Big Data

Preface
Acknowledgments
About this book

1. A new paradigm for Big Data
1.1 How this book is structured
1.2 1.2Scaling with a traditional database
1.3 NoSQL is not a panacea
1.4 First principles
1.5 Desired properties of a Big Data system
1.6 The problems with fully incremental architectures
1.7 Lambda Architecture
1.8 Recent trends in technology
1.9 Example application: SuperWebAnalytics.com
1.10 Summary

Part 1: Batch layer
2. Data model for Big Data
2.1 The properties of data
2.2 The fact-based model for representing data
2.3 Graph schemas
2.4 A complete data model for SuperWebAnalytics.com
2.5 Summary

3. Data model for Big Data: Illustration
3.1 Why a serialization framework?
3.2 Apache Thrift
3.3 Limitations of serialization frameworks
3.4 Summary

4. Data storage on the batch layer
4.1 Storage requirements for the master dataset
4.2 Choosing a storage solution for the batch layer
4.3 How distributed filesystems work
4.4 Storing a master dataset with a distributed filesystem
4.5 Vertical partitioning
4.6 Low-level nature of distributed filesystems
4.7 Storing the SuperWebAnalytics.com master dataset on a distributed filesystem
4.8 Summary

5. Data storage on the batch layer: Illustration
5.1 Using the Hadoop Distributed File System
5.2 Data storage in the batch layer with Pail
5.3 Storing the master dataset for SuperWebAnalytics.com
5.4 Summary

6. Batch layer
6.1 Motivating examples
6.2 Computing on the batch layer
6.3 Recomputation algorithms vs. incremental algorithms
6.4 Scalability in the batch layer
6.5 MapReduce: a paradigm for Big Data computing
6.6 Low-level nature of MapReduce
6.7 Pipe diagrams: a higher-level way of thinking about batch computation
6.8 Summary

7. Batch layer: Illustration
7.1 An illustrative example
7.2 Common pitfalls of data-processing tools
7.3 An introduction to JCascalog
7.4 Composition
7.5 Summary

8. An example batch layer: Architecture and algorithms
8.1 Design of the SuperWebAnalytics.com batch layer
8.2 Workflow overview
8.3 Ingesting new data
8.4 URL normalization
8.5 User-identifier normalization
8.6 Deduplicate pageviews
8.7 Computing batch views
8.8 Summary

9. An example batch layer: Implementation
9.1 Starting point
9.2 Preparing the workflow
9.3 Ingesting new data
9.4 URL normalization
9.5 User-identifier normalization
9.6 Deduplicate pageviews
9.7 Computing batch views
9.8 Summary

Part 2: Serving layer
10. Serving layer
10.1 Performance metrics for the serving layer
10.2 The serving layer solution to the normalization/denormalization problem
10.3 Requirements for a serving layer database
10.4 Designing a serving layer for SuperWebAnalytics.com
10.5 Contrasting with a fully incremental solution
10.6 Summary

11. Serving layer: Illustration
11.1 Basics of ElephantDB
11.2 Building the serving layer for SuperWebAnalytics.com
11.3 Summary

Part 3: Speed layer
12. Realtime views
12.1 Computing realtime views
12.2 Storing realtime views
12.3 Challenges of incremental computation
12.4 Asynchronous versus synchronous updates
12.5 Expiring realtime views
12.6 Summary

13. Realtime views: Illustration
13.1 Cassandra’s data model
13.2 Using Cassandra
13.3 Summary

14. Queuing and stream processing
14.1 Queuing
14.2 Stream processing
14.3 Higher-level, one-at-a-time stream processing
14.4 SuperWebAnalytics.com speed layer
14.5 Summary

15. Queuing and stream processing: Illustration
15.1 Defining topologies with Apache Storm
15.2 Apache Storm clusters and deployment
15.3 Guaranteeing message processing
15.4 Implementing the SuperWebAnalytics.com uniques-over-time speed layer
15.5 Summary

16. Micro-batch stream processing
16.1 Achieving exactly-once semantics
16.2 Core concepts of micro-batch stream processing
16.3 Extending pipe diagrams for micro-batch processing
16.4 Finishing the speed layer for SuperWebAnalytics.com
16.5 Pageviews over time 262 n Bounce-rate analysis
16.6 Another look at the bounce-rate-analysis example
16.7 Summary

17. Micro-batch stream processing: Illustration
17.1 Using Trident
17.2 Finishing the SuperWebAnalytics.com speed layer
17.3 Fully fault-tolerant, in-memory, micro-batch processing
17.4 Summary

18. Lambda Architecture in depth
18.1 Defining data systems
18.2 Batch and serving layers
18.3 Speed layer
18.4 Query layer
18.5 Summary


Index