Turn Raw Data into AI-Ready Fuel with Data Engineering
We design and build the data infrastructure that powers your AI — scalable pipelines, governed warehouses, and real-time streaming architectures that turn messy data into competitive advantage.
Trusted by innovative teams worldwide
Proven Data Engineering Credentials
Certified across the platforms that power modern data infrastructure.
Complete Data Engineering Capabilities
From batch ETL to real-time streaming — we build the data layer your AI and analytics teams actually need.
ETL/ELT Pipeline Development
Reliable, idempotent data pipelines using Airflow, dbt, and Prefect — batch and micro-batch processing with built-in data quality checks at every stage.
Data Warehouse & Lakehouse Design
Modern warehouse and lakehouse architectures on Snowflake, Databricks, or BigQuery — optimized for both analytical queries and ML feature serving.
Real-Time Streaming
Event-driven architectures with Kafka, Flink, and Kinesis for use cases like fraud detection, live pricing, and real-time dashboards that demand sub-second latency.
Data Quality & Observability
Automated testing with Great Expectations, Monte Carlo, or custom validators — schema validation, freshness checks, anomaly detection, and alerting before bad data spreads.
Data Governance & Cataloging
Lineage tracking, access controls, PII detection, and automated cataloging — so your team can discover, trust, and audit data across the organization.
ML Feature Engineering
Feature stores and feature pipelines that serve consistent features to both training and inference — bridging the gap between data engineering and machine learning.
Your AI Is Only as Good as Your Data Layer
Book a free data architecture review and get a pipeline health report.
Data engineering isn't plumbing. It's your AI foundation.
Every AI initiative we've seen fail had the same root cause: bad data infrastructure. We build the pipelines, warehouses, and governance that make AI initiatives succeed.
Data Engineering Built for AI Workloads
Most data stacks weren't designed for ML. We build infrastructure that serves both analytics dashboards and production ML models with equal reliability.
Why Data Teams Trust OpenMalo
We've modernized data stacks for fintech startups processing millions of daily transactions and enterprises migrating from legacy warehouses.
Tell Us About Your Data Challenges
Share your current stack and pain points. We'll respond with a targeted assessment within 24 hours.
Our Engagement Process
Assessment
Full audit of your current data stack — sources, pipelines, storage, quality, costs, and bottlenecks identified.
Architecture Design
Target-state architecture with technology selection, cost projections, and migration plan tailored to your team size and timeline.
Pipeline Development
Iterative pipeline builds with CI/CD, automated testing, and data quality gates — deployed incrementally with zero downtime.
Monitoring & Optimization
Observability dashboards, alerting rules, cost optimization, and performance tuning across the full stack.
Handoff & Enablement
Documentation, runbooks, team training, and ongoing support to ensure your team owns and evolves the platform confidently.
What Our Clients Say
“OpenMalo migrated our entire data stack from a legacy Hadoop cluster to Databricks in 8 weeks — with zero data loss and 60% lower monthly costs. Their migration playbook was incredibly thorough.
“We were spending $14K/month on a Snowflake warehouse that was poorly optimized. OpenMalo restructured our pipelines and cut our bill to $5.8K while actually improving query performance. Remarkable.
“Their real-time fraud detection pipeline processes 2 million transactions per hour with 99.9% uptime. It caught $1.2M in fraudulent transactions in the first quarter alone.
60% Cloud Cost Reduction with Zero Downtime Migration
Data Stack Modernization for TrustBridge Capital
How we migrated a legacy Hadoop-based data warehouse to a modern Databricks lakehouse — reducing cloud costs by 60%, improving query performance by 8×, and enabling real-time ML feature serving.
Legacy data infrastructure blocking AI adoption
TrustBridge Capital's 6-year-old Hadoop cluster couldn't support real-time analytics or ML workloads. Query times had grown to 15+ minutes, and the team spent more time maintaining infrastructure than building features.
Our Approach: Parallel migration with dual-write pattern, automated data validation comparing old vs. new outputs, Databricks lakehouse with Delta Live Tables, and feature store integration — completed in 8 weeks.
Read Full Case StudyFrequently Asked Questions
We work across the modern data stack: Airflow, dbt, Prefect for orchestration; Snowflake, Databricks, BigQuery for warehousing; Kafka, Flink, Kinesis for streaming; and Great Expectations, Monte Carlo for data quality. We choose tools based on your needs, not our preferences.
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