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Advanced Analytics with Spark: Patterns for Learning from Data at Scale, by Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills
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In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.
You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications.
Patterns include:
- Recommending music and the Audioscrobbler data set
- Predicting forest cover with decision trees
- Anomaly detection in network traffic with K-means clustering
- Understanding Wikipedia with Latent Semantic Analysis
- Analyzing co-occurrence networks with GraphX
- Geospatial and temporal data analysis on the New York City Taxi Trips data
- Estimating financial risk through Monte Carlo simulation
- Analyzing genomics data and the BDG project
- Analyzing neuroimaging data with PySpark and Thunder
- Sales Rank: #49698 in Books
- Brand: Ryza, Sandy/ Laserson, Uri/ Owen, Sean/ Wills, Josh
- Published on: 2015-04-20
- Original language: English
- Number of items: 1
- Dimensions: 9.17" h x .58" w x 7.01" l, .0 pounds
- Binding: Paperback
- 276 pages
About the Author
Sandy Ryza is a data scientist at Cloudera and active contributor to the Apache Spark project. He recently led Spark development at Cloudera and now spends his time helping customers with a variety of analytic use cases on Spark. He is also a member of the Hadoop Project Management Committee.
Uri Laserson is a data scientist at Cloudera, where he focuses on Python in the Hadoop ecosystem. He also helps customers deploy Hadoop on a wide range of problems, focusing on life sciences and health care. Previously, Uri cofounded Good Start Genetics, a next generationdiagnostics company while working towards a PhD in biomedical engineering at MIT.
Sean Owen is Director of Data Science for EMEA at Cloudera. He has been a significant contributor to the Apache Mahout machine learning project since 2009, and authored its “Taste” recommender framework. He created the Oryx (formerly Myrrix) project for realtime large scale learning on Hadoop, built on lambda architecture principles, and has contributed to Spark and Spark’s MLlib project.
Josh Wills is Cloudera's Senior Director of Data Science, working with customers and engineers to develop Hadoop based solutions across a wide range of industries. He is the founder and VP of the Apache Crunch project for creating optimized MapReduce and Spark pipelines in Java.Prior to joining Cloudera, Josh worked at Google, where he worked on the ad auction system and then led the development of the analytics infrastructure used in Google+.
Most helpful customer reviews
22 of 23 people found the following review helpful.
Great introduction to real world data science at scale
By Ram
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.
16 of 17 people found the following review helpful.
Advanced and Scala heavy
By Brian Castelli
This is a solid book, with practical case study examples that one can follow. It really is an "advanced" book. One can learn quite a bit from this volume, but if you're a beginner you should start with something else. For beginners, I recommend Learning Spark (http://www.amazon.com/gp/product/B00SW0TY8O). I was disappointed with this advanced volume in that the authors focused almost exclusively on scala. This focus leads us down the path to unnecessary complexity in at least a few places. I would have liked to see more examples using Spark's pyspark library for Python.
7 of 7 people found the following review helpful.
If you are looking for a intro to data science, data analysis and machine learning at scale - this is the right book
By Adam Lieskovsky
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.
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