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- (Reference Guide) Scala and Spark for Big Data Analytics
Book Description
Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you.
The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment.
You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio.
By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
What You Will Learn
- Understand object-oriented & functional programming concepts of Scala
- In-depth understanding of Scala collection APIs
- Work with RDD and DataFrame to learn Spark’s core abstractions
- Analysing structured and unstructured data using SparkSQL and GraphX
- Scalable and fault-tolerant streaming application development using Spark structured streaming
- Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML
- Build clustering models to cluster a vast amount of data
- Understand tuning, debugging, and monitoring Spark applications
- Deploy Spark applications on real clusters in Standalone, Mesos, and YARN
Table of Contents
1: Introduction to Scala
- History and purposes of Scala
- Platforms and editors
- Installing and setting up Scala
- Scala: the scalable language
- Scala for Java programmers
- Scala for the beginners
- Summary
2: Object-Oriented Scala
- Variables in Scala
- Methods, classes, and objects in Scala
- Packages and package objects
- Java interoperability
- Pattern matching
- Implicit in Scala
- Generic in Scala
- SBT and other build systems
- Summary
3: Functional Programming Concepts
- Introduction to functional programming
- Functional Scala for the data scientists
- Why FP and Scala for learning Spark?
- Pure functions and higher-order functions
- Using higher-order functions
- Error handling in functional Scala
- Functional programming and data mutability
- Summary
4: Collection APIs
- Scala collection APIs
- Types and hierarchies
- Performance characteristics
- Java interoperability
- Using Scala implicits
- Summary
5: Tackle Big Data – Spark Comes to the Party
- Introduction to data analytics
- Introduction to big data
- Distributed computing using Apache Hadoop
- Here comes Apache Spark
- Summary
6: Start Working with Spark – REPL and RDDs
- Dig deeper into Apache Spark
- Apache Spark installation
- Introduction to RDDs
- Using the Spark shell
- Actions and Transformations
- Caching
- Loading and saving data
- Summary
7: Special RDD Operations
- Types of RDDs
- Aggregations
- Partitioning and shuffling
- Broadcast variables
- Accumulators
- Summary
8: Introduce a Little Structure - Spark SQL
- Spark SQL and DataFrames
- DataFrame API and SQL API
- Aggregations
- Joins
- Summary
9: Stream Me Up, Scotty - Spark Streaming
- A Brief introduction to streaming
- Spark Streaming
- Discretized streams
- Stateful/stateless transformations
- Checkpointing
- Interoperability with streaming platforms (Apache Kafka)
- Structured streaming
- Summary
10: Everything is Connected - GraphX
- A brief introduction to graph theory
- GraphX
- VertexRDD and EdgeRDD
- Graph operators
- Pregel API
- PageRank
- Summary
11: Learning Machine Learning - Spark MLlib and Spark ML
- Introduction to machine learning
- Spark machine learning APIs
- Feature extraction and transformation
- Creating a simple pipeline
- Unsupervised machine learning
- Binary and multiclass classification
- Summary
12: My Name is Bayes, Naive Bayes
- Multinomial classification
- Bayesian inference
- Naive Bayes
- The decision trees
- Summary
13: Time to Put Some Order - Cluster Your Data with Spark MLlib
- Unsupervised learning
- Clustering techniques
- Centroid-based clustering (CC)
- Hierarchical clustering (HC)
- Distribution-based clustering (DC)
- Determining number of clusters
- A comparative analysis between clustering algorithms
- Submitting Spark job for cluster analysis
- Summary
14: Text Analytics Using Spark ML
- Understanding text analytics
- Transformers and Estimators
- Tokenization
- StopWordsRemover
- NGrams
- TF-IDF
- Word2Vec
- CountVectorizer
- Topic modeling using LDA
- Implementing text classification
- Summary
15: Spark Tuning
- Monitoring Spark jobs
- Spark configuration
- Common mistakes in Spark app development
- Optimization techniques
- Summary
16: Time to Go to ClusterLand - Deploying Spark on a Cluster
- Spark architecture in a cluster
- Deploying the Spark application on a cluster
- Summary
17: Testing and Debugging Spark
- Testing in a distributed environment
- Testing Spark applications
- Debugging Spark applications
- Summary
18: PySpark and SparkR
- Introduction to PySpark
- Installation and configuration
- Introduction to SparkR
- Summary
SKU | 031014S |
---|---|
Weight | 4.0840 |
Coming Soon | No |
Days of Training | No |
Audience | Student |
Product Family | Partnerware |
Product Type | Print Courseware |
Electronic | No |
ISBN | 9781785280849 |
Language | English |
Page Count | 778 |
Curriculum Library | No |
Year | No |
Manufacturer's Product Code | No |
Current Revision | 1.0 |
---|---|
Revision Notes | No Revision Information Available |
Original Publication Date | 2018-06-05 00:00:00 |
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(Reference Guide) Scala and Spark for Big Data Analytics eBook
(031014SE) Student Digital Courseware$51.99