Amazon S3 by Bravas Technology
Enterprise ETL pipelines, structured SQL warehouse tables, and analytics-ready SafetyCulture data built on Amazon S3.
Features
Structured Data Lake Layers
SafetyCulture API data is organised into Bronze (raw), Silver (curated), and Gold (analytics-ready) datasets within S3. This layered approach separates ingestion from curated analytics outputs. It aligns directly with Medallion architecture principles
AWS-Native Transformations
Transformations are implemented using AWS-native services such as Glue or Spark-based processing. JSON flattening, schema mapping, and deduplication ensure consistent data quality. Incremental merge logic supports scalable enterprise refresh cycles.
Partitioned Storage Strategy
Data is partitioned by timestamp and batch identifiers to minimise query scanning and reduce cost. Structured folder design enforces separation between ingestion and reporting datasets. This improves performance and cost control.
Incremental Batch Processing
Watermark ingestion ensures that only updated records are processed in each cycle. Batch IDs enable reconciliation and traceability across Bronze, Silver, and Gold layers. This maintains enterprise-grade reliability.
BI Visualisations & Dashboards
Bravas designs and builds dashboards using Athena or Redshift connected to curated Gold datasets. KPI models support executive and operational reporting. Dashboards provide enterprise visibility into inspections, actions, and compliance performance.
Description
Bravas delivers a scalable, production-ready SafetyCulture data lake architecture on Amazon S3.
SafetyCulture data is extracted via REST API, landed into partitioned Bronze JSON storage, transformed into curated Silver Parquet datasets, and prepared for structured SQL warehouse modelling via Redshift or Athena.
The solution supports:
• Full and incremental endpoint ingestion
• Watermark-based updates
• Batch-level traceability
• Structured Bronze, Silver, and Gold lake layering
• Schema enforcement and partition optimisation
• Business-ready datasets for analytics and BI
• Watermark-based updates
• Batch-level traceability
• Structured Bronze, Silver, and Gold lake layering
• Schema enforcement and partition optimisation
• Business-ready datasets for analytics and BI
Amazon S3 serves as the foundational storage layer for scalable, enterprise-grade SafetyCulture analytics.
Testimonials
Tyler Mason
CTO at El Jannah
The Bravas team delivered an excellent end-to-end data solution for El Jannah, integrating SafetyCulture, 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
No - Amazon S3 provides storage only. AWS Glue, Spark-based jobs, or Athena are required for transformation and querying.
AWS Glue is commonly used for ETL processing and schema transformation. Other AWS-native compute services may also be used.
Costs include S3 storage, Glue compute (if used), and Athena or Redshift query costs for reporting.
No - dashboards connect through Athena or Redshift, not directly to S3 storage.
