Ashish Tadose's talk at first IMC-Pune meetup.

1. Every ad.
Every sales channel.
Every screen.
One platform.
Building Distributed Data
Streaming System
AshishTadose
Lead Software Engineer
Big Data Analytics - PubMatic

2. Agenda
• What is stream processing
• Streaming architecture
• Scalable Data Ingestion
• RealTime Streaming Processing system
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3. What is Streaming Process ?
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4. In simple words, Streaming is…
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5. Batch & Streaming processing
Data
Generator
Ingestion
Distributed
File system
Processing Data Store
Batch processing
Data
Generator
Ingestion
Message
Queue
Processing Data Store
Stream Data processing

6. Batch & Streaming processing
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Data
Generator
Ingestion
Message
Queue
Processing Data Store
Stream Data processing
Distributed
File system
Processing Data Store
Batch processing

7. Batch & Streaming processing
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Data
Generator
Ingestion
Message
Queue
Processing Data Store
Stream Data processing
Distributed
File system
Processing Data Store
Batch processing

8. Lambda Architecture:Velocity &Volume
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9. Streaming
Ingestion
Technologies
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10. Ingestion Ecosystem
• Sources
• Machine data
• External stream & syslogs
• Data Collection
• Flume
• Kafka
• Kinesis
• Confluent
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11. Flume
• Easier to setup
• Rich set of in-build tools
• No inherent support for data replication
• Nodes works in isolation
• Memory channel vs File Channel 11

12. Kinesis
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13. Kafka
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 http://kafka.apache.org/
 Originated at LinkedIn, open sourced in early 2011
 Implemented in Scala, some Java
 9 core committers, plus ~ 20 contributors

14. Why is Kafka so fast?
• Fast writes:
• While Kafka persists all data to disk, essentially all writes go to the
page cache of OS, i.e. RAM.
• Fast reads:
• Very efficient to transfer data from page cache to a network socket
• Linux: sendfile() system call
• Combination of the two = fast Kafka!
• Example (Operations):On a Kafka cluster where the consumers are mostly
caught up you will see no read activity on the disks as they will be serving
data entirely from cache.
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http://kafka.apache.org/documentation.html#persistence

15. Flafka – Flume meets Kafka
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16. Confluent - Centralized Ingestion with Kafka Pipeline
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17. Stream
Processing
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18. RealTime Stream Processing
• Processing system
• Apache Storm
• Apache Samza
• Apache Spark (Streaming)
• Project Apex - DataTorrent
• Storage
• Hive HDFS
• Hbase
• MySql
• Custom
• Access
• Depend of data storage
• Scalable query interface - Kafka 18

19. Streaming Design Patterns
• Micro batching
• Unpredictable incoming data
• Creating multiple streams
• Out of sequence events
• Stream joins
• Top N metrics
• External Lookup
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20. ThankYou
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