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Distributed logging, messaging and data connector cluster on top of Kafka

• Publish and subcribe to streams of records, similar to message queue
• Store streams of records in a fault tolerant way
• Process streams of records
• APIs : Producer, Consumer, Streams, Connector
• Migrate data sources built on top of Kafka

• Cloud native kafka

• Java 8
• Maven / Gradle

• kafka
  • stream API
    • Changelog topic
    • event schema
  • aggregate
  • connect
    • transfer,sync data from DB -> ElasticSearch, HDFS,etc with Kafka
  • orchestration vs choreography
• Flink
• microservices
• large scale event processing
• Apache Airflow
• DB : RockDB

• Download Kafka source code : git clone https://github.com/<your github id>/kafka.git
• Build Kafka source code
• Run Zookeeper
• Testing Kafka setup Create a new topic named "test" with a single partition and only one replica:

bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test We can now see that topic if we run the list topic command:

bin/kafka-topics.sh --list --zookeeper localhost:2181 test Run the producer and then type a few messages into the console to send to the server.

bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test This is a message This is another message Kafka also has a command line consumer that will dump out messages to standard output.

bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning This is a message This is another message If you have each of the above commands running in a different terminal then you should now be able to type messages into the producer terminal and see them appear in the consumer terminal.

• Socket and streams
• Bandwidth problems
• Communication between distributed applications
  • TCP - UDP
  • Multicasting
• HPC cluster computing
  • Parallel system architecture
  • Massively parallel processor MPP
  • Symmetric multiprocesssor SMP
• Ethernet
• Wireless networks
• OSI model
• RPC
• Clusters - High performance computing cluster
• Shard service
• Cache shard
• Hot sharding system
• Multi threading
• P

• Scheduling
• Hardware bottleneck
• CPU performance
• Algorithm efficiency
• Kernel scheduling
• I/O and CPU cache

• Distributed and decentralized databases
• Replication
• Horizontal/vertical partition
• Hybrid set up
• Distributed DBMS architecture
• Distributed transaction
• Replicated data

• Atomic operations
• Guarded actions
• Nondeterminism
• Fairness
• Theorem 4.1
• Chandy-Lamport algorithm
• Lai-Yang algorithm
• All- to - All broadcasting
• Termination-detection algorithm
• Dijkstra-scholten algorithm
• Wave algorithm
• Graph algorithm
• Coordination algorithms

• 2 way SSL
• SECaaS

• Data replication
• Sequential update mechanism
• Process synchronization

• Functions and event processing
• Master election
• Batch computational pattern
• Work queue system

• Distributed application on AWS

• AppDynamics
• Distributed debugging

• Distributed discrete event simulation
• Sensor network
• Social and peer to peer network
• Security in distributed environment

• DB update -> Kafka connect with Debezium
• Distributed kafka connect
• Cloud native Kafka architecture
• Kafka with Schema registry