Data and AI teams creating one governed lakehouse
Support ingestion, engineering, SQL, BI, data science, machine learning, retrieval, and AI assets under shared governance.
Databricks lakehouse, Unity Catalog, Delta Lake, Lakeflow, SQL warehouses, data engineering, machine learning, AI, security, and operations
Databricks lakehouse engineering services
Rokad designs, builds, migrates, governs, and operates Databricks lakehouse platforms across data engineering, analytics, machine learning, and AI.
Platform fit / 01
Designed for teams with a specific platform requirement.
Databricks combines lakehouse storage, Spark processing, SQL analytics, orchestration, governance, data science, machine learning, and AI. Rokad designs account and workspace boundaries, cloud storage, Unity Catalog, Delta or Iceberg tables, Lakeflow pipelines and jobs, SQL warehouses, identity, CI/CD, observability, cost, and lifecycle operations.
Organisations migrating Spark, Hadoop, lake, or warehouse workloads
Move data and processing while redesigning storage, catalogues, pipelines, compute, security, quality, and operational ownership.
Companies standardising multiple Databricks workspaces
Align accounts, workspaces, metastores, catalogues, identity, clusters, serverless compute, network, deployment, cost, and governance.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
Workspaces become isolated project environments
Storage, catalogues, identities, libraries, clusters, notebooks, jobs, secrets, policies, and data ownership diverge.
Compute choices do not match workload economics
Interactive, jobs, SQL, serverless, shared, dedicated, autoscaling, photon, instance, and cluster policies are selected inconsistently.
Notebook delivery bypasses software engineering controls
Source, dependencies, tests, environments, data contracts, deployment, lineage, monitoring, and rollback remain informal.
Platform capabilities / 03
What Rokad can implement and operate.
01
Databricks account, workspace, cloud, network, identity, metastore, catalogue, compute, workload, usage, and cost assessment
02
Unity Catalog, catalogues, schemas, managed, external and foreign tables, volumes, lineage, permissions, audit, sharing, and governance
03
Delta Lake and supported open table formats, medallion or domain design, optimisation, retention, change data, and data quality
04
Lakeflow ingestion, declarative pipelines, jobs, workflows, orchestration, streaming, batch, retries, backfills, and observability
05
SQL warehouses, data modelling, semantic and BI integration, performance, concurrency, serverless, and workload isolation
06
Notebooks, repositories, packages, environments, tests, CI/CD, Databricks Asset Bundles, APIs, infrastructure code, and release workflows
07
Data science, MLflow, feature and model assets, vector and AI workloads, security, cost, support, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
Lakehouse foundation
Accounts, workspaces, cloud storage, networks, identity, Unity Catalog, catalogues, schemas, tables, volumes, and policies.
Engineering and orchestration
Lakeflow, jobs, notebooks, packages, streaming, batch, dependencies, tests, data quality, lineage, retries, and backfills.
Analytics and AI workloads
SQL warehouses, BI, data science, MLflow, feature, model, vector, application, and AI assets with governed access.
Databricks operations
Compute, policies, jobs, pipelines, freshness, quality, permissions, security, performance, cost, releases, incidents, and support.
Use cases / 05
Where this platform creates practical leverage.
Enterprise Databricks lakehouse
Establish cloud, workspace, Unity Catalog, storage, compute, ingestion, transformation, SQL, ML, governance, and operations.
Legacy data-platform migration
Move Spark, Hadoop, warehouse, ETL, lake, notebook, and machine-learning workloads with validation and continuity.
Databricks data-engineering platform
Build reusable ingestion, Lakeflow, testing, quality, lineage, orchestration, deployment, observability, and developer workflows.
Unified analytics and AI foundation
Connect governed tables and features to SQL, BI, notebooks, models, retrieval, agents, evaluation, and applications.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Unity Catalog defines the governance boundary
Design metastores, workspaces, catalogues, schemas, storage, identities, privileges, lineage, audit, and ownership before workload migration.
Compute is isolated by workload and trust
Select serverless, SQL, job, interactive, shared, dedicated, GPU, and policy controls from performance, data, security, and cost requirements.
Notebook exploration and production delivery are separated
Move reusable logic into versioned modules, pipelines, jobs, tests, assets, environments, and deployment workflows while preserving exploration.
Quality and governance / 07
Production controls are part of the implementation.
Governed data boundaries
Catalogues, schemas, workspaces, projects, domains, identity, classification, policy, lineage, audit, and ownership are explicit.
Tested and observable data
Contracts, freshness, completeness, validity, reconciliation, lineage, failures, backfills, and consumer impact are measurable.
Workload and cost isolation
Compute, storage, concurrency, priority, scaling, quotas, budgets, retention, and workload ownership protect performance and economics.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
Databricks account, workspace, storage, catalogue, compute, pipeline, security, usage, and cost assessment
Lakehouse, Unity Catalog, data, compute, pipeline, analytics, AI, and operating architecture
Cloud and Databricks infrastructure, workspaces, catalogues, storage, policies, and shared services
Lakeflow pipelines, jobs, notebooks, packages, SQL warehouses, models, ML, or AI assets
Testing, lineage, monitoring, security, performance, cost, CI/CD, backup, and recovery controls
Data, developer, ML, administrator, governance, operator, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Snowflake
Managed cloud data platform for warehousing, pipelines, sharing, applications, governance, and analytics.
02
Microsoft Fabric
Microsoft SaaS analytics platform spanning OneLake, engineering, warehouse, real-time, BI, and governance.
01
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
02
AI development
AI applications, agents, retrieval, evaluation, model integration, and intelligent workflows.
03
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
FAQ
Databricks lakehouse engineering services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Can Rokad implement Unity Catalog for existing workspaces?
Can Rokad implement Unity Catalog for existing workspaces?
Yes. We assess metastores, workspaces, storage, tables, identities, permissions, jobs, clusters, lineage, sharing, and migration constraints before staged adoption.
02Can Rokad migrate Spark or Hadoop workloads to Databricks?
Can Rokad migrate Spark or Hadoop workloads to Databricks?
Yes. We map data, formats, jobs, libraries, schedules, clusters, dependencies, security, performance, costs, tests, and cutover requirements.
03Can Databricks support both BI and AI workloads?
Can Databricks support both BI and AI workloads?
Yes. We design shared governance and data assets with workload-specific SQL, engineering, data science, model, vector, application, compute, and service boundaries.
04Can Rokad manage Databricks after launch?
Can Rokad manage Databricks after launch?
Yes. Managed scope can include pipelines, jobs, compute, SQL, data quality, catalogues, permissions, security, performance, cost, releases, incidents, and new data products.
Databricks · Data platform engineering
Build Databricks as a governed lakehouse platform across data, analytics, and AI.
Rokad can establish Unity Catalog and cloud foundations, migrate workloads, build Lakeflow and SQL systems, and operate quality, security, and cost.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.