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YouTube

Fast Data Architectures for Streaming Applications

GOTO Conferences via YouTube

Overview

Explore fast data architectures for streaming applications in this 45-minute conference talk from GOTO Chicago 2017. Dive into the evolution of Big Data from batch-oriented to stream-oriented processing, focusing on architecture patterns, tools, and techniques for real-time data analysis. Learn about trade-offs guiding design decisions, challenges in stream analytics, major streaming data tools, and integration strategies. Discover how to handle late-arriving data, perform machine learning model training, and address high-scale problems through partitioning. Compare streaming engines like Spark, Kafka, and Flink, and understand the relationship between microservices and fast data architectures. Gain insights into reactive programming, merging streams, and the differences between batch and stream processing in modern Big Data ecosystems.

Syllabus

Intro
Riley Report
Context
Hadoop Classic
Hadoop Architecture
Time vs Money
Streaming Data Architecture
Streaming Engines
Latency Requirements
RealTime
Latency
Windowing
Machine Learning
Batch Jobs
Volume
HighScale Problems
Partitioning Data
Integration
Sequels
Spark vs Kafka
Dynamic ML
Event Processing
Individual Event Processing
Google Stream Processing
Apache Beam
Stream Processing
Flink
akkaStreams
KafkaStreams
Spark
Lambda
Microservices and Fast Data
Microservices
Single Responsibility
Micro Services
Reactive Programming
Twitter
Big Data vs Microservices
Light Ben
Merging streams
Runner
Storm
Knife
Beam
Legacy Data
Batch vs Stream
Sharing Models

Taught by

GOTO Conferences

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