Can you help optimise our existing platform without migrating?

Absolutely. Many engagements focus on optimising existing infrastructure — partitioning strategies, query tuning, cost controls, and governance improvements.

How do you handle data migration risk?

We use parallel-run strategies where old and new systems operate simultaneously. Data validation checkpoints and automated comparison tests ensure nothing is lost.

What if we are a small team with limited engineering resources?

We design for your team size. Simpler architectures with managed services often outperform complex setups that your team cannot maintain.

Do you provide ongoing support after the design phase?

Yes. We offer embedded advisory during implementation and can provide ongoing architecture reviews as your platform evolves.

How long does a typical data infrastructure advisory take?

Four to eight weeks depending on scope. A focused pipeline review takes four weeks; a full platform design with migration planning takes six to eight weeks.

Infrastructure Advisory

Data Infrastructure That
Scales With You

AI and analytics are only as good as the data platform underneath. We advise engineering leaders on lakehouse design, pipeline architecture, governance, and the build-versus-buy decisions that define long-term cost and agility.

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50+ Data platform designs delivered

70% Average reduction in pipeline maintenance effort

3 PB Largest data platform we have architected

What You Get

Advisory Deliverables

Architecture decisions documented with rationale, trade-offs, and implementation guidance.

Platform Architecture Design

End-to-end data platform blueprint covering ingestion, storage, processing, serving, and orchestration layers.

Pipeline Architecture Review

Assessment of existing ETL/ELT pipelines with recommendations for reliability, observability, and performance.

Lakehouse Design Document

Medallion architecture design with partitioning strategies, file formats, and query optimisation guidance.

Governance & Cataloguing Plan

Data cataloguing strategy, access control design, lineage tracking, and quality monitoring framework.

Cost Optimisation Analysis

Detailed cost modelling across storage, compute, and tooling with recommendations for reducing spend by 30-50%.

Technology Selection Matrix

Scored comparison of tools and platforms (Snowflake, Databricks, BigQuery, open-source) against your specific requirements.

Our Process

How We Advise

Current State Assessment

We map your existing data architecture, pipeline dependencies, cost drivers, and pain points through interviews and system review.

Requirements Definition

We work with stakeholders to define the target state based on analytics needs, AI workloads, compliance requirements, and growth projections.

Architecture Design

We design the target platform with detailed component selection, integration patterns, and migration strategies.

Cost & Risk Modelling

Every design option is modelled for three-year TCO, operational risk, and vendor lock-in implications.

Migration Roadmap

A phased migration plan with rollback strategies, data validation checkpoints, and team training milestones.

Ready to Start?

Your Data Platform Should Be an Asset, Not a Liability

Whether you are modernising a legacy warehouse or designing from scratch, we can help you make decisions you will not regret.

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Who This Is For

Engineering leaders making foundational data platform decisions.

FinTech Data Teams

Design compliant data platforms that serve real-time fraud detection, regulatory reporting, and customer analytics simultaneously.

Enterprise Data Leaders

Modernise legacy data warehouses into lakehouse architectures without disrupting existing analytics workflows.

Growth-Stage Startups

Build a data platform that handles 10× growth without 10× cost — get the architecture right before scaling.

Healthcare Data Officers

Architect HIPAA-compliant data platforms that enable AI and analytics while maintaining strict access controls.

Why OpenMalo

Why OpenMalo for Data Infrastructure

We have designed platforms at every scale and know where complexity hides.

Platform-Agnostic Expertise

We have deep experience across Snowflake, Databricks, BigQuery, Redshift, and open-source stacks. No vendor bias.

Cost-Obsessed Design

We model three-year TCO for every recommendation because infrastructure decisions compound financially.

Migration Experience

We have guided dozens of migrations from legacy warehouses to modern platforms without data loss or downtime.

AI-Ready by Default

Every platform we design is built to serve both analytics and AI workloads — not just traditional BI.

Governance First

Cataloguing, lineage, and quality monitoring are integral to the architecture — not an afterthought.

Practical, Not Theoretical

Our consultants have operated data platforms in production. We know the difference between a diagram and a system that works.

Get Started

Let's Design Your Data Platform

Tell us about your current setup and where you need to go. We will start with an honest assessment.

Free 30-minute architecture consultation

Current state assessment in the first week

Vendor-neutral platform recommendations

Three-year TCO modelling included

Migration roadmap with risk mitigation strategies

Featured Case Study

Lakehouse Migration Cuts Query Costs by 45%

FinTech Case Study

Payments Company Modernises Data Platform

A B2B payments company was running a legacy Redshift cluster that cost $180K per year and could not support the real-time analytics and ML workloads the product team needed.

45%

Reduction in annual data platform costs

12×

Improvement in complex query performance

Hours of downtime during migration

The Challenge

A monolithic Redshift cluster that was expensive, slow for complex queries, and could not serve ML feature pipelines.

Complex analytical queries timing out at 30+ minutes

Data scientists could not access data without filing tickets

No data cataloguing — tribal knowledge was the only documentation

Storage and compute costs growing 40% year-over-year

Our Approach: We designed a Databricks lakehouse with medallion architecture, migrated 2.4TB of historical data with zero downtime using a parallel-run strategy, and implemented a data catalogue with automated lineage tracking. Annual costs dropped from $180K to $99K.

FAQ

Frequently Asked Questions

It depends on your workload mix. Snowflake excels at structured analytics; Databricks is stronger for ML and unstructured data. We benchmark both against your specific use cases.