Reverse ETL & Data Activation

Migrate legacy data warehouses to modern cloud-native platforms. Snowflake, BigQuery, Redshift, and Databricks specialization. Full ETL/ELT rebuild.

• ✦Legacy warehouse assessment
• ✦Schema conversion and migration

Build scalable data lakes with Delta Lake, Apache Iceberg, or Apache Hudi. Ingest, catalog, govern, and serve data for analytics and ML at any scale.

• ✦Multi-format data ingestion
• ✦Schema evolution and enforcement

Build real-time analytics pipelines with event streaming (Kafka, Kinesis), real-time dashboards, and live alerting. Track user behavior, system metrics, and business KPIs with sub-second latency.

• ✦Event streaming (Kafka, Kinesis, Pub/Sub)
• ✦Real-time dashboard (Grafana, custom)

Implement data mesh architecture: domain-owned data products, federated governance, self-service infrastructure, and automated discoverability. Decentralize data ownership.

• ✦Domain data product definition and ownership
• ✦Federated governance with global policies

End-to-end ML model lifecycle management: experiment tracking, model registry, CI/CD for ML, A/B testing, monitoring, and automated retraining.

• ✦Experiment tracking and comparison
• ✦Model registry with versioning and staging

Sub-second analytics on streaming data with pre-aggregation, materialized views, and real-time dashboards. ClickHouse, Druid, or Pinot managed service.

• ✦Sub-second query on billions of rows
• ✦Streaming ingestion (Kafka, Kinesis, Pub/Sub)

Enforce data contracts between producers and consumers. Schema registry, breaking change detection, compatibility modes, and automated documentation.

• ✦Centralized schema registry (Avro, Protobuf, JSON)
• ✦Breaking change detection and prevention

Evaluate, compare, and benchmark LLMs: accuracy, latency, cost, safety, and fairness. A/B test models in production with automated evaluation pipelines.

• ✦Multi-model evaluation (OpenAI, Anthropic, Gemini, local)
• ✦Accuracy, latency, cost benchmarking