Cover -- Title Page -- Copyright -- Contents -- Chapter 1 Introduction to Streaming Data -- Sources of Streaming Data -- Operational Monitoring -- Web Analytics -- Online Advertising -- Social Media -- Mobile Data and the Internet of Things -- Why Streaming Data Is Different -- Always On, Always Flowing -- Loosely Structured -- High-Cardinality Storage -- Infrastructures and Algorithms -- Conclusion -- Part I Streaming Analytics Architecture -- Chapter 2 Designing Real-Time Streaming Architectures -- Real-Time Architecture Components -- Collection -- Data Flow -- Processing -- Storage -- Delivery -- Features of a Real-Time Architecture -- High Availability -- Low Latency -- Horizontal Scalability -- Languages for Real-Time Programming -- Java -- Scala and Clojure -- JavaScript -- The Go Language -- A Real-Time Architecture Checklist -- Collection -- Data Flow -- Processing -- Storage -- Delivery -- Conclusion -- Chapter 3 Service Configuration and Coordination -- Motivation for Configuration and Coordination Systems -- Maintaining Distributed State -- Unreliable Network Connections -- Clock Synchronization -- Consensus in an Unreliable World -- Apache ZooKeeper -- The znode -- Watches and Notifications -- Maintaining Consistency -- Creating a ZooKeeper Cluster -- ZooKeeper's Native Java Client -- The Curator Client -- Curator Recipes -- Conclusion -- Chapter 4 Data-Flow Management in Streaming Analysis -- Distributed Data Flows -- At Least Once Delivery -- The "n+1" Problem -- Apache Kafka: High-Throughput Distributed Messaging -- Design and Implementation -- Configuring a Kafka Environment -- Interacting with Kafka Brokers -- Apache Flume: Distributed Log Collection -- The Flume Agent -- Configuring the Agent -- The Flume Data Model -- Channel Selectors -- Flume Sources -- Flume Sinks -- Sink Processors -- Flume Channels.

Flume Interceptors -- Integrating Custom Flume Components -- Running Flume Agents -- Conclusion -- Chapter 5 Processing Streaming Data -- Distributed Streaming Data Processing -- Coordination -- Partitions and Merges -- Transactions -- Processing Data with Storm -- Components of a Storm Cluster -- Configuring a Storm Cluster -- Distributed Clusters -- Local Clusters -- Storm Topologies -- Implementing Bolts -- Implementing and Using Spouts -- Distributed Remote Procedure Calls -- Trident: The Storm DSL -- Processing Data with Samza -- Apache YARN -- Getting Started with YARN and Samza -- Integrating Samza into the Data Flow -- Samza Jobs -- Conclusion -- Chapter 6 Storing Streaming Data -- Consistent Hashing -- "NoSQL" Storage Systems -- Redis -- MongoDB -- Cassandra -- Other Storage Technologies -- Relational Databases -- Distributed In-Memory Data Grids -- Choosing a Technology -- Key-Value Stores -- Document Stores -- Distributed Hash Table Stores -- In-Memory Grids -- Relational Databases -- Warehousing -- Hadoop as ETL and Warehouse -- Lambda Architectures -- Conclusion -- Part II Analysis and Visualization -- Chapter 7 Delivering Streaming Metrics -- Streaming Web Applications -- Working with Node -- Managing a Node Project with NPM -- Developing Node Web Applications -- A Basic Streaming Dashboard -- Adding Streaming to Web Applications -- Visualizing Data -- HTML5 Canvas and Inline SVG -- Data-Driven Documents: D3.js -- High-Level Tools -- Mobile Streaming Applications -- Conclusion -- Chapter 8 Exact Aggregation and Delivery -- Timed Counting and Summation -- Counting in Bolts -- Counting with Trident -- Counting in Samza -- Multi-Resolution Time-Series Aggregation -- Quantization Framework -- Stochastic Optimization -- Delivering Time-Series Data -- Strip Charts with D3.js -- High-Speed Canvas Charts -- Horizon Charts.

Conclusion -- Chapter 9 Statistical Approximation of Streaming Data -- Numerical Libraries -- Probabilities and Distributions -- Expectation and Variance -- Statistical Distributions -- Discrete Distributions -- Continuous Distributions -- Joint Distributions -- Working with Distributions -- Inferring Parameters -- The Delta Method -- Distribution Inequalities -- Random Number Generation -- Generating Specific Distributions -- Sampling Procedures -- Sampling from a Fixed Population -- Sampling from a Streaming Population -- Biased Streaming Sampling -- Conclusion -- Chapter 10 Approximating Streaming Data with Sketching -- Registers and Hash Functions -- Registers -- Hash Functions -- Working with Sets -- The Bloom Filter -- The Algorithm -- Choosing a Filter Size -- Unions and Intersections -- Cardinality Estimation -- Interesting Variations -- Distinct Value Sketches -- The Min-Count Algorithm -- The HyperLogLog Algorithm -- The Count-Min Sketch -- Point Queries -- Count-Min Sketch Implementation -- Top-K and "Heavy Hitters" -- Range and Quantile Queries -- Other Applications -- Conclusion -- Chapter 11 Beyond Aggregation -- Models for Real-Time Data -- Simple Time-Series Models -- Linear Models -- Logistic Regression -- Neural Network Models -- Forecasting with Models -- Exponential Smoothing Methods -- Regression Methods -- Neural Network Methods -- Monitoring -- Outlier Detection -- Change Detection -- Real-Time Optimization -- Conclusion -- Index -- EULA.