Databricks consultants and Apache Spark engineers

Digiventi provides lakehouse architects who join your team to plan Databricks implementations or address gaps in an existing platform. They assess source systems and workload requirements before defining storage layouts and workspace boundaries on your chosen cloud.

Architecture work can cover Delta Lake table design and Unity Catalog configuration, including access policies and data lineage. Specialists translate data ownership and retention requirements into platform controls. For sensitive datasets, they review permissions against GDPR-related requirements with your security and privacy teams.

Apache Spark engineers build and repair batch or streaming pipelines using PySpark and Spark SQL. Bring them in when ingestion cannot keep pace with source systems or data processing delays affect downstream reporting.

Work can include incremental ingestion with Auto Loader, change data capture, Structured Streaming and Lakeflow Jobs orchestration. Engineers investigate skewed joins and shuffle overhead, then test changes against representative workloads. They add data quality checks and replay procedures so failed runs can be recovered without duplicating records.

Databricks consultants prepare governed datasets for reporting and machine learning, with attention to query cost and reproducibility.

• SQL analytics: Analytics engineers develop reporting tables and tune Databricks SQL warehouses. They reconcile business definitions with source data and investigate queries that delay dashboards.
• Machine learning engineering: Engineers configure MLflow experiment tracking and model registration, then connect training workflows to approved datasets. Work can include model serving integration and evaluation checks before a model version enters production.

Databricks support specialists investigate failed jobs and provide ongoing engineering support for production workloads. They trace failures through job runs and Spark execution plans, then correct the underlying code or configuration. Compute reviews examine cluster sizing and autoscaling against actual workload demand.

For migrations and runtime upgrades, engineers assess compatibility and validate results against existing outputs. They can move legacy Spark workloads into Databricks, introduce automated deployment through Databricks Asset Bundles, and document rollback procedures before production changes.