BI and big data convergence in modern cloud architecture (sponsored by Arcadia Data)

Description

Procter & Gamble (P&G) is a leading consumer packaged goods (CPG) business with a product portfolio of household names like Tide and Gillette. The company relies heavily on data, particularly for business intelligence. P&G’s experience as a big data and Apache Hadoop veteran that has run Hadoop in production for several years proves that exploring new architectures and technologies—especially those that take advantage of the cloud—will provide a new paradigm shift in providing big data as a centralized service throughout an organization. For example, although running computation where the data lives is critical for performance, P&G has found added benefits to a decoupled architecture to complement our Hadoop infrastructure. This provides many opportunities for BI, analytics, and big data convergence with an atomic data layer service that can simplify ETL and data science workloads.

Terry McFadden offers an overview of P&G’s modern analytics architecture and explains how it differs from traditional approaches.

Topics include:

• How the modern data lake compares to the old atomic data layer
• How cloud technologies like object stores can enable flexible BI workloads
• The impact on ETL workloads with modified filesystem semantics of object stores
• The explosion in possibilities of analytics and data science workloads with cheap ad hoc clusters in the cloud

This session is sponsored by Arcadia Data.