最新章節(jié)

• Summary
• Deploying the Zeppelin notebooks
• Deploying the Apache Spark workers
• Deploying the Apache Spark master
• Prerequisites
• Example--Apache Spark on Kubernetes

• Summary 更新時(shí)間：2021-07-02 18:56:09
• Deploying the Zeppelin notebooks
• Deploying the Apache Spark workers
• Deploying the Apache Spark master
• Prerequisites
• Example--Apache Spark on Kubernetes
• Using Kubernetes for provisioning containerized Spark applications
• Understanding Kubernetes
• Understanding the core concepts of Docker
• Linux containers
• Control groups
• Namespaces
• Containerization
• Bare metal virtual machines and containers
• Apache Spark on Kubernetes
• Summary
• Real data science work with SparkR
• Interactive exploratory analysis using Python and Pixiedust
• ETL with Scala
• The IEEE PHM 2012 data challenge bearing dataset
• Learning by example
• Why notebooks are the new standard
• Apache Spark with Jupyter Notebooks on IBM DataScience Experience
• Summary
• Example 5 – connected components
• Example 4 – triangle counting
• Example 3 – page rank
• Example 2 – filtering
• Example 1 – counting
• Examples
• Join reordering
• Join elimination
• Materialized views
• Graph-relational translation
• Architecture
• Apache Spark GraphFrames
• Summary
• Example 5 – connected components
• Example 4 – triangle counting
• Example 3 – PageRank
• Example 2 – filtering
• Example 1 – counting
• Creating a graph
• The raw data
• Graph analytics/processing with GraphX
• Overview
• Apache Spark GraphX
• Summary
• Run the examples in Apache Spark
• Running the examples in Eclipse
• Install the Deeplearning4j example within Eclipse
• Testing the test data generator
• Deploying the test data generator flow
• Deploy the Node-RED IoT Starter Boilerplate to the IBM Cloud
• Deploying the test data generator
• Mastering chaos: the Lorenz attractor model
• Example: an IoT real-time anomaly detector
• Deeplearning4j
• ND4J - high performance linear algebra for the JVM
• Deeplearning4j
• H2O Flow
• The example code – MNIST
• Example code – income
• Deep Learning
• Performance tuning
• Data quality
• Sourcing the data
• Architecture
• The build environment
• Overview
• H2O
• Deep Learning on Apache Spark with DeepLearning4j and H2O
• Summary
• Apache SystemML in action
• Performance measurements
• How low-level operators are optimized on
• High-level operators are generated
• Language parsing
• ApacheSystemML architecture
• An example - alternating least squares
• A cost-based optimizer for machine learning algorithms
• The history of Apache SystemML
• Why on Apache Spark?
• Why do we need just another library?
• Apache SystemML
• Summary
• Using the evaluator to assess the quality of the cross-validated and tuned model
• CrossValidation and hyperparameter tuning
• Model evaluation
• Training the machine learning model
• Testing the feature engineering pipeline
• Feature engineering
• Data preparation
• Winning a Kaggle competition with Apache SparkML
• Hyperparameter tuning
• CrossValidation
• CrossValidation and hyperparameter tuning
• Model evaluation
• RandomForestClassifier
• Estimators
• Pipelines
• VectorAssembler
• OneHotEncoder
• String indexer
• Transformers
• The concept of pipelines
• What does the new API look like?
• Apache SparkML
• Summary
• ANN in practice
• Artificial neural networks
• K-Means in practice
• Theory on Clustering
• Clustering with K-Means
• Naive Bayes in practice
• Theory on Classification
• Classification with Naive Bayes
• The development environment
• Architecture
• Apache Spark MLlib
• Summary
• More on stream life cycle management
• Controlling continuous applications
• Example - connection to a MQTT message broker
• State versioning guarantees consistent results after reruns
• Idempotent sinks prevent data duplication
• Replayable sources can replay streams from a given offset
• How transparent fault tolerance and exactly-once delivery guarantee is achieved
• Increased performance with good old friends
• How Apache Spark improves windowing
• How streaming engines use windowing
• Windowing
• True unification - same code same engine
• The concept of continuous applications
• Structured Streaming
• Summary
• Kafka
• Flume
• File streams
• TCP stream
• Streaming sources
• Checkpointing
• Errors and recovery
• Overview
• Apache Spark Streaming
• Summary
• Operator fusing versus the volcano iterator model
• A practical example on whole stage code generation performance
• Understanding whole stage code generation
• Understanding columnar storage
• Code generation
• Cache eviction strategies and pre-fetching
• Cache-friendly layout of data in memory
• A practical example on memory usage and performance
• Understanding the BytesToBytesMap
• The variable length values region
• The fixed length values region
• The null bit set region
• Understanding the UnsafeRow object
• Memory management beyond the Java Virtual Machine Garbage Collector
• Project Tungsten
• Summary
• How smart data sources work internally
• Using the explain method to obtain the PEP
• Practical examples
• Code generation
• Physical Execution Plan generation and selection
• How to optimize the Resolved Logical Execution Plan
• Internal class and object representations of LEPs
• How to go from Unresolved Logical Execution Plan to Resolved Logical Execution Plan
• The SQL abstract syntax tree
• Managing temporary views with the catalog API
• Understanding the workings of the Catalyst Optimizer
• The Catalyst Optimizer
• Summary
• RDDs versus DataFrames versus Datasets
• User-defined functions
• The Dataset API in action
• Using Datasets
• Applying SQL table joins
• Using SQL subqueries
• Defining schemas manually
• Using SQL
• DataFrames
• Predicate push-down on smart data sources
• Implicit schema discovery
• Understanding the DataSource API
• Processing the Parquet files
• Processing JSON files
• Processing the text files
• Importing and saving data
• The SparkSession--your gateway to structured data processing
• Apache Spark SQL
• Summary
• Cloud
• Coding
• Memory
• Data locality
• Hadoop Distributed File System
• The cluster structure
• Performance
• Cloud-based deployments
• Apache Mesos
• Apache YARN
• Standalone
• Local
• Cluster management
• Cluster design
• What's new in Apache Spark V2?
• Extended ecosystem
• Spark graph processing
• Spark SQL
• Spark Streaming
• Spark machine learning
• A First Taste and What’s New in Apache Spark V2
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• Title Page
• coverpage

• coverpage
• Title Page
• Credits
• About the Author
• About the Reviewer
• 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
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• A First Taste and What’s New in Apache Spark V2
• Spark machine learning
• Spark Streaming
• Spark SQL
• Spark graph processing
• Extended ecosystem
• What's new in Apache Spark V2?
• Cluster design
• Cluster management
• Local
• Standalone
• Apache YARN
• Apache Mesos
• Cloud-based deployments
• Performance
• The cluster structure
• Hadoop Distributed File System
• Data locality
• Memory
• Coding
• Cloud
• Summary
• Apache Spark SQL
• The SparkSession--your gateway to structured data processing
• Importing and saving data
• Processing the text files
• Processing JSON files
• Processing the Parquet files
• Understanding the DataSource API
• Implicit schema discovery
• Predicate push-down on smart data sources
• DataFrames
• Using SQL
• Defining schemas manually
• Using SQL subqueries
• Applying SQL table joins
• Using Datasets
• The Dataset API in action
• User-defined functions
• RDDs versus DataFrames versus Datasets
• Summary
• The Catalyst Optimizer
• Understanding the workings of the Catalyst Optimizer
• Managing temporary views with the catalog API
• The SQL abstract syntax tree
• How to go from Unresolved Logical Execution Plan to Resolved Logical Execution Plan
• Internal class and object representations of LEPs
• How to optimize the Resolved Logical Execution Plan
• Physical Execution Plan generation and selection
• Code generation
• Practical examples
• Using the explain method to obtain the PEP
• How smart data sources work internally
• Summary
• Project Tungsten
• Memory management beyond the Java Virtual Machine Garbage Collector
• Understanding the UnsafeRow object
• The null bit set region
• The fixed length values region
• The variable length values region
• Understanding the BytesToBytesMap
• A practical example on memory usage and performance
• Cache-friendly layout of data in memory
• Cache eviction strategies and pre-fetching
• Code generation
• Understanding columnar storage
• Understanding whole stage code generation
• A practical example on whole stage code generation performance
• Operator fusing versus the volcano iterator model
• Summary
• Apache Spark Streaming
• Overview
• Errors and recovery
• Checkpointing
• Streaming sources
• TCP stream
• File streams
• Flume
• Kafka
• Summary
• Structured Streaming
• The concept of continuous applications
• True unification - same code same engine
• Windowing
• How streaming engines use windowing
• How Apache Spark improves windowing
• Increased performance with good old friends
• How transparent fault tolerance and exactly-once delivery guarantee is achieved
• Replayable sources can replay streams from a given offset
• Idempotent sinks prevent data duplication
• State versioning guarantees consistent results after reruns
• Example - connection to a MQTT message broker
• Controlling continuous applications
• More on stream life cycle management
• Summary
• Apache Spark MLlib
• Architecture
• The development environment
• Classification with Naive Bayes
• Theory on Classification
• Naive Bayes in practice
• Clustering with K-Means
• Theory on Clustering
• K-Means in practice
• Artificial neural networks
• ANN in practice
• Summary
• Apache SparkML
• What does the new API look like?
• The concept of pipelines
• Transformers
• String indexer
• OneHotEncoder
• VectorAssembler
• Pipelines
• Estimators
• RandomForestClassifier
• Model evaluation
• CrossValidation and hyperparameter tuning
• CrossValidation
• Hyperparameter tuning
• Winning a Kaggle competition with Apache SparkML
• Data preparation
• Feature engineering
• Testing the feature engineering pipeline
• Training the machine learning model
• Model evaluation
• CrossValidation and hyperparameter tuning
• Using the evaluator to assess the quality of the cross-validated and tuned model
• Summary
• Apache SystemML
• Why do we need just another library?
• Why on Apache Spark?
• The history of Apache SystemML
• A cost-based optimizer for machine learning algorithms
• An example - alternating least squares
• ApacheSystemML architecture
• Language parsing
• High-level operators are generated
• How low-level operators are optimized on
• Performance measurements
• Apache SystemML in action
• Summary
• Deep Learning on Apache Spark with DeepLearning4j and H2O
• H2O
• Overview
• The build environment
• Architecture
• Sourcing the data
• Data quality
• Performance tuning
• Deep Learning
• Example code – income
• The example code – MNIST
• H2O Flow
• Deeplearning4j
• ND4J - high performance linear algebra for the JVM
• Deeplearning4j
• Example: an IoT real-time anomaly detector
• Mastering chaos: the Lorenz attractor model
• Deploying the test data generator
• Deploy the Node-RED IoT Starter Boilerplate to the IBM Cloud
• Deploying the test data generator flow
• Testing the test data generator
• Install the Deeplearning4j example within Eclipse
• Running the examples in Eclipse
• Run the examples in Apache Spark
• Summary
• Apache Spark GraphX
• Overview
• Graph analytics/processing with GraphX
• The raw data
• Creating a graph
• Example 1 – counting
• Example 2 – filtering
• Example 3 – PageRank
• Example 4 – triangle counting
• Example 5 – connected components
• Summary
• Apache Spark GraphFrames
• Architecture
• Graph-relational translation
• Materialized views
• Join elimination
• Join reordering
• Examples
• Example 1 – counting
• Example 2 – filtering
• Example 3 – page rank
• Example 4 – triangle counting
• Example 5 – connected components
• Summary
• Apache Spark with Jupyter Notebooks on IBM DataScience Experience
• Why notebooks are the new standard
• Learning by example
• The IEEE PHM 2012 data challenge bearing dataset
• ETL with Scala
• Interactive exploratory analysis using Python and Pixiedust
• Real data science work with SparkR
• Summary
• Apache Spark on Kubernetes
• Bare metal virtual machines and containers
• Containerization
• Namespaces
• Control groups
• Linux containers
• Understanding the core concepts of Docker
• Understanding Kubernetes
• Using Kubernetes for provisioning containerized Spark applications
• Example--Apache Spark on Kubernetes
• Prerequisites
• Deploying the Apache Spark master
• Deploying the Apache Spark workers
• Deploying the Zeppelin notebooks
• Summary 更新時(shí)間：2021-07-02 18:56:09