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Big Data: Principles and Best Practices of Scalable Real-Time Data Systems

	Big Data: Principles And Best Practices Of Scalable Real-Time Data Systems by Nathan Marz, James Warren
	Price : Rs 649.00
	Your Price : Rs 551.65

This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You`ll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you`ll learn specific technologies like Hadoop, Storm and NoSQL databases.

Part: Batch layer

Data model for Big Data
The properties of data
The fact-based model for representing data
Graph schemas
complete data model for SuperWebAnalytics.com
Summary

Data model for Big Data: Illustration
Why a serialization framework?
Apache Thrift
Limitations of serialization frameworks
Summary

Data storage on the batch layer
Storage requirements for the master dataset
Choosing a storage solution for the batch layer
How distributed filesystems work
Storing a master dataset with a distributed filesystem
Vertical partitioning
Low-level nature of distributed filesystems
Storing the SuperWebAnalytics.com master dataset on a distributed filesystem
Summary

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

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

Batch layer: Illustration
An illustrative example
Common pitfalls of data-processing tools
An introduction to JCascalog
Composition
Summary

An example batch layer: Architecture and algorithms
Design of the SuperWebAnalytics.com batch layer
Workflow overview
Ingesting new data
URL normalization
User-identifier normalization
Deduplicate pageviews
Computing batch views
Summary

An example batch layer: Implementation
Starting point
Preparing the workflow
Ingesting new data
URL normalization
User-identifier normalization
Deduplicate pageviews
Computing batch views
Summary

Part 2: Serving layer

Serving layer
Performance metrics for the serving layer
The serving layer solution to the normalization/denormalization problem
Requirements for a serving layer database
Designing a serving layer for SuperWebAnalytics.com
Contrasting with a fully incremental solution
Summary

Serving layer: Illustration
Basics of ElephantDB
Building the serving layer for SuperWebAnalytics.com
Summary

Part 3: Speed layer

Realtime views
Computing realtime views
Storing realtime views
Challenges of incremental computation
Asynchronous versus synchronous updates
Expiring realtime views
Summary

Realtime views: Illustration
Cassandra’s data model
Using Cassandra
Summary

Queuing and stream processing
Queuing
Stream processing
Higher-level, one-at-a-time stream processing
SuperWebAnalytics.com speed layer
Summary

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

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

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

Lambda Architecture in depth
Defining data systems
Batch and serving layers
Speed layer
Query layer
SummaryISBN - 9789351198062

Pages : 328