Amazon Redshift by Bravas Technology
Enterprise ETL pipelines, structured SQL warehouse tables, and analytics-ready Mitti data built natively in Amazon Redshift.
Features
Redshift Warehouse Modelling
Curated Mitti datasets are loaded into structured fact and dimension tables inside Redshift. Business rules, merge logic, and KPI calculations are implemented using SQL transformation patterns. The resulting warehouse supports scalable enterprise analytics and reporting.
S3 Staging Integration
Raw API data is landed into partitioned Bronze storage in Amazon S3 before being transformed into curated datasets. This separation of raw and curated layers aligns with Medallion architecture principles. It ensures clean promotion of trusted data into warehouse tables.
Incremental Load Optimisation
Watermark-based ingestion processes only new or updated inspection and action records. Batch identifiers support reconciliation and monitoring across transformation stages. This approach reduces compute costs while maintaining reliable refresh cycles.
Performance & Distribution Tuning
Sort keys and distribution styles are configured to optimise query performance across large Mitti datasets. Warehouse tables are tuned for cross-entity joins and high-volume reporting. This ensures responsive analytics at enterprise scale.
BI Visualisations & Dashboards
Bravas designs and builds executive and operational dashboards using Redshift-connected BI tools. KPI definitions and reusable reporting models are implemented on top of curated Gold tables. Dashboards support compliance, operational performance, and safety reporting across the organisation.
Description
Bravas delivers a scalable, production-ready integration from Mitti into Amazon Redshift.
Mitti data is extracted via REST API, staged in structured Bronze storage, transformed into curated Silver datasets, and loaded into relational Gold warehouse tables inside Redshift.
The solution supports:
• Full and incremental endpoint ingestion
• Watermark-based updates
• Batch-level traceability
• 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
• Structured SQL transformations
• Relational modelling for inspections, actions, users, groups, sites, and activity logs
• Business-ready marts for analytics and BI
Amazon Redshift serves as the final warehouse layer, optimised for high-performance analytical queries and enterprise reporting at scale.
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
Amazon Redshift provisioned clusters or Redshift Serverless are supported.
Yes - S3 is typically used for staging raw API data before loading into Redshift warehouse tables.
Transformations are implemented using Redshift SQL, including incremental merge patterns and dimensional modelling.
Only if querying external S3 data directly. It is not required if all curated data is loaded into Redshift tables.
Pricing includes compute (cluster or serverless) and S3 storage costs.
