Data and AI engineering on Databricks. We design and build lakehouses, data pipelines and AI applications with governed access, tested releases and an operating model your team can own.
A workspace is the starting point. DAQ connects source contracts, processing, analytics and AI into an implementation with clear ownership. We work from the workload and its constraints, whether the platform is new or already in production.
Source-specific ingestion into Delta tables, with Bronze retaining source data, Silver enforcing types and contracts, and Gold publishing business-ready datasets. We select managed connectors, Auto Loader or custom extraction to match each source’s change and deletion semantics.
In the deliverySource contracts, processing code and recorded quality checks.
Lakeflow pipelines and Structured Streaming for incremental processing, with checkpoints, event-time rules and replay procedures. Databricks SQL serves analytical queries; a separate serving path is designed when applications need operational reads.
In the deliveryFreshness targets, recovery tests and a defined serving contract.
Retrieval-augmented generation, tool-using agents and model delivery built around governed data. MLflow supports experiment tracking, tracing and evaluation. We test retrieval permissions, answer quality and tool behavior before release, then monitor the deployed application.
In the deliveryEvaluation datasets, versioned application code and release criteria.
Unity Catalog permissions, lineage and audit aligned with data ownership. Deployment and runtime identities are assigned deliberately. Application and reporting access are verified along each serving path, including the identity that actually reaches the data.
In the deliveryAn access model, environment boundaries and auditable permissions.
Declarative Automation Bundles, formerly Databricks Asset Bundles, version supported resources alongside their code. GitHub Actions can validate, deploy and run tests across environments. Runtime tests and approval gates establish release readiness; bundle validation alone does not.
In the deliveryA deployment repository, executed checks and an operations runbook.
A proposed Azure Databricks design for SQL, REST and file sources. Explore the source contracts, Bronze–Silver–Gold quality gates and GitHub release path. This is a design reference, not a claimed client delivery.
Permission-aware retrieval, answers with cited sources and MLflow evaluation as a release gate. See how the data and application boundaries connect.
Yes. We can scope a specific ingestion framework, streaming workload, governance model, release process or AI application. The assessment identifies what can stay, what needs a change and how that change will be verified.
Platform engineering establishes or improves the way data and AI workloads are built and operated. Migration adds source-to-target translation, reconciliation, coexistence and cutover planning for an existing estate.
Explore migration to DatabricksSource count and behavior, data quality, freshness requirements, access constraints and the target workloads. We use discovery to establish dependencies and acceptance criteria before proposing a delivery sequence and estimate.
Bring the outcome you need and the constraints around it. We can scope a focused assessment, a platform implementation or a production-readiness review.
Discuss your Databricks platform