Proof,
Not Promises.

Challenge

A 500-store retail chain was running a legacy Hadoop cluster built in 2016. Processing 10TB of daily transaction data took 14 hours. Overnight batch jobs failed 3× per week.

Solution

We designed a Databricks Lakehouse with Delta Lake medallion architecture. Built Kafka → Spark Streaming pipelines for real-time transaction processing and Unity Catalog for governance.

Challenge

A fast-growing payments platform was scaling from 10M to 500M daily transactions. Their Redshift warehouse ran at 95% capacity, costing $180K/month.

Solution

Redesigned with Data Vault 2.0 in Snowflake, dbt for all transformations, zero-copy cloning for 6 dev/test environments, and multi-cluster warehouse configuration.

Challenge

120 hospitals each defined "patient wait time" differently. Leadership was making decisions on Excel reports that contradicted each other.

Solution

Built a unified Azure Synapse platform with a certified semantic layer, standardized KPI definitions, Power BI Premium with role-based access, and trained 50+ analysts.

Challenge

Sensor data from 10,000 factory machines was being dropped because the pipeline couldn't handle 2M events/second. Unplanned downtime cost $4M per year.

Solution

Built a Databricks + Kafka streaming platform ingesting 2M events/second. Delta Live Tables for quality, MLflow for predictive maintenance, real-time anomaly detection.

Challenge

A $500M e-commerce company had a 3-person data team and 200 business users who needed data. Every report was a manual 2-week queue.

Solution

Built a Snowflake platform with dbt, a certified semantic layer in Cube, Power BI Premium for self-service, automated data quality checks, and a data catalog.

Challenge

A regional insurance provider lost 3 senior engineers. Delivery was stalled on a critical customer analytics project with a hard regulatory deadline.

Solution

Embedded 3 Aureus senior engineers. Completed the platform on time, migrated 40 legacy ETL jobs to Airflow, and upskilled the existing 5-person team.

Challenge

A fast-growing D2C brand with 800 SKUs was making inventory decisions manually. Stockouts cost $3.2M in lost revenue the previous year. Their Excel-based forecast ran monthly and hit 67% accuracy — not good enough at their scale.

Solution

Built an ML demand-forecasting platform on Databricks: an ensemble model combining gradient boosting with external signals — promotions, weather, social trends. MLflow tracked every experiment. Predictions refreshed daily and fed directly into their Snowflake inventory system via automated pipelines.