Databricks by Bravas Technology
Enterprise ETL pipelines, structured SQL warehouse tables, and analytics-ready Mitti data built natively in Databricks.
Features
Delta Lake Medallion Architecture
Mitti data is ingested into a Bronze Delta layer, transformed into structured Silver datasets, and promoted into Gold analytical tables using Delta Lake best practices. This ACID-compliant architecture ensures reliability, scalability, and consistent schema enforcement. The Medallion model aligns directly to enterprise data lakehouse design principles
Notebook & Job-Based Transformations
Transformations are implemented using Databricks notebooks and scheduled jobs, leveraging SQL and Python for schema mapping, flattening, and deduplication. Incremental merge logic ensures efficient updates of inspection and action datasets. Job orchestration maintains consistent batch-level traceability.
Unity Catalog Governance
Unity Catalog manages data access controls, lineage tracking, and governance across Bronze, Silver, and Gold layers. Permissions are structured to separate raw ingestion from curated analytics datasets. This supports enterprise compliance and controlled reporting environments.
Incremental & Partitioned Processing
Watermark logic ensures only updated records are processed during each execution cycle. Partitioning and Delta optimisation reduce compute overhead and improve query performance. Batch identifiers maintain traceability across data layers.
BI Visualisations & Dashboards
Gold datasets are exposed through Databricks SQL Warehouse for BI integration. Dashboards provide analytics across inspections, actions, user activity, and operational KPIs. The structured SQL layer enables consistent reporting across teams.
Description
Bravas delivers a scalable, production-ready integration from Mitti into Databricks.
Mitti data is extracted via REST API, landed into a Bronze Delta layer, transformed into curated Silver datasets using Spark-based processing, and modelled into structured Gold warehouse tables accessible via Databricks SQL.
The solution supports:
• Full and incremental endpoint ingestion
• Watermark-based updates
• Batch-level traceability
• Delta Lake optimisation and merge logic
• Structured SQL transformations
• Relational modelling for inspections, actions, users, groups, sites, and activity logs
• Business-ready marts for analytics and BI
• Watermark-based updates
• Batch-level traceability
• Delta Lake optimisation and merge logic
• Structured SQL transformations
• Relational modelling for inspections, actions, users, groups, sites, and activity logs
• Business-ready marts for analytics and BI
Databricks serves as both the transformation engine and analytics layer, delivering governed, scalable, and performance-optimised enterprise reporting.
Testimonials
Tyler Mason
CTO at El Jannah
The Bravas team delivered an excellent end-to-end data solution for El Jannah, integrating Mitti, Employment Hero, Deputy, and Sonder data into our Azure environment. Their structured approach and technical expertise gave us a reliable, scalable data foundation for reporting and operational insights. The implementation was professional, well-managed, and aligned to our broader data strategy.
Pricing
* The pricing here is for display purposes only. You should contact the partner for the most up to date and correct pricing information. We do not take any responsibility for this pricing information, which is provided by our partners. Pricing last updated: 24 Feb, 2026 12:00AM
FAQ
Databricks Premium or above is recommended.
Strongly recommended for governance and production deployments.
Charged via DBUs (Databricks Units) plus cloud infrastructure costs.
Power BI, Tableau, Looker, and JDBC/ODBC tools via SQL Warehouse.
