[DevNexus-2018] Apache Kafka A Streaming Data Platform

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@ Apache Kafka A Streaming Data Platform

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@ @gamussa @confluentinc Who am I?

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@ @gamussa @confluentinc Solutions Architect Who am I?

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@ @gamussa @confluentinc Solutions Architect Developer Advocate Who am I?

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@ @gamussa @confluentinc Solutions Architect Developer Advocate @gamussa in internetz Who am I?

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@ @gamussa @confluentinc Solutions Architect Developer Advocate @gamussa in internetz Hey you, yes, you, go follow me in twitter © Who am I?

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc A company is build on

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@ @gamussa @confluentinc A company is build on DATA FLOWS but All we have is DATA STORES

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc Streaming Platform 1. Pub/Sub 2. Store 3. Process

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@ @gamussa @confluentinc Streaming Platform 1. Pub/Sub 2. Store 3. Process

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@ @gamussa @confluentinc Core abstraction

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@ @gamussa @confluentinc Core abstraction DB - table

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@ @gamussa @confluentinc Core abstraction DB - table Hadoop - file

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@ @gamussa @confluentinc Core abstraction DB - table Hadoop - file Messaging -?

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@ @gamussa @confluentinc LOGS

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@ @gamussa @confluentinc Producing to Kafka Time

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@ @gamussa @confluentinc Producing to Kafka Time C C C

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@ @gamussa @confluentinc Producing to Kafka - With Key Time A B C D hash(key) % numPartitions = N

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@ @gamussa @confluentinc Producing to Kafka - No Key Time Messages will be produced in a round robin fashion

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@ @gamussa @confluentinc Producing to Kafka - No Key Time Messages will be produced in a round robin fashion

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@ @gamussa @confluentinc Producing to Kafka - No Key Time Messages will be produced in a round robin fashion

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@ @gamussa @confluentinc Producing to Kafka - No Key Time Messages will be produced in a round robin fashion

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@ @gamussa @confluentinc Consuming From Kafka - Single Consumer C

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@ @gamussa @confluentinc Consuming From Kafka - Grouped Consumers C C C1 C C C2

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@ @gamussa @confluentinc Consuming From Kafka - Grouped Consumers C C C C

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@ @gamussa @confluentinc Consuming From Kafka - Grouped Consumers 0 1 2 3

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@ @gamussa @confluentinc Consuming From Kafka - Grouped Consumers 0 1 2 3

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@ @gamussa @confluentinc Consuming From Kafka - Grouped Consumers 0, 3 1 2 3

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@ @gamussa @confluentinc Producers Consumers

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc Kafka Connect does hard work so you don’t

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@ @gamussa @confluentinc Kafka Connect does hard work so you don’t 1. Scale out

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@ @gamussa @confluentinc Kafka Connect does hard work so you don’t 1. Scale out

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@ @gamussa @confluentinc Kafka Connect does hard work so you don’t 1. Scale out

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@ @gamussa @confluentinc Kafka Connect does hard work so you don’t 1. Scale out

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc Streaming Platform 1. Pub/Sub 2. Store 3. Process

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@ @gamussa @confluentinc Why Store?

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@ @gamussa @confluentinc Scalability of a filesystem

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@ @gamussa @confluentinc Scalability of a filesystem Throughput 100s mb/s

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@ @gamussa @confluentinc Scalability of a filesystem Throughput 100s mb/s TBs per server

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@ @gamussa @confluentinc Scalability of a filesystem Throughput 100s mb/s TBs per server Commodity Hardware

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@ @gamussa @confluentinc Scalability of a filesystem Throughput 100s mb/s TBs per server Commodity Hardware O(1) writes

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@ @gamussa @confluentinc Guarantees of a database

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@ @gamussa @confluentinc Guarantees of a database Persistence

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@ @gamussa @confluentinc Guarantees of a database Persistence Strict ordering

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@ @gamussa @confluentinc Distributed by Design

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@ @gamussa @confluentinc Replication Distributed by Design

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@ @gamussa @confluentinc Replication Fault Tolerance Distributed by Design

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@ @gamussa @confluentinc Replication Fault Tolerance Partitioning Distributed by Design

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@ @gamussa @confluentinc Replication Fault Tolerance Partitioning Scale Distributed by Design

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc Partition Leadership and Replication Broker 1 Topic1 partition1 Broker 2 Broker 3 Broker 4 Topic1 partition1 Topic1 partition1 Leader Follower Topic1 partition2 Topic1 partition2 Topic1 partition2 Topic1 partition3 Topic1 partition4 Topic1 partition3 Topic1 partition3 Topic1 partition4 Topic1 partition4

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@ @gamussa @confluentinc Partition Leadership and Replication - node failure Broker 1 Topic1 partition1 Broker 2 Broker 3 Broker 4 Topic1 partition1 Topic1 partition1 Leader Follower Topic1 partition2 Topic1 partition2 Topic1 partition2 Topic1 partition3 Topic1 partition4 Topic1 partition3 Topic1 partition3 Topic1 partition4 Topic1 partition4

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@ @gamussa @confluentinc Streaming Platform 1. Pub/Sub 2. Store 3. Process

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@ @gamussa @confluentinc What is Stream Processing? A machine for combining streams of events

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc https://www.confluent.io/download/

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@ @gamussa @confluentinc We are hiring! https://www.confluent.io/careers/

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@ @gamussa @confluentinc One more thing…

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc

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@ @gamussa @confluentinc A Major New Paradigm

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@ @gamussa @confluentinc Thanks! questions? @gamussa viktor@confluent.io We are hiring! https://www.confluent.io/careers/