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COLES

CASE - DATA PROCESSING

Data Factory/

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of our Work!

CLIENT
TECHNICAL STACK

Oracle Exadata, R, Unix, Control-M Scheduling, DataStage, Microstrategy

SKILLS & ROLES

1 Developer Data engineer

CUSTOMER BENEFITS

╋ Development of data modules loading 10 TeraBytes of data for fast SQL statements performance
╋ 13 external events added to the master table to optimize the forecast demands models prediction: Weather, events and festival, end date and promotions information products, …

WHAT IS THE CHALLENGE?

Massive data processing to rebuild 3 years of sales history with 150 attributes and measures describing each product in all the Coles stores in Australia.

 

WHAT ARE WE TALKING ABOUT?

In the Data Department of Coles, we are part of the team building a fast historical and Big table storing the historical sales of each Coles items (product) and external events with 3 years of history. The goal is to forecast demands and optimize the Supply Chain. Every Coles shops in Australia has a range of items – all the products in the shops. The Data Ingestion team is working with the Data Scientist team to build a Fast and massive historical master table. Data scientists trains their AI models with this table in order to forecast the customers demands in each shop.

 

WHAT ABOUT DELIVERY?

╋ Build a Master Oracle Exadata table in order to maximise batch processing and SQL Statements and provide detailed data to train the forecast demands Coles AI.
╋ Consolidation of the historical information for 50 millions of items – merged and curated from DWH data, enterprise operational systems and external sources
╋ External data sources ingestion: get from various interfaces the best valuable located information to enrich the AI models (for example: from Bureau of Meteorology/BOM and for each Australian shop, the weather forecast data from the closest BOM station).

Our experts

Rahul Kumar Pandey

Julien Labouze