Hien Luu - Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library
Here you can read online Hien Luu - Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2018, publisher: Apress, genre: Computer. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Hien Luu: author's other books
Who wrote Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library? Find out the surname, the name of the author of the book and a list of all author's works by series.
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Font size:
Interval:
Bookmark:
Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484235782 . For more detailed information, please visit www.apress.com/source-code .
has extensive working experience in designing and building big data applications and scalable web-based applications. He is particularly passionate about the intersection between big data and machine learning. Hien enjoys working with open source software and has contributed to Apache Pig and Azkaban. Teaching is one of his passions, and he serves as an instructor at the UCSC Silicon Valley Extension school teaching Apache Spark. He has given presentations at various conferences such a QCon SF, QCon London, Seattle Data Day, Hadoop Summit, and JavaOne.
is a research fellow at the Centre for Mathematical Modeling at FLAME University in Pune, India. His current research interests focus on topological quantum computation, nonequilibrium and finite-temperature aspects of topological quantum field theories, and applications of quantum materials effects for reversible computing. He received an M.Sc. in physics from Carnegie Mellon University, with a background in theoretical analysis of materials for spintronics applications as well as Monte Carlo simulations for the renormalization group of finite-temperature spin lattice systems.
There is no better time to learn Spark than now. Spark has become one of the critical components in the big data stack because of its ease of use, speed, and flexibility. This scalable data processing system is being widely adopted across many industries by many small and big companies, including Facebook, Microsoft, Netflix, and LinkedIn. This chapter provides a high-level overview of Spark, including the core concepts, the architecture, and the various components inside the Apache Spark stack.
Spark is a general distributed data processing engine built for speed, ease of use, and flexibility. The combination of these three properties is what makes Spark so popular and widely adopted in the industry.
The Apache Spark website claims it can run a certain data processing job up to 100 times faster than Hadoop MapReduce . In fact, in 2014, Spark won the Daytona GraySort contest, which is an industry benchmark for sorting 100TB of data (one trillion records). The submission from Databricks claimed Spark was able to sort 100TB of data three times faster and using ten times fewer resources than the previous world record set by Hadoop MapReduce.
Since the inception of the Spark project, the ease of use has been one of the main focuses of the Spark creators. It offers more than 80 high-level, commonly needed data processing operators to make it easy for developers, data scientists, and data analysts to build all kinds of interesting data applications. In addition, these operators are available in multiple languages, namely, Scala, Java, Python, and R. Software engineers, data scientists, and data analysts can pick and choose their favorite language to solve large-scale data processing problems with Spark.
In terms of flexibility, Spark offers a single unified data processing stack that can be used to solve multiple types of data processing workloads, including batch processing, interactive queries, iterative processing needed by machine learning algorithms, and real-time streaming processing to extract actionable insights at near real-time. Before the existence of Spark, each of these types of workload required a different solution and technology. Now companies can just leverage Spark for most of their data processing needs. Using a single technology stack will help with dramatically reducing the operational cost and resources.
A big data ecosystem consists of many pieces of technology including a distributed storage engine called HDFS, a cluster management system to efficiently manage a cluster of machines, and different file formats to store a large amount of data efficiently in binary and columnar format. Spark integrates really well with the big data ecosystem. This is another reason why Spark adoption has been growing at a really fast pace.
Another really cool thing about Spark is it is open source; therefore, anyone can download the source code to examine the code, to figure out how a certain feature was implemented, or to extend its functionalities. In some cases, it can dramatically help with reducing the time to debug problems.
Font size:
Interval:
Bookmark:
Similar books «Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library»
Look at similar books to Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Discussion, reviews of the book Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.