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.

Transcript

1. Big Data in action with infinispan Galder Zamarreño Arrizabalaga 7th September 2017
2. Build Infinispan based infrastructure to store, search and process near real-time data and calculate analytics
3. real-time data Real-time data is challenging Delays can have big impact
4. data growth Exponential data growth (smartphone, IoT...etc) How to analyse it?
5. in-memory data grids IMDG
6. 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
7. 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
8. 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
9. Event Broker listen to data changes continuous query
10. continuous query Continuous Query combines complex querying with reactive data changes
11. Demo Domain Station board stop station train
12. Openshift Platform-as-a-Service (PaaS) Public or private Polyglot Based on Docker & Kubernetes
13. Vert.x Tool-kit for building reactive applications on the JVM Event-Driven & Non-Blocking Polyglot
14. Real-Time Demo Continuous Query Verticle Http App Verticle Data Grid Replication Sock JS Bridge Real Time Laptop Http Websockets JavaFX Injector Verticle
15. DEmo Real-time
16. Data Analytics map/ reduce via java stream spark/ hadoop integration
17. spark - hadoop Powerful analytics APIs Combo with Infinispan backend Separate process management
18. distributed java streams Extended Java 8 Stream API to data stored in
19. Distributed streams map(λ) λ λ
20. What is the time of the day when there is the biggest ratio of delayed trains?
21. 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
22. Demo ANalytics
23. Build Infinispan based infrastructure to store, search and process near real-time data and calculate analytics
24. 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
25. Thanks! • github.com/infinispan-demos/swiss-transport-datagrid • Branch: early17 • infinispan.org • openshift.com • vertx.io @galderz #infinispan