Data Platform

The challenge

Data is only valuable when it is accessible, trustworthy, and timely. Many organizations struggle with siloed databases, inconsistent definitions, and batch processes that deliver yesterday's answers to today's questions. A modern data platform breaks down these silos and creates a single source of truth that the entire organization can rely on.

Building that platform requires more than choosing the right storage engine. It demands a coherent approach to ingestion, transformation, governance, and consumption — ensuring that data flows from source systems into analytics and applications with full lineage and quality controls.

What we deliver

• Data platform architecture spanning ingestion, storage, processing, and serving layers
• Data lake and lakehouse implementations with medallion architecture for progressive refinement
• ETL/ELT pipeline development with orchestration, error handling, and retry logic
• SQL transformation layer development using dbt for modular, tested, and documented data models
• Data governance framework including cataloging, classification, lineage, and access policies
• Self-service data access layers that empower analysts without compromising security
• Performance tuning and cost optimization for large-scale analytical workloads

How we work

Mallow takes a use-case-driven approach to data platform design. Rather than building infrastructure in a vacuum, we identify the highest-value analytical and operational use cases first, then design the platform to serve them. This ensures early business impact and avoids over-engineering. We embed governance from the start so data quality improves as the platform grows.

Key technologies

• Microsoft Fabric
• Databricks
• Azure Data Lake Storage
• Azure Synapse Analytics
• Azure Data Factory
• dbt (data build tool)
• Microsoft Purview