Data volumes continue to grow, demanding new, more scalable solutions for low-latency data processing. Previously, the default approach to deploying such systems was to throw a ton of hardware at the problem. However, that is no longer necessary, as newer technologies showcase a level of efficiency that enables smaller, more manageable clusters while handling extreme workloads. Processing billions of events per second on Kafka can now be done with a modest investment in compute resources. In this session, you will learn how to architect and build the fastest data processing applications that scale linearly, and combine streaming data and reference data data-in-motion and data-at-rest with machine learning. We will take you through the end-to-end framework and example application, built on the Hazelcast Platform, an open source software engine designed for ultra-fast performance. We will also show how you can leverage SQL to further explore the operational data in the solution including querying Kafka topics and key-value data on the in-memory data store. Attendees will also get access to the Github sample application shown.

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Low-latency data
processing at Giga-scale
John DesJardins – CTO, Hazelcast

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Topics to Cover
✦ Why Latency Matters
✦ Enemies of Performance
✦ Anti-Patterns
✦ Architecture - Low-latency Processing with Kafka and Hazelcast
✦ Benefits
✦ Demonstration
✦ Learn More

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Why Does Latency Matter?
Latency Windows
✦ 300 milliseconds – Blink of an Eye
✦ 50 milliseconds – Maximum Latency Window for Credit Card Authorization & Fraud Checks
✦ Other Latency-Critical Moments:
• Time to Respond to Medical Device Alerts in ER
• Time to Respond to Industrial Equipment Failure
• Time Window to Prevent Shopping Cart Abandonment

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Enemies of Performance & Scalability
✦ Network Hops
✦ Storage I/O
✦ Processor – “Context Switching”
✦ Excessive or Unnecessary “Staged” Elements to Architecture
✦ Data Enrichment Requiring Added Data Retrieval

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Anti-Pattern for Low Latency – Lambda Architecture
Web Server
Web Client
Real-time
Events
Ingest
& Filter
Aggregated
Store
Kafka
✦ Excess Ping-Ponging of Data
✦ Data Stored and Then Aggregated
✦ Data Often Stored Again for Querying
✦ Doesn’t Lend Itself to Ad-Hoc Queries
✦ Operational Data Views Add Latency
✦ Reference Data Not Available for Enrichment
Real-time
Feed
Aggregate
& Store

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Anti-Pattern for Low Latency – “Delta” Architecture
✦ Repeated Persistence
✦ Repeated Network Hops
✦ Many Changes to Data
✦ Longest Lag from Data Birth to Value Creation
Ingest
“Raw”
Refine &
Enrich
Cleanse
d
Real-time
Analytics &
Machine
Learning
Data Lake
Batch Data
Data Is
Born
Action
Taken,
Value
Created

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Continuous Query with Drilldown
Process Flow
✦ A query kicks off a data ingest job
✦ Data is aggregated and indexed
into the in-memory store
✦ End users can drilldown into
trade details
via the web UI
Business Benefits
✦ Near-real-time visibility
✦ Cost-effective
✦ Reliable, high uptime
✦ Cloud-native for when move to
public cloud is desired
Technical Benefits
✦ No database to maintain, no storage of data copies
✦ Easily extensible architecture
Hazelcast Cluster
Web Server
Web Client
Real-time Events Data Store
Ingest
Continuous
Query 1
(load and agg)
Index
Query
Submit query
Cancel query
Data Source
(e.g., Kafka, Kinesis,
Log Files)

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Kafka Topic
Kafka Topic
Kafka Topic
Data Processing
Live Events
Analytics
Live Events
Analytics
Live Events
Analytics
Live Events
Analytics
Web Client
Architecture – Scale Ingest/Query Processing Together
Streaming Ingest Data Store Query
Streaming Ingest Data Store Query
Streaming Ingest Data Store Query
Streaming Ingest Data Store Query
Event Stream Input

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Data-in-Motion
“Events”
Streaming Analytics
Data-at-Rest
“Traditional Data”
Databases
UNIFIED
Why Is This Different?

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Advantages
✦ Partitioning of Both Compute & Data = Maximizing Parallel & Distributed Architecture
✦ Data-Aware Processing = Data Locality – Less Movement of Data
✦ Collaborative Worksharing = Simplifies Scalability
✦ In-Memory Optimized = Further Drive Down Latency

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Summary of Benchmark Stats
▪ 1 BILLION TPS with 99% under 30ms latency
▪ 45 nodes
▪ Linear scaling with predictable latency

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Continuous
Business
Events
Pipeline
MQ
Kafka
IoT
Custom
Connector
Enterprise
Applications
Hazelcast
File Watcher
Socket
Database
Events
Sources Sinks
MQ
Kafka
Alerts
Interactive
Analytics
Enterprise
Applications
Hazelcast
Databases
HDFS, S3,
NoSQL
Files
Actionable
Context
Hazelcast Platform
The Hazelcast Platform
Transform Combine Stream ML Inference
Stream and Batch
Processing Engine
Compute
In-Memory
Data Store
Storage
Persistence

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From Streaming Events to Data Applications
▪ Modular approach: streaming jobs become
building blocks for rich data applications,
connected by either operational data store or
messaging - Simplifies development, deployment
& scale
Hazelcast turns high-volume streams into data applications
▪ Built-in Operational Store Continuously serves
data to users
▪ Event-driven (push) or request-driven (pull)
access
▪ Indexed for concurrent, low-latency queries
Source Results
Operational
Data
Web Client
Sink
Enrichment Group & Filter
Aggregate
& Analyze

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Kafka Topic
Kafka Topic
Kafka Topic
Data Processing
Live Events
Analytics
Live Events
Analytics
Live Events
Analytics
Live Events
Analytics
Web Client
Architecture – Scale Ingest/Query Processing Together
Streaming Ingest Machine Learning
Streaming Ingest Machine Learning
Streaming Ingest Machine Learning
Streaming Ingest Machine Learning
Event Stream Input

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Demonstration – Real-
time Trade Processing

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Give It a Try or Learn More
✦ Giga-scale Benchmark Details
https://hazelcast.com/blog/billion-events-per-second-with-millisecond-latency-streaming-analytics-at-giga-scale/
✦ Trade Monitoring Solution Info
https://hazelcast.com/use-cases/risk-compliance-trade-monitoring/
Hazelcast Solution Demos Github Repo:
https://github.com/hazelcast/hazelcast-platform-demos
✦ Try Hazelcast 5.0 for free
https://hazelcast.com/get-started/
https://cloud.hazelcast.com/sign-up
✦ Learn More
Docs: https://docs.hazelcast.com/home/index.html
Training: https://training.hazelcast.com/
Blogs: https://hazelcast.com/blog/

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Thank You
john.desjardins@hazelcast.com
@johnmdesjardins