Stream processing frameworks for big data: the internals

سرفصل های دوره

A deep dive into the internals of Flink, Spark Streaming, Structured Streaming, and Kafka Streams

1 - Introduction

1 - Introduction

2 - Course overview

2 - General characteristics

3 - Overview

4 - Stream processing and distributed processing

5 - Frameworks Flink

6 - Frameworks Kafka Streams

7 - Frameworks Spark Streaming and Structured Streaming

8 - Ecosystem Connectors

9 - Ecosystem Batch Processing

10 - Ecosystem ML Libraries and Other Libraries

11 - Maturity

12 - Streaming models

3 - APIs

13 - Programming languages

14 - API levels

15 - Operators

16 - Operators Sliding and Tumbling Windows

17 - Operators Session and Count Windows

18 - Operators Joining

19 - Operators Lowlevel Operators

20 - Configuration

4 - Time

21 - Time characteristics l

22 - Time characteristics II

23 - Outoforder processing

24 - Triggers

5 - Performance Latency and throughput

25 - Latency Definition and influence of streaming model

26 - Latency influence of operation

27 - Latency predictability

28 - Throughput

29 - General advice

6 - Scalability elasticity and parallelization

30 - Scalability

31 - Elasticity

32 - Parallelization

7 - State management

33 - State

34 - State backends

35 - State features

8 - Fault tolerance

36 - Message delivery guarantees

37 - Checkpointing

38 - Checkpointing savepoints

39 - Writeaheadlogs

40 - Fault tolerance in Kafka Streams

41 - Master and worker failures

9 - Summary

42 - Summary

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تولید کننده: Udemy-Training