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@PatrickMcFadin
Patrick McFadin
Chief Evangelist for Apache Cassandra
How to Build Data Pipelines for Real-Time
Applications with SMACK & Apache Kafka
1
The problem
Your
Magical
App
Sad solutions
SMACK
Spark
Mesos
Akka
Cassandra
Kafka
CassandraAkka
SparkKafka
Organize Process Store
Mesos
KafkaKafkaKafka
SparkSparkSpark
AkkaAkkaAkka
CassandraCassandraCassandra
CassandraAkka
SparkKafka
Organize Process Store
Managing Weather Data
Windsor California
67.3 F
Rainfall total: 1.2cm
Today:
High: 73.4F
Low : 51.4F
Yesterday:
High: 75.2F
Low : 52.3F
Our
Magical
App
Reactive and immediate
Batch
KillrWeather
KillrWeather
Windsor California
67.3 F
Rainfall total: 1.2cm
Today:
High: 73.4F
Low : 51.4F
Yesterday:
High: 75.2F
Low : 52.3F
https://github.com/killrweather/killrweather
Spark
Mesos
Akka
Cassandra
Kafka
Kafka
Kafka decouples data pipelines
Webinar - How to Build Data Pipelines for Real-Time Applications with SMACK & Apache Kafka
Webinar - How to Build Data Pipelines for Real-Time Applications with SMACK & Apache Kafka
The problem
Kitchen
Hamburger
please
Meat disk
on bread
please
The problem
Kitchen
The problem
Kitchen
Order Queue
Hamburger
please
Order
The problem
Kitchen
Order Queue
The problem
Kitchen
Order Queue
Meat disk
on bread
please
You mean a
Hamburger?
Uh yeah.
That.
Order
Order from chaos
Producer
Consumer
Topic = FoodOrder
Order from chaos
Producer
Topic = Food
Order
1
Consumer
Order from chaos
Producer
Topic = Food
Order
1
Order
Consumer
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order
4
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order
4
Order
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order
4
Order
5
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order
4
Order
5
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order
4
Order
5
Order from chaos
Producer
Topic = Food
Order
1
Order
2
Consumer
Order
3
Order
4
Order
5
Scale
Producer
Topic = Hamburgers
Order
1
Order
2
Consumer
Order
3
Order
4
Order
5
Topic = Pizza
Order
1
Order
2
Order
3
Order
4
Order
5
Topic = Food
Kafka
Producer
Topic = Temperature
Temp
1
Temp
2
Consumer
Temp
3
Temp
4
Temp
5
Collection API
Temperature
Processor
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Precipitation
Processor
Broker
Kafka
Producer
Topic = Temperature
Temp
1
Temp
2
Consumer
Temp
3
Temp
4
Temp
5
Collection API
Temperature
Processor
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Precipitation
Processor
Broker
Partition 0
Partition 0
Kafka
Producer Consumer
Collection API
Temperature
Processor
Precipitation
Processor
Topic = Temperature
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Partition 1
Temperature
Processor
Kafka
Producer Consumer
Collection API
Temperature
Processor
Precipitation
Processor
Topic = Temperature
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Partition 1
Temperature
Processor
Topic = Temperature
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Partition 1
Topic Temperature
Replication Factor = 2
Topic Precipitation
Replication Factor = 2
Kafka
Producer
Consumer
Collection API
Temperature
Processor
Precipitation
Processor
Topic = Temperature
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Partition 1 Temperature
Processor
Topic = Temperature
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
p
1
Temp
2
Tem
p
3
Temp
4
Temp
5
Partition 1
Temperature
Processor
Temperature
Processor
Precipitation
Processor
Topic Temperature
Replication Factor = 2
Topic Precipitation
Replication Factor = 2
Guarantees
Order
•Messages are ordered as they are sent by the
producer
•Consumers see messages in the order they were
inserted by the producer
Durability
•Messages are delivered at least once
•With a Replication Factor N up to N-1 server failures
can be tolerated without losing committed messages
CassandraAkka
SparkKafka
Organize Process Store
Mesos
Coming soon!
• May 4: How to Achieve High Throughput for Real-Time Applications with SMACK,
Apache Kafka and Spark Streaming
• May 18: How to Build Data Pipelines with SMACK: Storage Strategy using
Cassandra and DSE
• June 1: How to Build Data Pipelines with SMACK: Analyzing Data with Spark
• For the latest schedule of webinars, check out our Webinars
page: http://www.datastax.com/resources/webinars
Go get your SMACK on
Thank you!
Follow me on twitter: @PatrickMcFadin

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Webinar - How to Build Data Pipelines for Real-Time Applications with SMACK & Apache Kafka

Editor's Notes

  1. Dealing with data can be hard Too many choices
  2. We can wind up building some pretty messed up stuff
  3. Data pipelines consisting of many parts Kafka to organize Akka and Spark to process Cassandra to Store Mesos to manage everything
  4. We are building our app with these principles Some things need to be processed immediately Some in a batch after we have the entire picture
  5. This app exists. it’s called KillrWeather
  6. One order, no problem Multiple order. Chaos No set format
  7. Restaurant
  8. Restaurant
  9. Restaurant
  10. Restaurant
  11. Waiters provide order
  12. Waiters provide order
  13. Waiters provide order
  14. Waiters provide order
  15. Waiters provide order
  16. Waiters provide order
  17. Waiters provide order
  18. Waiters provide order
  19. Waiters provide order
  20. Waiters provide order
  21. Waiters provide order
  22. Waiters provide order
  23. Waiters provide order
  24. Waiters provide order
  25. Waiters provide order
  26. Waiters provide order
  27. Waiters provide order
  28. Waiters provide order
  29. Waiters provide order
  30. Waiters provide order
  31. Waiters provide order
  32. Data pipelines consisting of many parts Kafka to organize Akka and Spark to process Cassandra to Store Mesos to manage everything