Data Platforms & Tools
What We Build
End-to-end data platforms that turn messy data into competitive advantage
Real-Time Data Pipelines
Streaming architectures with Kafka, Flink, and Spark for sub-second data processing at scale.
- Event streaming
- CDC pipelines
- Stream processing
- Real-time ETL
Data Warehousing
Modern data warehouses on Snowflake, Redshift, and BigQuery optimized for analytics workloads.
- Data modeling
- Query optimization
- Incremental loads
- Materialized views
Data Lakehouse
Unified data architectures combining the flexibility of data lakes with warehouse performance.
- Delta Lake
- Iceberg tables
- ACID transactions
- Time travel
Business Intelligence
Self-service analytics with Tableau, Looker, and custom dashboards for data-driven decisions.
- Custom dashboards
- Metric definitions
- Self-service BI
- Embedded analytics
Data Quality & Governance
Automated data quality checks, lineage tracking, and governance frameworks for trusted data.
- Data validation
- Lineage tracking
- Data catalogs
- Access controls
Big Data Processing
Distributed computing with Spark, Presto, and Trino for petabyte-scale batch processing.
- Spark optimization
- Distributed queries
- Job orchestration
- Cost optimization
Our Approach
Data-first, automation-heavy, and always production-ready
Data Discovery & Audit
Mapping your data landscape, identifying gaps, quality issues, and integration opportunities.
Architecture Design
Scalable data architectures with clear data flow, quality gates, and observability built in.
Pipeline Development
Production-grade pipelines with automated testing, monitoring, and error handling at every stage.
Analytics & Insights
Self-service dashboards, custom metrics, and ML-ready datasets for your teams.
Recent Projects
Data platforms that deliver measurable impact
Real-Time Recommendation Engine
Built a streaming data pipeline processing 10M events/sec for personalized product recommendations. Increased conversion by 35% and average order value by 20%.
Data Lakehouse Migration
Migrated a legacy data warehouse to a modern lakehouse on Databricks. 100TB+ of historical data, reducing query costs by 60% while improving performance 10x.