2. Modern Customer Engagement
• SMS
• Web Chat
• FB Messenger, Twitter DM
• Offers & Surveys
• Scheduled Callbacks
• Customer Context
• Behaviour Analytics
• Call Attribution to Campaigns
• Predictive Models
• Voice Calls
• Video
• Screen-share
Customer Journey Analytics
Connect the dots with live analytics and
AI to discover, analyse and predict
customer behaviour patterns.
Digital Messaging
Connect with customers by having live
web chat or SMS conversations, sending
targeted messages and offers.
Real Time Communications
Connect in real time using voice, video
and screensharing to engage with
exceptional customer service.
3. • Engage at the best time
• Accelerate revenue conversion
• Improve Customer Experience
• Resolve issues quickly
• Reduce calls / workload
• Increase First Call Resolution
• Reduce bounce and abandons
How Companies Benefit
7. Focus on real-time analytics
Make predictions on live visitors in real-time (in seconds) by:
Ingesting customer actions (events) and context
Building predictive models
Actions offered to customers based on real-time predictions
7
10. Engineering challenges
Product complexity
Communication platform
Data platform
Scale
Millions of events per day
Billions of events overall
Typically no stable
schemas
10
Real-time aspects
Response in second(s)
Streaming nature
Reliability
24/7 availability
Services go down
Servers disappear
14. Why Spark
Fast for iterative algorithms (important for Machine Learning)
Good integration with other tools (Kafka and Cassandra)
One code base for streaming and batch processing
Easy to deploy and maintain
Growing ecosystem (SQL, MLlib, GraphX, …)
Large open-source community
14
15. Data source: Kafka
Pub-sub message broker
Fast: 100s MBs /s on a single broker
Scalable: partitioned data streams
Durable: messages persisted and replicated
Distributed: Strong durability and fault-tolerance
Downside: requires ZooKeeper
15
16. Scalable storage
Easy to setup
High availability - no master
Great performance
CQL - SQL like querying
Great support and bug-free drivers from Datastax
Key: Design your schema around queries;
16
19. Goals for Analytics platform
Easy to scale
As real-time as possible
Performance vs. flexibility
~80% of queries known upfront
Limited resources
Low latency
19
21. Summary
Materialise views for buckets every minute
Hourly roll ups on raw events
Some numbers:
1bn+ events / day on 8 cores (Spark)
Sub-second query time
Lessons learned:
Know your data partitioning
Idempotent design is key!
21
23. AI platform
Goal: predict probability of customer X achieving goal Y
Train Models per Outcome and Business (1000s)
Apply models per each event in real time (5s)
Flexibility to add new data features on demand
Different dataset sizes forcing different algorithms
23
24. Spark ML Pipeline
“Decode” Spark ML pipeline & stages
Combine feature & model pipelines per-outcome
“Compose” per-outcome pipeline in streaming
Apply different pipelines per event in streaming batch
25. Key takeaways
Streaming over batch - highly reactive, low latency
Design for idempotent processing: things will always fail
Open source is great (most of the time) and cheap
macdab@altocloud.com
25
One code base for batch and streaming
Richer API (e.g. window functions)
HDFS is the only requirement (that is if you want to do checkpointing)
Fast
A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients.
Scalable
Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers
Durable
Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact.
Distributed by Design
Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
