Free Ebook Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Exactly what regarding Advanced Analytics With Spark: Patterns For Learning From Data At Scale If that's relevant to your issue, it will not just offer those suggestions. It will certainly provide examples, simple as well as simple examples of what you should do in resolving your troubles. It will likewise turn up the outcome and kinds of guide that is read. Many people are falling in love in this book since its power to assist everybody improve.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Free Ebook Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Advanced Analytics With Spark: Patterns For Learning From Data At Scale. It is the time to improve as well as freshen your ability, understanding and also experience included some amusement for you after long time with monotone points. Operating in the workplace, going to examine, picking up from test and also even more activities may be completed and you should start new things. If you really feel so tired, why don't you attempt new thing? A quite simple thing? Checking out Advanced Analytics With Spark: Patterns For Learning From Data At Scale is what we offer to you will understand. And also the book with the title Advanced Analytics With Spark: Patterns For Learning From Data At Scale is the reference now.
Why should be this publication? It's all that you require now. And even you do not require the message of this book straight now, you can discover the advantage some day. Someday, you will certainly feel that you are really lucky to locate Advanced Analytics With Spark: Patterns For Learning From Data At Scale as one of your analysis materials. If you start to feel it, possibly, you can not remind all about this book and cannot locate where this book is. Hence, you can check out once more this publication in this internet site, a site with million catalogues of guides.
What relationship to the reading publication activity is from the book, you can see and recognize just how the regulation of this life. You will see exactly how the others will certainly gaze to others. And will see just how the literary works is developed for some entertaining significance. Advanced Analytics With Spark: Patterns For Learning From Data At Scale is one of the jobs by somebody that has such sensation. Based on some facts, it will ensure you to open your mind and also think with each other regarding this subject. This publication look will certainly help you to make much better idea of reasoning.
When you have actually read it a lot more pages, you will know increasingly more again. Moreover when you have actually checked out all completed. That's your time to always keep in mind as well as do what the lesson as well as experience of this publication used to you. By this problem, you have to know that every publication ahs various means to present the impression to any kind of viewers. Yet they will certainly be as well as should be. This is what the DDD constantly offers you lesson about it.
Product details
Paperback: 276 pages
Publisher: O'Reilly Media; 1 edition (April 20, 2015)
Language: English
ISBN-10: 1491912766
ISBN-13: 978-1491912768
Product Dimensions:
7 x 0.6 x 9.2 inches
Shipping Weight: 1 pounds
Average Customer Review:
4.3 out of 5 stars
33 customer reviews
Amazon Best Sellers Rank:
#500,214 in Books (See Top 100 in Books)
This book fills an important gap in large scale data science.Spark has emerged as the big data platform of choice for data scientists both from the ease of use as well as the performance / optimization point of view. In a few lines of Scala code, Spark allows you to write iterative algorithms that scale out very well. For a data scientist who wants to explore large scale data sets, Spark is a great starting point (this is incredible progress in the Spark community given the project is just about 4 years old). However, Spark itself is moving fast and maturing with time, and Spark and Scala as well as distributed algorithms are typically not in the arsenal of many data scientists today.What this book does is teach you how to think about data science problems at scale, in the context of Spark. By well chosen examples covering both supervised and unsupervised learning, the authors take you step by step from a practical problem definition (say how to recommend music given user's history of music listened to) to what features are relevant, what machine learning algorithm to use and how to tune parameters to optimize the solution and how you can use Spark to do all of this in an interactive / iterative manner. As a bonus, they also point you to well engineered data sets that you can use to follow along the discussion and learn by trying out the examples yourself.By embracing the feature engineering steps and data cleaning/ error handling and tuning /feedback steps, the authors manage to show how real world data science works and how you can do full stack data science using Spark and gain immensely from the interactive nature of the Spark REPL.Overall, I highly recommend this book, and though it is the first book on Data Science using Spark, it sets a high standard for subsequent efforts.
TL;DR If you are looking for a intro to data science, data analysis and machine learning at scale - this is the right book. Sure, there are others, maybe more popular books from O'Reilly considering these topics, but the authors of those are using R and Python and the books are not focused on the performance and scalability. For closer details regarding Spark you can also take a look at this introductory Spark book - Learning Spark.This book presents 9 case studies of data analysis applications in various domains. The topics are diverse and the authors always use real world datasets. Beside learning Spark and a data science you will also have the opportunity to gain insight about topics like taxi traffic in NYC, deforestation or neuroscience. Without any previous exposure or contact with machine learning readers might struggle to understand certain chapters, so I think it's good idea to actually try those examples yourself while reading and Google for further details about the used methods. Many of the chapters end only with basic models, which barely outperform the baselines, so if you want to, there is a lot of space for their improvement and further work.Spark itself provides it's users with APIs in three languages - Java, Scala and Python. This books successfully covers each one of these, although you can feel slight preference of a Scala throughout the book. For Scala starters - they always explain some of the special constructs or syntax features which is in fact a nice thing. Introduction and Appendix chapters provides basic information about the Spark core, RDDs (Resilient distributed datasets) or options of running Spark - whether in cluster (Mesos, YARN, Spark's own) or standalone settings. Throughout the book you can find some really worthy tips about Spark or data analysis - like using other serializer than the Java's default (they recommend kryo), overview of data cleansing and whole machine learning pipeline. To sum up, I recommend this book to every data scientist - because it demonstrates advanced topics like workload distribution and scaling on an enjoyable examples.
It is a so, so book. Examples are okay and the codes provided are "elegant" - certainly the result of spending hours and hours optimizing them; but that is not what a typical Spark users will face in life. The explanations are hurried and they make it very hard for the reader to connect the dots. It seems that the book's intent was right, but the application was woefully inadequate. If you do all the work in the book, you will be very competent at reading csv files - but is about all. The authors have a habit of providing esoteric "helper" functions to clean up the files but you don't really understand what is happening because either the explanations are thin or there is none to be found. A big part of data science is preparing the data - anyone can turn the crank on clean data but how do you go from the start to finish. This was their opportunity and they left a big gap. Spark's ML examples are nicer than what is presented in this book; paying for a book to get minimal information is a bit odd. I was really looking forward to going through this book and I am glad I did; it makes me appreciate authors who spend time writing good books.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale PDF
Advanced Analytics with Spark: Patterns for Learning from Data at Scale EPub
Advanced Analytics with Spark: Patterns for Learning from Data at Scale Doc
Advanced Analytics with Spark: Patterns for Learning from Data at Scale iBooks
Advanced Analytics with Spark: Patterns for Learning from Data at Scale rtf
Advanced Analytics with Spark: Patterns for Learning from Data at Scale Mobipocket
Advanced Analytics with Spark: Patterns for Learning from Data at Scale Kindle