Core specialty · Databricks solutions & support
End-to-end Databricks architecture, custom application development, data and AI engineering, multi-cloud migration, governance, optimization, and production support for Federal missions and Commercial enterprises.
Architecture through operations
Innovative BI designs Databricks account, workspace, catalog, storage, compute, network, identity, and deployment patterns for AWS and Azure. Architecture addresses environment isolation, private connectivity, customer-managed keys where required, secrets, service principals, serverless and classic compute choices, policy controls, audit logging, disaster recovery, and infrastructure as code.
We carry that foundation into Lakeflow ingestion and orchestration, Delta Lake and streaming data products, Unity Catalog governance, Databricks SQL and semantic analytics, MLflow model and agent lifecycles, Model Serving, custom Databricks Apps, CI/CD, observability, performance engineering, FinOps, and sustained platform operations.
Federal and Commercial delivery
Every engagement is shaped around the customer’s security boundary, regulatory obligations, cloud estate, delivery model, data sensitivity, and measurable business or mission outcome.
Databricks solution portfolio
Account and workspace topology, Unity Catalog metastores, cloud storage, identity federation, private connectivity, compute policies, environment isolation, infrastructure as code, and disaster-recovery design.
Assessment, dependency discovery, workload rationalization, code conversion, data movement, reconciliation, dual-run, cutover, and decommissioning for legacy warehouses, Hadoop, Spark, ETL, BI, and ML estates.
Lakeflow Connect, Jobs, and declarative pipelines; Auto Loader, batch and streaming, CDC, medallion architectures, data-quality expectations, materialized views, orchestration, and event-log observability.
Catalog and schema design, fine-grained access, attribute-based controls where applicable, lineage, discovery, classification, row filters, column masks, data sharing, auditability, and governed data and AI assets.
MLflow 3 tracking and evaluation, feature engineering, model registry, batch and real-time inference, Model Serving, vector search, retrieval-augmented generation, tool-using agents, tracing, guardrails, monitoring, and human feedback.
Secure Python or Node.js applications using Streamlit, Dash, Gradio, React, or Express for operational dashboards, RAG assistants, data-entry workflows, review queues, decision tools, and embedded data products.
Application engineering
Custom applications connect Databricks SQL, Unity Catalog-governed resources, Model Serving endpoints, OAuth-based authentication, APIs, enterprise systems, and operational workflows without creating another disconnected data stack.
Define users, decisions, data products, workflow states, security roles, latency, availability, and adoption measures.
Build Python or Node.js services, responsive interfaces, SQL and API integrations, governed retrieval, automated tests, and reusable components.
Package configuration and dependencies, automate CI/CD with Declarative Automation Bundles, promote across environments, and validate access.
Monitor application logs, audit events, telemetry, cost, user behavior, model quality, incidents, releases, and service objectives.
Migration factory
Migration patterns are selected after discovery; tools and sequencing vary by source platform, scale, latency, security, data-retention, and business-continuity requirements.
Inventory data, notebooks, jobs, pipelines, reports, models, dependencies, SLAs, security rules, usage, cost, and technical debt.
Define target architecture, migration waves, mapping rules, landing patterns, conversion accelerators, CI/CD, rollback, and acceptance criteria.
Execute schema, data, code, pipeline, model, and governance migration with reconciliation, performance baselines, security tests, and business validation.
Coordinate dual run, release readiness, change management, production cutover, hypercare, optimization, knowledge transfer, and legacy retirement.
Databricks support services
Support spans platform administration, DataOps, MLOps/LLMOps, AgentOps, application operations, SRE practices, service management, and continuous improvement.
Explore lifecycle supportMonitor Lakeflow runs, query performance, data quality, application telemetry, model and agent traces, utilization, cost, and service health.
Triage incidents, isolate dependencies, recover jobs and services, coordinate escalation, communicate impact, and validate restoration.
Administer identities, entitlements, policies, catalogs, lineage, secrets, model and prompt versions, audit records, and controlled releases.
Tune Spark and SQL workloads, compute, storage layout, pipelines, serving endpoints, applications, reliability, and cost-to-value.
Start a focused conversation
Share the Federal or Commercial context, cloud, source platforms, data scale, security boundary, target outcomes, and operating constraints.
