2. Data Lakes & Lakehouses

I build modern lakehouse architectures that combine the flexibility of data lakes with the performance and governance of data warehouses — designed for scalability, cost-efficiency, and cloud-native operations.

Lakehouse Infrastructure

Deploy production-grade lakehouse platforms on your chosen stack:

• Apache Iceberg — Open table format with ACID transactions, schema evolution, time travel, and partition pruning for high-performance analytics

• Databricks — Unified governance, Delta Lake format, and integrated compute with Spark

• Snowflake — Managed cloud data platform with separation of storage and compute, instant elasticity, and zero-copy cloning

Compute Engines

Query and transform your data at any scale:

• Apache Spark — Distributed processing for large-scale ETL, ML pipelines, and batch analytics

• DuckDB / DuckLake — Embedded OLAP engine for fast local analytics and lightweight lakehouse queries

• Trino — Federated query engine for interactive analytics across multiple data sources

Data Ingestion

Reliable, scalable data ingestion from any source:

• Debezium — Change Data Capture (CDC) from relational databases with exactly-once delivery to your lakehouse

• Streaming ingestion — Real-time data landing with Kafka Connect, Spark Structured Streaming, or Flink

Key Capabilities

Table Format Design	Partitioning strategies, compaction, and optimization for query performance
Cost Optimization	Storage tiering, compute auto-scaling, and workload isolation
Data Governance	Access controls, lineage tracking, and compliance (GDPR, SOC2)
Multi-Cloud Strategy	Portable architectures that avoid vendor lock-in

Explore related topics:

• Data Storage

• Query Federation with Trino

• Database Extraction

• Data Governance