Google Cloud data and AI consultants: analytics and ML

Digiventi provides BigQuery consultants and Google Cloud data engineers to address slow reporting and unreliable ingestion. They assess existing workloads before changing warehouse architecture.

• Warehouse development: Build SQL models with Dataform or dbt, define partitioning and clustering, and reconcile migrated datasets against source records.
• Pipeline engineering: Implement batch or streaming ingestion with Dataflow and Pub/Sub. Add validation checks and replay procedures for failed loads.

Performance reviews cover query execution plans and reservation usage, with recommendations tied to measured workloads.

Looker consultants turn inconsistent reporting requirements into governed LookML models and usable dashboards. They work with business owners to define measures such as revenue recognition or order fulfilment, then trace those definitions to warehouse data.

LookML developers configure Explores and access filters, review join behaviour, and test changes before release. For existing deployments, specialists investigate incorrect totals or slow dashboards and maintain embedded analytics integrations. Metric definitions stay documented alongside the models so reporting changes can be reviewed against agreed business rules.

Vertex AI engineers take machine learning experiments into production or develop Gemini applications against approved enterprise data. Initial assessments establish evaluation criteria and check whether source data supports the intended use.

• Machine learning engineering: Build training pipelines and model-serving endpoints. Configure versioning and monitoring, with rollback procedures for releases that fail acceptance checks.
• Generative AI development: Implement retrieval-augmented generation with Vertex AI and Gemini. Test retrieval quality and response grounding, then apply data-access boundaries and request logging.

Google Cloud data platform specialists maintain existing workloads and address operational gaps across analytics and AI services. Support can cover incident investigation and release reviews, including failed scheduled queries or broken reporting dependencies.

Governance specialists configure IAM permissions and BigQuery policy tags, then review retention rules against documented GDPR requirements. Engineers add Cloud Monitoring alerts and recovery runbooks for production workloads. Architecture reviews examine data lineage and service dependencies before a migration or significant platform change.