask before starting
Know exactly what's broken in your data infrastructure — and what to fix first
We'll review your current pipelines, warehouse, and data quality posture — and return a written audit with a prioritised improvement roadmap. All free, with no commitment required.
