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Big Data In Action With Infinispan

Big Data In Action With Infinispan

Dealing with real-time, in-memory, streaming data is a unique challenge and with the advent of the smartphone and IoT (trillions of internet connected devices), we are witnessing an exponential growth in data at scale.

Building data layers that can satisfy these requirements can be challenging, but with the help of Infinispan, an in-memory data grid from Red Hat, you can take advantage of state of the art distributed data processing capabilities to tackle these challenges. From classic or full-text queries, to Spark/Hadoop integrations via distributed Java Streams, these wide ranging data processing capabilities make Infinispan the perfect choice for the Big Data era.

In this session, we will identify critical patterns and principles that will help you achieve greater scale and response speed. On top of that, you will witness how Infinispan follows these patterns and principles to tackle a big data situation via a live coding demonstration.

Galder Zamarreño

September 07, 2017
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  1. What is a imdg? • Distributed in-memory data • Server

    "mesh" • Peer-to-peer (P2P) • No master/slaves • No single bottleneck • No single point failure • Commodity hardware
  2. infinispan is a imdg Custom Applications Mobile Applications Web Apps

    & Websites JBoss Middleware Fuse "memory" across machines into a unified data store Read-through, write-through, write-behind • NoSQL • Extreme Performance • Linear Scalability • Fault Tolerant • Event processing • Configurable ACID Txn Infinispan Databases and/or file system Analytical Framework
  3. Infinispan Use Cases Event Broker listen to data changes continuous

    query Data Analytics map/ reduce via java stream spark/ hadoop integration Distributed Cache cache frequent data transient short-lived storage NoSQL Database key/value store ACID transactions
  4. Real-Time Demo Continuous Query Verticle Http App Verticle Data Grid

    Replication Sock JS Bridge Real Time Laptop Http Websockets JavaFX Injector Verticle
  5. What is the time of the day when there is

    the biggest ratio of delayed trains?
  6. Analytics Demo Data Grid Replication Delay Calculator Server Task Delay

    Calculator Server Task Delay Calculator Server Task Analytics Verticle Injector Verticle Analytics Jupyter Laptop HTTP
  7. credits Approve by Aha-Soft from the Noun Project engineer by

    Wilson Joseph from the Noun Project transformation by Felipe Perucho from the Noun Project analytics by Roman Kovbasyuk from the Noun Project Database sharing by YuguDesign from the Noun Project Server by designify.me from the Noun Project