Data, analytics and AI
Databricks Consulting and Delivery
Waverton Consulting Group delivers Databricks lakehouse engagements for Australian and New Zealand data and analytics teams: platform architecture, Apache Spark engineering, MLflow machine learning pipelines, Unity Catalog data governance and migration from legacy data platforms. Our engineers have delivered Databricks on AWS, Azure and Google Cloud across financial services, insurance, retail and professional services.
Databricks environments that are built without architectural discipline accumulate cost, complexity and technical debt. Waverton delivers engagements that are designed for the workload, governed from the start and handed over in a state your team can operate and extend.
Platform architecture and lakehouse design
Waverton delivers Databricks architecture engagements covering medallion lakehouse design, Unity Catalog metastore configuration and governance model, workspace and cluster design, networking and security architecture, and the cost controls that prevent unbounded compute spend. Architecture work is delivered as a defined engagement before implementation begins, producing a design document that your team can validate and that the delivery work is built against.
Spark engineering and data pipeline delivery
Apache Spark is Databricks' primary compute engine, and Waverton delivers Spark engineering work covering batch and streaming pipeline development, Delta Lake table management, performance tuning for large-scale workloads and the testing practices that make pipelines reliable in production. We work alongside your data engineering team or deliver pipelines as standalone engagements, with code review, documentation and the CI/CD pipeline configuration that makes deployment repeatable.
MLflow and machine learning platform
Databricks is a common choice for machine learning platforms because MLflow experiment tracking, model registry and serving are integrated into the workspace. Waverton delivers MLflow implementations and machine learning pipeline builds, working from feature engineering through model training, experiment tracking, model registration and deployment to serving endpoints. We work alongside your data science team rather than replacing it, providing the engineering depth that turns prototype models into production deployments.
Unity Catalog and data governance
Unity Catalog is Databricks' unified governance layer, providing fine-grained access control, data lineage tracking and audit capabilities across lakehouses and external data sources. Waverton delivers Unity Catalog implementations covering metastore architecture, privilege model design, data classification and the lineage configuration that satisfies data governance and compliance requirements for Australian organisations.
How to engage
Databricks platform builds, migrations and MLflow implementations with defined scope suit fixed bid delivery. Ongoing data engineering, pipeline development and platform optimisation run well as time and materials. Databricks engineers and architects can be embedded into your data platform team. For organisations wanting Waverton to manage their Databricks environment operationally, the managed service model provides ongoing delivery and platform oversight.
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