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17438593
Scala and Spark for Big Data Analytics: Explore the concepts of functional programming, data streaming, and machine learning
67% of respondents would recommend this to a friend
Item #:
17438593
XOF 36686
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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.
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What Stands Out
Functional Programming
Leverage Scala's functional programming paradigm to write cleaner, more maintainable code, enhancing productivity and making it easier to work with complex data transformations in big data analytics.
Data Streaming
Utilize Apache Spark's structured streaming capabilities to process real-time data efficiently, allowing businesses to react instantly to changing data sets, thereby gaining a competitive edge in analytics.
Machine Learning Integration
Combine Scala and Spark's powerful machine learning libraries to develop predictive models directly on big data, simplifying workflows and increasing accuracy for data-driven decision-making.
Product Details
- Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye!Key FeaturesLearn Scala's sophisticated type system that combines Functional Programming and object-oriented conceptsWork on a wide array of applications, from simple batch jobs to stream processing and machine learningExplore the most common as well as some complex use-cases to perform large-scale data analysis with SparkBook DescriptionScala 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 learnUnderstand object-oriented & functional programming concepts of ScalaIn-depth understanding of Scala collection APIsWork with RDD and DataFrame to learn Spark's core abstractionsAnalysing structured and unstructured data using SparkSQL and GraphXScalable and fault-tolerant streaming application development using Spark structured streamingLearn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & MLBuild clustering models to cluster a vast amount of dataUnderstand tuning, debugging, and monitoring Spark applicationsDeploy Spark applications on real clusters in Standalone, Mesos, and YARNTable of ContentsIntroduction to ScalaObject-Oriented ScalaFunctional Programming ConceptsCollection APIsTackle Big Data - Spark Comes to the PartyStart Working with Spark - REPL and RDDsSpecial RDD OperationsIntroduce a Little Structure - Spark SQLStream Me Up, Scotty - Spark StreamingEverything is Connected - GraphXLearning Machine Learning - Spark MLlib and Spark MLMy Name is Bayes, Naive BayesTime to Put Some Order - Cluster Your Data with Spark MLlibText Analytics Using Spark MLSpark TuningTime to Go to ClusterLand - Deploying Spark on a ClusterTesting and Debugging SparkPySpark and SparkR
| Publisher | Packt Publishing |
| Publication date | July 25, 2017 |
| Language | English |
| Print length | 786 pages |
| ISBN-10 | 1785280848 |
| ISBN-13 | 978-1785280849 |
| Item Weight | 2.92 pounds (1.32 kg) |
| Dimensions | 7.5 x 1.8 x 9.25 inches (19.1 x 4.6 x 23.5 cm) |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to integrate big data techniques with functional programming and machine learning.
-
Software Developers
Great for developers aiming to enhance their skills in Scala and Apache Spark for big data projects.
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Big Data Analysts
Perfect for analysts seeking to learn data streaming and analytics for real-time processing with Scala and Spark.
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Beginners
Not suitable for those with little to no programming experience, as it requires a foundational understanding of concepts.
Product Description
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Data Processing Editorial Review
Scala and Spark for Big Data Analytics provides a comprehensive exploration of functional programming, data streaming, and machine learning, perfect for anyone interested in using Scala with Spark. The book covers essential topics intricately, including numerous coding examples and challenges related to big data analytics. Users have noted that while the book is extensive with 786 pages, it serves as both a practical guide and a theoretical reference, making it suitable for both beginners and professionals transitioning from Java to Scala for big data processing. Its detailed description of various machine learning algorithms and advanced Spark topics is especially appreciated, providing a solid foundation for those seeking to enhance their big data skills.
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Pros
- In-depth explanations of complex concepts
- Numerous practical examples for clarity
- Covers a wide range of Spark operations
- Useful guide for transitioning from Java to Scala
- Includes real-life big data analytics problems
Cons
- Some code examples may require additional research
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Features & Benefits
- Learn Scala's sophisticated type system combining functional programming and object-oriented concepts
- Work on a wide array of applications, from simple batch jobs to stream processing and machine learning
- Explore common and complex use-cases for large-scale data analysis with Spark
- Develop Spark applications using SparkR and PySpark APIs
- Understand machine-learning best practices and build predictive models with widely used algorithms in Spark MLlib & ML
- Learn tuning, debugging, and monitoring Spark applications, and deploy them on real clusters in Standalone, Mesos, and YARN
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