This slide deck explores WSO2 Stream Processor’s new features and improvements and explain how they make an organization excel in the current competitive marketplace.
2. WSO2 Stream Processor
An open source, cloud native analytics product optimized
to create real-time, actionable insights for agile digital
businesses.
2
3. Stream Processing
3
● Need to write code
● Complex deployment (5 - 6 nodes)
● Inability to change fast
Challenges
Source : http://cdn.business2community.com/wp-content/uploads/2012/08/Invest_Money_Photoxpress_2938045.jpg
4. WSO2 Stream Processor
4
● Need to write code
○ Streaming SQL + Graphical Editor
● Complex deployment (5 - 6 nodes)
○ 2 node minimum HA deployment (scale beyond with
Kafka)
● Inability to change fast
○ Query templates, editor
The solution
6. • Lightweight and lean
• Easy-to-learn streaming SQL with graphical editor
• High performance analytics with just 2 nodes (HA)
• Native support for streaming machine learning
• Long-term aggregations from seconds to years
• Highly scalable deployment with exactly-once processing
• Tools for development and monitoring
• Tools for business users to write their own rules
Overview of WSO2 Stream Processor
7. Market Recognition
● Named as a Strong Performer in The Forrester Wave™: Big Data
Streaming Analytics, Q1 2016
● Highest score possible in 'Acquisition and Pricing' criteria, and among
second highest scores in 'Ability to Execute' criteria
● The Forrester report notes:
“WSO2 is an open source middleware provider that includes a full spectrum of
architected-as-one components such as application servers, message brokers, enterprise
service bus, and many others.
Its streaming analytics solution follows the complex event processor architectural
approach, so it provides very low-latency analytics. Enterprises that already use WSO2
middleware can add CEP seamlessly. Enterprises looking for a full middleware stack that
includes streaming analytics will find a place for WSO2 on their shortlist as well.”
7
12. 1. Streaming data preprocessing and transformation
2. Data store integration
3. Streaming data summarization from seconds to years
4. KPI analysis and alerts
5. Event correlation and trend analysis
6. Real-time prediction and streaming machine learning
7. Service integration
8. Improved JSON processing support
Supported Streaming Analytics Patterns
16. • Generate dashboard and widgets
• Fine grained permissions
– Dashboard level
– Widget level
– Data level
• Localization support
• Inter-widget communication
• Shareable dashboards with widget state persistence
Dashboards
30. • High performance
– Process around 100k events/sec
– While most other stream
processing systems need around
5+ nodes
• Zero downtime
• Zero event loss
• Simple deployment
with RDBMS coordination (no
ZooKeeper, Kafka, etc.)
• Multi data center support
Minimum HA with 2 Nodes
Stream Processor
Stream Processor
Event Sources
Dashboard
Notification
Invocation
Data Source
Siddhi App
Siddhi App
Siddhi App
Siddhi App
Siddhi App
Siddhi App
Event
Store
31. Distributed Deployment with Kafka
• Exactly-once processing
• Fault tolerance
• Highly scalable
• No back pressure
• Distributed development configurations via annotations
• Pluggable distribution options (YARN, K8, etc.)
36. Success Stories
Experian makes real-time marketing channel decisions under 200
milliseconds using WSO2.
Eurocat built their next generation shopping experience by integrating
iBeacons and IoT devices with WSO2.
Cleveland Clinic makes real-time clinical decisions and comparative
analysis using
United Airlines improved passenger wait times and logistics via real
time IoT data and predictions.
36
37. Success Stories
37
a
TFL used WSO2 real time streaming to create next generation transport
systems.
Uber detected fraud in real time, processing over 400K events per
second
CSI uses WSO2 streaming capabilities to integrate people, systems,
and things.
State of Arizona monitors and manages their Private PaaS in real time
to improve efficiencies and trim costs.