Big Data Analytics with Spark PDF

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On this page, we’ve provided the most up-to-date download link for Big Data Analytics with Spark PDF. Apache Spark is an open-source distributed processing system designed for large data tasks. It uses in-memory caching as well as optimized execution of queries to run rapid analytic queries against data in any amount.

Big Data Analytics with Spark PDF

Name Big Data Analytics with Spark
Pages 68
Size 1.6 MB
Language English

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Big Data Analytics with Spark

Fundamentally, Spark is an open-source framework similar to the manner that Hadoop is – that offers several inter-connected platforms, standards, and systems that are suitable for Big Data projects. Similar to Hadoop, Spark is open-source and is under the umbrella of Apache Software Foundation.

Speed. Built from the bottom up to improve performance, Spark can be 100 times quicker than Hadoop for large-scale data processing, thanks to the use of memory computation and other enhancements. Spark can also be fast for when data are stored on disks and holds the current record in the world for large-scale disk sorting. Spark is an all-purpose, distributed data processing engine that can be used for use in a vast array of scenarios.

Some of the tasks often connected to Spark are ETL as well as SQL batch jobs across huge data sets processing streams of data generated by sensors and IoT devices, financial systems, as well as machine learning.

Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications, and users is exploding.

Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real-time data streams and so on.

As a result, the adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib.

Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You’ll learn the basics of functional programming in Scala so that you can write Spark applications in it. What’s more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka, and so on.

So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost―possibly a big boost―to your career.

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