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WEBINAR
How To Get Monitoring Right For Streaming 

And Fast Data Systems Built With Spark, 

Mesos, Akka, Cassandra and Kafka
Paul Jasek, Senior Director of Global Solution Architects
Agenda
1. Fast Data & Streaming Applications
2. The Challenges of Monitoring Fast Data Applications	
3. What To Look For In a Fast Data Application
4. Intelligent End-To-End Monitoring from Lightbend	
5. Live Demo
6. Questions
reactivemanifesto.org
Reactive Underpinnings: Fast Data and streaming applications
often incorporate, or are based on, Reactive principles
• Real-time	personalization		
• Real-time	decision-making		
• IoT	data	processing		
• Legacy	batch	processing	
modernization
Growing Number Of Use Cases Across Industries
• Serve	existing	customers	better	
and	reduce	churn	
• Attract	new	ones	and	drive	
growth	
• Launch	new	products	more	
easily		
• Enter	new	markets	more	quickly.
• Rapidly Evolving Ecosystem
• Understanding the Data Pipeline
• Dynamic Architectures
• Intricately Interconnected
• Distributed And Clustered
The Challenges of Monitoring Fast Data Applications
Apache Spark, As An Illustrative Example
The Challenges of Monitoring Fast Data Applications
Concern Questions	To	Ask
Data	Health	(for	a	
given	application)
• Throughput:	is	data	processing	occurring	at	the	expected	rate?		
• Latency:	is	data	processing	occurring	within	the	expected	timeframe?		
• Error/quality:	are	there	problems	with	the	data	being	produced?		
• Input	data:	are	input	data	streams	flowing	into	Spark	behaving	normally?	For	instance,	what	are	the	
throughput	rates	for	Kafka	topics	feeding	into	the	Spark	job?
Dependency	Health • Are	the	systems	feeding	input	into	the	storm	job	(such	as	Kafka)	healthy?		
• Are	the	systems	that	the	application	is	dependent	on,	such	as	Memcache	or	other	API	endpoints,	
healthy?	
Service	Health • Is	the	Spark	master	operating	normally?	If	not,	engineering	will	be	unable	to	re-balance	workloads	or	
restart	jobs.	
Application	Health • Are	the	application	KPIs	within	normal	operating	parameters?	
Topology	Health • Are	there	resources	assigned	to	the	given	Spark	topology?		
• •	Are	the	Spark	tasks	and	executors	well-distributed	amongst	the	Spark	cluster?		
• •	Are	the	performance	counters	(emitted,	failed,	latency,	etc.)	for	the	given	Spark	topology	normal?	
Node	System	Health • Are	the	key	system	metrics	(load,	CPU,	memory,	net-i/o,	disk-i/o,	disk	free)	operating	normally?
Can traditional monitoring tools help?
Why traditional monitoring tools won’t help you
• Built	to	monitor	monolithic	
applications	
• Can	only	be	used	to	extract	
metrics	and	trace	information	
based	on	a	synchronous	flow	
• Not	built	for	asynchronous	
flows	(i.e.	in	Fast	Data	and	
streaming	applications)	
• Cannot	easily	handle	streaming	
systems	running	on	distributed	
clusters
• Deep Telemetry
• Domain Expertise	
• Automated Discovery
• Real-Time Topology Visualization
• Intelligent, Rapid Troubleshooting
What users need to effectively monitor Fast Data and
streaming applications
• Lightbend Monitoring takes a modern approach to instrumenting and
visualizing distributed streaming systems	
• Helps users not just in production but also in development (so they can
build their applications right from Day 1) 	
• Shows the end-to-end status of applications, data frameworks, and the
associated infrastructure in a single view.
Intelligent, End-To-End Monitoring
• Deep Telemetry	
• Domain Expertise
• Intelligent Anomaly
Detection
• Fine-Grained
Visibility, with Drill-
Down Capabilities
Data-Science Driven Anomaly Detection
• Automated Topology
Discovery
• Automatic Metric
Collection
• Real-Time Topology
Visualization
Automated Discovery, Configuration & Topology
Visualization
• Single Pane of Glass
Visibility
• Rapid Root Cause
Analysis
• Reduced Mean-Time-
To-Repair (MTTR)
Intelligent, Rapid Troubleshooting
• Dramatically reduce the time and cost to identify and remediate issues across
application life-cycle.

• Create happier, more satisfied customers – and lower churn	
• Lower HW/infrastructure costs and reduce concerns about chargebacks & SLA
penalties
• Deliver rapid time to value because everything you need for monitoring is packaged
into an easy-to-use solution
Benefits for your business
On to the demo…
Upgrade your grey matter!

Get the free O’Reilly book by Dr. Dean Wampler, 

VP of Fast Data Engineering at Lightbend
bit.ly/lightbend-fast-data
End-To-End Monitoring For Your Fast Data 

And Streaming Applications From Lightbend
SET UP A 20-MIN DEMO

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How To Get Monitoring Right For Streaming & Fast Data Systems Built With Spark, Mesos, Akka, Cassandra & Kafka

  • 1. WEBINAR How To Get Monitoring Right For Streaming 
 And Fast Data Systems Built With Spark, 
 Mesos, Akka, Cassandra and Kafka Paul Jasek, Senior Director of Global Solution Architects
  • 2. Agenda 1. Fast Data & Streaming Applications 2. The Challenges of Monitoring Fast Data Applications 3. What To Look For In a Fast Data Application 4. Intelligent End-To-End Monitoring from Lightbend 5. Live Demo 6. Questions
  • 3.
  • 4.
  • 5. reactivemanifesto.org Reactive Underpinnings: Fast Data and streaming applications often incorporate, or are based on, Reactive principles
  • 6. • Real-time personalization • Real-time decision-making • IoT data processing • Legacy batch processing modernization Growing Number Of Use Cases Across Industries • Serve existing customers better and reduce churn • Attract new ones and drive growth • Launch new products more easily • Enter new markets more quickly.
  • 7. • Rapidly Evolving Ecosystem • Understanding the Data Pipeline • Dynamic Architectures • Intricately Interconnected • Distributed And Clustered The Challenges of Monitoring Fast Data Applications
  • 8. Apache Spark, As An Illustrative Example The Challenges of Monitoring Fast Data Applications Concern Questions To Ask Data Health (for a given application) • Throughput: is data processing occurring at the expected rate? • Latency: is data processing occurring within the expected timeframe? • Error/quality: are there problems with the data being produced? • Input data: are input data streams flowing into Spark behaving normally? For instance, what are the throughput rates for Kafka topics feeding into the Spark job? Dependency Health • Are the systems feeding input into the storm job (such as Kafka) healthy? • Are the systems that the application is dependent on, such as Memcache or other API endpoints, healthy? Service Health • Is the Spark master operating normally? If not, engineering will be unable to re-balance workloads or restart jobs. Application Health • Are the application KPIs within normal operating parameters? Topology Health • Are there resources assigned to the given Spark topology? • • Are the Spark tasks and executors well-distributed amongst the Spark cluster? • • Are the performance counters (emitted, failed, latency, etc.) for the given Spark topology normal? Node System Health • Are the key system metrics (load, CPU, memory, net-i/o, disk-i/o, disk free) operating normally?
  • 10. Why traditional monitoring tools won’t help you • Built to monitor monolithic applications • Can only be used to extract metrics and trace information based on a synchronous flow • Not built for asynchronous flows (i.e. in Fast Data and streaming applications) • Cannot easily handle streaming systems running on distributed clusters
  • 11. • Deep Telemetry • Domain Expertise • Automated Discovery • Real-Time Topology Visualization • Intelligent, Rapid Troubleshooting What users need to effectively monitor Fast Data and streaming applications
  • 12. • Lightbend Monitoring takes a modern approach to instrumenting and visualizing distributed streaming systems • Helps users not just in production but also in development (so they can build their applications right from Day 1) • Shows the end-to-end status of applications, data frameworks, and the associated infrastructure in a single view. Intelligent, End-To-End Monitoring
  • 13. • Deep Telemetry • Domain Expertise • Intelligent Anomaly Detection • Fine-Grained Visibility, with Drill- Down Capabilities Data-Science Driven Anomaly Detection
  • 14. • Automated Topology Discovery • Automatic Metric Collection • Real-Time Topology Visualization Automated Discovery, Configuration & Topology Visualization
  • 15. • Single Pane of Glass Visibility • Rapid Root Cause Analysis • Reduced Mean-Time- To-Repair (MTTR) Intelligent, Rapid Troubleshooting
  • 16. • Dramatically reduce the time and cost to identify and remediate issues across application life-cycle.
 • Create happier, more satisfied customers – and lower churn • Lower HW/infrastructure costs and reduce concerns about chargebacks & SLA penalties • Deliver rapid time to value because everything you need for monitoring is packaged into an easy-to-use solution Benefits for your business
  • 17. On to the demo…
  • 18. Upgrade your grey matter!
 Get the free O’Reilly book by Dr. Dean Wampler, 
 VP of Fast Data Engineering at Lightbend bit.ly/lightbend-fast-data
  • 19. End-To-End Monitoring For Your Fast Data 
 And Streaming Applications From Lightbend SET UP A 20-MIN DEMO