Hybrid Transactional/Analytical Processing Using Spark and In-Memory Data Fabrics

Track: Data Science and Machine Learning
Skill Level: Advanced
Room: Room A403
Time Slot: Fri 2/24, 4:00 PM
Tags: big data , spark distribution , fast data analytics
Abstract

Increasingly, businesses are trying to achieve better insights out of large amounts of streamed data by bringing it closer to real time processing power and querying from a live data mart. At the moment we are witnessing a convergence of workflows and technology platforms for real time, analytics, cloud, and in-memory processing. This allows us to effectively address time-sensitive business decisions that involve the volumes of big data while benefiting from the velocities of real time processing. This involves real time analytics capabilities for price optimizations, fraud detection, risk calculations, and operational business intelligence.

In this session, we present a fast data pipeline solution architecture combining in-memory computing and a high performance Spark distribution. We will examine a large-scale transportation use case who implemented this solution for IoT real-time operational intelligence against billions of edge/sensor data points.

Ali Hodroj

Ali Hodroj is Vice President of Products and Strategy, In-Memory Computing at GigaSpaces. He also has held several other strategic roles at the company, including Director of Solution Architecture and Technology Evangelist, helping leading organizations in finance, retail, telecommunications, and transportation. Prior to joining GigaSpaces, he served in technical roles at companies including GE, Intel, and Tektronix.

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