A leading provider of market and shopper information, predictive analytics and business intelligence to 95% of the Fortune 500 consumer packaged goods (CPG) and retail companies needed a more cost-effective and efficient approach to process, analyze and manage data. Zaloni designed and built the backend solution architecture for an enterprise data warehouse (EDW) augmentation solution to provide a more cost-effective, flexible and expandable data processing and storage environment.
Before the data lake: Tasked with managing massive volumes of data (ingesting hundreds of gigabytes of data from external data providers every week), the company struggled to keep costs low while providing clients with state-of-the-art analytic and intelligence tools.
With the data lake: Using Hadoop for the aggregate POS dataset and servicing the extractions that populate the company’s custom, in-memory analytics farm enabled the company to offload more data, faster, and realize substantial savings in a very short period of time.
Results: The company realized $5.2 million annual savings and $4.4 million projected additional savings upon (upon completion of the fine-grained data-at-rest modification project). Additionally, the company saw a nearly 50% reduction in mainframe MIPS (millions of instructions per second/processing power and CPU consumption) and better throughput with Bedrock than with only Hadoop.