Databricks Modernization Practice
A dedicated practice for moving legacy data, BI, and AI workloads onto the Databricks Lakehouse with Mosaic AI — engineered by certified Databricks practitioners and backed by Zorost’s eleven shipped AI platforms.
Unity CatalogDelta LakeMosaic AILakeflow / DLTAuto LoaderMLflow
Why Databricks — in one minute
For every customer profile we serve — federal agency modernizing legacy reporting, manufacturing group consolidating quality data, pharma R&D unifying evidence pipelines, freight platform serving real-time analytics, financial services group operationalizing ML — the same five forces converge on Databricks Lakehouse + Mosaic AI:
- One platform for SQL, ML, streaming, and agentic AI — instead of five.
- Open formats (Delta Lake, Iceberg) — no lock-in at the data layer.
- Unity Catalog — one governance and lineage plane across every workload.
- Mosaic AI — vector search, model serving, AI gateway, and agent framework, native to the lakehouse.
- Cost & performance economics — typically 30–60% lower TCO than legacy stacks for equivalent workloads, without rewriting business logic from scratch.
The challenge is not “should we modernize.” The challenge is how to modernize without breaking what works. That’s what this practice does.
The 10 Services in This Practice
Each service is delivered as a fixed-price assessment → fixed-price implementation → optional managed operations. All services are deliverable on Azure, AWS, and GCP.
1. Legacy BI Migration (OBIEE / Cognos / MicroStrategy / SAP BO → Databricks SQL)
RPD reconstruction, semantic-layer redesign, ETL conversion, report rebuild, parallel-run cutover, and 30/60/90-day hyper-care. We have internalized the OBIEE → Databricks pattern as a repeatable methodology: physical layer → BMM → presentation layer → Databricks SQL.
2. ETL Conversion (Informatica / SSIS / DataStage / Talend → Lakeflow / DLT)
Source-to-stage mappings → Auto Loader. SCDs → DLT apply_changes. Workflow scheduling → Databricks Jobs. Data-quality rules → DLT expectations with quarantine and metric capture.
3. Dimensional Modeling & Data Vault on Delta Lake
Star schemas, Data Vault 2.0, One Big Table, Lakehouse Federation, Bronze/Silver/Gold medallion modeling with clear ownership boundaries. Migration sequence and parallel-run plan included.
4. Streaming Pipelines (Structured Streaming + Auto Loader + DLT)
File-based ingestion (Auto Loader), event streams (Kafka, Event Hubs, Kinesis, Pub/Sub), CDC (Debezium), stateful transformations, DLT pipelines with managed dependencies and expectations, real-time alerting.
5. Unity Catalog Governance & Data Mesh Enablement
Catalog → Schema → Table hierarchy by domain. Data products with domain ownership. Row/column-level security with dynamic views and mask() / filter(). PII/PHI/PCI/federal classification. Lineage across SQL, ML, streaming, and BI. SIEM-wired audit logging.
6. Feature Engineering & MLOps (MLflow + Feature Store)
Centralized features with online + offline serving, point-in-time correctness, model registry with promotion policies, hyperparameter optimization at scale, Mosaic AI Model Serving with A/B traffic splits, drift and calibration monitoring. We report calibration (ECE) and conformal prediction intervals — not just accuracy.
7. Mosaic AI Implementation (Vector Search · Model Serving · AI Gateway)
Vector Search on Delta, Model Serving with SLAs, AI Gateway (routing / key mgmt / rate limits / cost control), Foundation Model APIs, SQL-callable AI Functions inside Databricks SQL. Hybrid retrieval (vector + BM25) with re-ranking and citation-grounded generation.
8. Agentic Workflows (Mosaic AI Agent Framework)
Single-agent and multi-agent workflows (planner / executor / critic patterns). Tools that call Databricks SQL, MLflow models, REST APIs, and external services with typed contracts. Agent evaluation with golden datasets, regression suites, and hallucination detection.
9. Power BI & Tableau on Databricks SQL (Direct Lake / DirectQuery)
Direct Lake mode for Power BI, native Databricks SQL connectors for Tableau, semantic-layer modeling with row/column-level security pushed down through the BI tool. SSO and audit integration.
10. FinOps & Cost Optimization on Databricks
Cluster policies, photon enablement, autoscaling tuning, SQL warehouse sizing, job orchestration review, storage tiering, monitoring dashboards with budget alerts. Typical engagements identify 20–40% recurring cost reduction within 30 days.
How We Engage
Phase 1 — Discovery (1–2 weeks, fixed-price). Source inventory, usage telemetry, criticality matrix, technical-debt assessment, target architecture, migration plan, risk register.
Phase 2 — Foundation (4–8 weeks, fixed-price). Unity Catalog deployment, naming and security standards, environment provisioning, observability and FinOps baselines, reference pipeline.
Phase 3 — Migration Sprints (per workstream, fixed-price). ETL conversion, semantic-layer migration, report rebuilds, ML/AI workload migration, agentic workflows. Each sprint ends in a parallel run and signed acceptance.
Phase 4 — Managed Operations (monthly, optional). SLA-backed support for pipelines, jobs, models, and BI workloads. Cost monitoring and tuning. Incident response.
Book a Modernization Assessment
Fixed-price discovery engagement, two-week turnaround. Deliverable is a migration plan with sequenced workstreams, target architecture, cost model, and risk register — ready to take into procurement.
