I Heart Logs. Event Data, Stream Processing, and Data Integration - Helion

ebook

Autor: Jay Kreps
ISBN: 978-14-919-0933-1
stron: 60, Format: ebook
Data wydania: 2014-09-23
Księgarnia: Helion

Cena książki: 92,65 zł (poprzednio: 107,73 zł)
Oszczędzasz: 14% (-15,08 zł)

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Tagi: Bazy danych

Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention.

Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models.

Go ahead and take the plunge with logs; you’re going love them.

Learn how logs are used for programmatic access in databases and distributed systems
Discover solutions to the huge data integration problem when more data of more varieties meet more systems
Understand why logs are at the heart of real-time stream processing
Learn the role of a log in the internals of online data systems
Explore how Jay Kreps applies these ideas to his own work on data infrastructure systems at LinkedIn

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Spis treści

I Heart Logs. Event Data, Stream Processing, and Data Integration eBook -- spis treści

Preface
- Conventions Used in This Book
- Safari Books Online
- How to Contact Us
1. Introduction
- What Is a Log?
- Logs in Databases
- Logs in Distributed Systems
  - Variety of Log-Centric Designs
  - An Example
  - Logs and Consensus
- Changelog 101: Tables and Events Are Dual
- Whats Next
2. Data Integration
- Data Integration: Two Complications
  - Data is More Diverse
  - The Explosion of Specialized Data Systems
- Log-Structured Data Flow
- My Experience at LinkedIn
- Relationship to ETL and the Data Warehouse
- ETL and Organizational Scalability
- Where Should We Put the Data Transformations?
- Decoupling Systems
- Scaling a Log
3. Logs and Real-Time Stream Processing
- Data Flow Graphs
- Logs and Stream Processing
- Reprocessing Data: The Lambda Architecture and an Alternative
  - What is a Lambda Architecture and How Do I Become One?
  - Whats Good About This?
  - And the Bad...
  - An Alternative
- Stateful Real-Time Processing
- Log Compaction
4. Building Data Systems with Logs
- Unbundling?
- The Place of the Log in System Architecture
- Conclusion
A. References
- Academic Papers, Systems, Talks, and Blogs
- Enterprise Software
- Open Source