Databricks

One lakehouse for analytics, ML and GenAI — governed from day one.

Databricks is the platform ByteWave uses when data engineering, ML and GenAI need to live in one governed lakehouse. We deliver Databricks estates with Unity Catalog as the single source of truth, Delta Lake for reliability, MLflow for the model lifecycle, and Mosaic AI for production GenAI. Streaming, batch, SQL and Python — one platform, one governance model, one bill.

What we deliver

• 01
  Lakehouse platform foundations Multi-workspace architecture, Unity Catalog metastore, account-level identity, networking (VPC / VNet peering, PrivateLink), CMEK.
• 02
  Delta Lake & medallion architecture Bronze / Silver / Gold zones, Delta Live Tables, Structured Streaming, schema enforcement, time travel, OPTIMIZE / VACUUM, Z-ordering, liquid clustering.
• 03
  Unity Catalog governance Three-level namespace, catalogs, schemas, tables, views, functions, models, volumes. Row filters, column masks, ABAC, attribute-based access.
• 04
  MLflow & model lifecycle Experiment tracking, Model Registry, model serving, signature validation, champion-challenger, drift detection, Model-as-a-Service.
• 05
  Mosaic AI & GenAI Vector Search, Model Serving, AI Functions, AI Playground, RAG Studio, Agent Framework, fine-tuning, foundation model evaluation.
• 06
  Databricks SQL & analytics SQL warehouses, dashboards, alerts, query federation, BI integrations (Power BI, Tableau, Looker), Databricks One.
• 07
  Databricks Workflows & orchestration Job orchestration, Delta Live Tables pipelines, conditional tasks, repair & rerun, Git folders, Databricks Asset Bundles (DABs).
• 08
  Lakehouse Federation & Iceberg Federated queries to Snowflake, BigQuery, Redshift, Synapse. UniForm Iceberg reads/writes. Delta Sharing for cross-platform data products.

Engagement model

Lakehouse readiness assessment, target architecture & TCO, workspace bootstrap, Unity Catalog rollout, medallion zone design, MLflow / Mosaic AI enablement, model & pipeline deployment, FinOps and managed operations. Same team that built it runs it.

Outcomes you can measure

• Pipeline reliability 99.9% with DLT + Workflows.
• Data freshness from 24h → <5 min with Structured Streaming.
• Model deployment lead time cut 8× with MLflow + Model Serving.
• Warehouse spend optimized with cluster policies + DBSQL serverless.
• Single governance model across BI, ML and GenAI.