Book description
The need for fast data applications is growing rapidly, driven by the IoT, the surge in machine-to-machine (M2M) data, global mobile device proliferation, and the monetization of SaaS platforms. So how do you combine real-time, streaming analytics with real-time decisions in an architecture that’s reliable, scalable, and simple?
In this O’Reilly report, Ryan Betts and John Hugg from VoltDB examine ways to develop apps for fast data, using pre-defined patterns. These patterns are general enough to suit both the do-it-yourself, hybrid batch/streaming approach, as well as the simpler, proven in-memory approach available with certain fast database offerings.
Their goal is to create a collection of fast data app development recipes. We welcome your contributions, which will be tested and included in future editions of this report.
Publisher resources
Table of contents
- Foreword
- Fast Data Application Value
- Fast Data and the Enterprise
- 1. What Is Fast Data?
- 2. Disambiguating ACID and CAP
- 3. Recipe: Integrate Streaming Aggregations and Transactions
- 4. Recipe: Design Data Pipelines
- 5. Recipe: Pick Failure-Recovery Strategies
- 6. Recipe: Combine At-Least-Once Delivery with Idempotent Processing to Achieve Exactly-Once Semantics
- Glossary
Product information
- Title: Fast Data: Smart and at Scale
- Author(s):
- Release date: October 2015
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491940372
You might also like
book
Expert Hadoop® Administration
The Comprehensive, Up-to-Date Apache Hadoop Administration Handbook and Reference “Sam Alapati has worked with production Hadoop …
audiobook
Transformed
Help transform your business and innovate like the world's top tech companies! Transformed: Moving to the …
book
The Evolving Role of the Data Engineer
Companies working to become data driven often view data scientists as heroes, but that overlooks the …
book
Sams Teach Yourself Apache Spark™ in 24 Hours
Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data …