Kick-Start with SMACK Stack

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SMACK is a combination of Spark, Mesos, Akka, Cassandra and Kafka. It is used for pipelined data architecture which is required for the real time data analysis and to integrate all the technology at the right place to efficient data pipeline.

Software

Agenda:
● What is SMACK?
● Why SMACK?
● Brief introduction of technologies
● How to Integrate all the technologies to create the data pipeline
● Demo

What is SMACK?
● Spark :Apache Spark is a fast and general-purpose cluster
computing system.
● Mesos :Cluster resource management system that provide
efficient resource allocation.
● Akka :Akka is a toolkit and runtime for building highly
concurrent, distributed, and resilient message-driven
applications on the JVM.
● Cassandra :The Apache Cassandra database is the right
choice when you need scalability and high availability.
● Kafka :distributed messaging system for handling real
time data.

Why SMACK?
● Smack is used for pipelined data architecture which is
required for the real time data analysis.
● Smack is use to integrate all the technology at the right
place to efficient data pipeline.
● Smack is use to linearly scale your whole cluster without
any hassle

Why Spark?
● Its general purpose big data processing engine which have
4 main components spark core, spark streaming, spark
ml, spark graphx
● So we can process our data which any of the component
at real time.
● Its provide fault tolerant for real time application.

Why Cassandra?
● Cassandra implements “no single points of failure
● Cassandra Write-path is so fast so it can handle real-time data easily
● It will support Datacenter architecture so we can easily use different
DC for different things.
Ingestion DC Analysis DC
Cassandra Cluster

Why Mesos?
Mesos Master
Mesos Master
Standby
Mesos Master
Standby
Zookepeer
Mesos Slave
Mesos Slave
Mesos Slave

Models in SMACK
● In SMACK models are Scala and AKKA.
● We can use models to write highly concurrent and parallel
applications.
● Example: We can use akka modules according to our use
case like akka-http, akka-scheduler, akka priority
mailboxes etc.

Models use in SMACK
Akka-Http
Akka-Scheduler

Why Kafka
● streams of data efficiently and in real time
● Use Kafka for fault tolerance.
● To create bridge between two applications.
Streaming
Source
Kafka
Broker
Spark Receiver

Architecture of Spark and cassandra
Cassandra Cluster
Spark Worker
Spark Worker
Spark Worker
Spark Worker
Spark worker nodes
will get the data on
local node so it will
avoid latency

Spark, Mesos, Cassandra
Mesos Slaves and cassandra nodes are collocated to enforce the better data
locality for spark.
Driver
Program
Mesos
Master
Mesos slave
Cassandra node
Mesos slave
Cassandra node
Mesos slave
Cassandra node

Demo Application Architecture
Tweets
Store tweets in
kafka topic
Retrieve
hashtags
Evaluate Top
hashtag in
every 10
seconds
Store tweets in
cassandra table

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1. Kick-Start with SMACK Stack Sandeep Purohit Software Consultant Knoldus Software LLP

2. Agenda: ● What is SMACK? ● Why SMACK? ● Brief introduction of technologies ● How to Integrate all the technologies to create the data pipeline ● Demo

3. What is SMACK? ● Spark :Apache Spark is a fast and general-purpose cluster computing system. ● Mesos :Cluster resource management system that provide efficient resource allocation. ● Akka :Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM. ● Cassandra :The Apache Cassandra database is the right choice when you need scalability and high availability. ● Kafka :distributed messaging system for handling real time data.

4. Why SMACK? ● Smack is used for pipelined data architecture which is required for the real time data analysis. ● Smack is use to integrate all the technology at the right place to efficient data pipeline. ● Smack is use to linearly scale your whole cluster without any hassle

5. SMACK Pipeline Architecture

6. Why Spark? ● Its general purpose big data processing engine which have 4 main components spark core, spark streaming, spark ml, spark graphx ● So we can process our data which any of the component at real time. ● Its provide fault tolerant for real time application.

7. Why Cassandra? ● Cassandra implements “no single points of failure ● Cassandra Write-path is so fast so it can handle real-time data easily ● It will support Datacenter architecture so we can easily use different DC for different things. Ingestion DC Analysis DC Cassandra Cluster

8. Why Mesos? Mesos Master Mesos Master Standby Mesos Master Standby Zookepeer Mesos Slave Mesos Slave Mesos Slave

9. Models in SMACK ● In SMACK models are Scala and AKKA. ● We can use models to write highly concurrent and parallel applications. ● Example: We can use akka modules according to our use case like akka-http, akka-scheduler, akka priority mailboxes etc.

10. Models use in SMACK Akka-Http Akka-Scheduler

11. Why Kafka ● streams of data efficiently and in real time ● Use Kafka for fault tolerance. ● To create bridge between two applications. Streaming Source Kafka Broker Spark Receiver

12. Architecture of Spark and cassandra Cassandra Cluster Spark Worker Spark Worker Spark Worker Spark Worker Spark worker nodes will get the data on local node so it will avoid latency

13. Spark, Mesos, Cassandra Mesos Slaves and cassandra nodes are collocated to enforce the better data locality for spark. Driver Program Mesos Master Mesos slave Cassandra node Mesos slave Cassandra node Mesos slave Cassandra node

14. Demo Application Architecture Tweets Store tweets in kafka topic Retrieve hashtags Evaluate Top hashtag in every 10 seconds Store tweets in cassandra table

15. Demo SMACK_Tweets

16. Thank You!!

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