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Practical Real-time Data Processing and Analytics
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Summary
IfyouareaJavadeveloperwhowouldliketobeequippedwithallthetoolsrequiredtodeviseanend-to-endpracticalsolutiononreal-timedatastreaming,thenthisbookisforyou.Basicknowledgeofreal-timeprocessingwouldbehelpful,andknowingthefundamentalsofMaven,Shell,andEclipsewouldbegreat.
目錄(229章)
倒序
- coverpage
- Title Page
- Copyright
- Practical Real-Time Data Processing and Analytics
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Why subscribe?
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Errata
- Piracy
- Questions
- Introducing Real-Time Analytics
- What is big data?
- Big data infrastructure
- Real–time analytics – the myth and the reality
- Near real–time solution – an architecture that works
- NRT – The Storm solution
- NRT – The Spark solution
- Lambda architecture – analytics possibilities
- IOT – thoughts and possibilities
- Edge analytics
- Cloud – considerations for NRT and IOT
- Summary
- Real Time Applications – The Basic Ingredients
- The NRT system and its building blocks
- Data collection
- Stream processing
- Analytical layer – serve it to the end user
- NRT – high-level system view
- NRT – technology view
- Event producer
- Collection
- Broker
- Transformation and processing
- Storage
- Summary
- Understanding and Tailing Data Streams
- Understanding data streams
- Setting up infrastructure for data ingestion
- Apache Kafka
- Apache NiFi
- Logstash
- Fluentd
- Flume
- Taping data from source to the processor - expectations and caveats
- Comparing and choosing what works best for your use case
- Do it yourself
- Setting up Elasticsearch
- Summary
- Setting up the Infrastructure for Storm
- Overview of Storm
- Storm architecture and its components
- Characteristics
- Components
- Stream grouping
- Setting up and configuring Storm
- Setting up Zookeeper
- Installing
- Configuring
- Standalone
- Cluster
- Running
- Setting up Apache Storm
- Installing
- Configuring
- Running
- Real-time processing job on Storm
- Running job
- Local
- Cluster
- Summary
- Configuring Apache Spark and Flink
- Setting up and a quick execution of Spark
- Building from source
- Downloading Spark
- Running an example
- Setting up and a quick execution of Flink
- Build Flink source
- Download Flink
- Running example
- Setting up and a quick execution of Apache Beam
- Beam model
- Running example
- MinimalWordCount example walk through
- Balancing in Apache Beam
- Summary
- Integrating Storm with a Data Source
- RabbitMQ – messaging that works
- RabbitMQ exchanges
- Direct exchanges
- Fanout exchanges
- Topic exchanges
- Headers exchanges
- RabbitMQ setup
- RabbitMQ — publish and subscribe
- RabbitMQ – integration with Storm
- AMQPSpout
- PubNub data stream publisher
- String together Storm-RMQ-PubNub sensor data topology
- Summary
- From Storm to Sink
- Setting up and configuring Cassandra
- Setting up Cassandra
- Configuring Cassandra
- Storm and Cassandra topology
- Storm and IMDB integration for dimensional data
- Integrating the presentation layer with Storm
- Setting up Grafana with the Elasticsearch plugin
- Downloading Grafana
- Configuring Grafana
- Installing the Elasticsearch plugin in Grafana
- Running Grafana
- Adding the Elasticsearch datasource in Grafana
- Writing code
- Executing code
- Visualizing the output on Grafana
- Do It Yourself
- Summary
- Storm Trident
- State retention and the need for Trident
- Transactional spout
- Opaque transactional Spout
- Basic Storm Trident topology
- Trident internals
- Trident operations
- Functions
- map and flatMap
- peek
- Filters
- Windowing
- Tumbling window
- Sliding window
- Aggregation
- Aggregate
- Partition aggregate
- Persistence aggregate
- Combiner aggregator
- Reducer aggregator
- Aggregator
- Grouping
- Merge and joins
- DRPC
- Do It Yourself
- Summary
- Working with Spark
- Spark overview
- Spark framework and schedulers
- Distinct advantages of Spark
- When to avoid using Spark
- Spark – use cases
- Spark architecture - working inside the engine
- Spark pragmatic concepts
- RDD – the name says it all
- Spark 2.x – advent of data frames and datasets
- Summary
- Working with Spark Operations
- Spark – packaging and API
- RDD pragmatic exploration
- Transformations
- Actions
- Shared variables – broadcast variables and accumulators
- Broadcast variables
- Accumulators
- Summary
- Spark Streaming
- Spark Streaming concepts
- Spark Streaming - introduction and architecture
- Packaging structure of Spark Streaming
- Spark Streaming APIs
- Spark Streaming operations
- Connecting Kafka to Spark Streaming
- Summary
- Working with Apache Flink
- Flink architecture and execution engine
- Flink basic components and processes
- Integration of source stream to Flink
- Integration with Apache Kafka
- Example
- Integration with RabbitMQ
- Running example
- Flink processing and computation
- DataStream API
- DataSet API
- Flink persistence
- Integration with Cassandra
- Running example
- FlinkCEP
- Pattern API
- Detecting pattern
- Selecting from patterns
- Example
- Gelly
- Gelly API
- Graph representation
- Graph creation
- Graph transformations
- DIY
- Summary
- Case Study
- Introduction
- Data modeling
- Tools and frameworks
- Setting up the infrastructure
- Implementing the case study
- Building the data simulator
- Hazelcast loader
- Building Storm topology
- Parser bolt
- Check distance and alert bolt
- Generate alert Bolt
- Elasticsearch Bolt
- Complete Topology
- Running the case study
- Load Hazelcast
- Generate Vehicle static value
- Deploy topology
- Start simulator
- Visualization using Kibana
- Summary 更新時間：2021-07-08 10:23:51
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