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HADOOP IN PRACTICE EBOOK

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Title: Audiobook Hadoop in Practice Ebook, Author: g-mailix, Name: Audiobook Hadoop in Practice Ebook, Length: 1 pages, Page: 1. Hadoop in Practice: Includes 85 Techniques [Alex Holmes] on cittadelmonte.info * FREE* shipping on qualifying offers. Summary Hadoop in Practice collects Did you know that Packt offers eBook versions of every book published, with PDF and ePub of Hadoop fit together and identify some areas of best practice.


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Hadoop in Practice collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like . An eBook copy of the previous edition of this book is included at no additional cost. Hadoop in Practice, Second Edition provides over tested, instantly. cittadelmonte.info . 8 Integrating R and Hadoop for statistics and more .. Purchase of Hadoop in Practice includes free access to a private web forum run.

Hadoop in Practice, Second Edition provides over tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere. It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout.

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Write a review Rate this item: Preview this item Preview this item. Hadoop in practice Author: Alex Holmes Publisher: Shelter Island, N. Subjects Apache Hadoop. File organization Computer science Electronic data processing -- Distributed processing. More like this Similar Items. Show all links. Allow this favorite library to be seen by others Keep this favorite library private.

Find a copy in the library Finding libraries that hold this item Electronic books Additional Physical Format: Print version: Holmes, Alex, Hadoop in practice.

Shelter Island, NY: Document, Internet resource Document Type: Alex Holmes Find more information about: Alex Holmes. Reviews User-contributed reviews Add a review and share your thoughts with other readers. Be the first.

Part 1 Background and fundamentals

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All rights reserved. Privacy Policy Terms and Conditions. Remember me on this computer. Cancel Forgot your password? Technique 59 A basic repartition join.

Technique 60 Optimizing the repartition join. Technique 61 Using Bloom filters to cut down on shuffled data. Data skew in reduce-side joins. Technique 62 Joining large datasets with high join-key cardinality. Technique 63 Handling skews generated by the hash partitioner. Sorting 6. Secondary sort. Technique 64 Implementing a secondary sort. Technique 65 Sorting keys across multiple reducers.

Sampling Technique 66 Writing a reservoir-sampling InputFormat. Utilizing data structures and algorithms at scale 7. Modeling data and solving problems with graphs 7. Modeling graphs. Technique 67 Find the shortest distance between two users. Using Giraph to calculate PageRank over a web graph. Technique 69 Calculate PageRank over a web graph. HyperLogLog 7. A brief introduction to HyperLogLog.

Technique 71 Using HyperLogLog to calculate unique counts. Tuning, debugging, and testing 8.

Measure, measure, measure. Tuning MapReduce 8. Common inefficiencies in MapReduce jobs. Technique 72 Viewing job statistics.

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Technique 74 Dealing with a large number of input splits. Technique 77 Blazingly fast sorting with binary comparators. Technique 78 Tuning the shuffle internals. Technique 79 Too few or too many reducers. Technique 80 Using stack dumps to discover unoptimized user code. Technique 81 Profiling your map and reduce tasks. Debugging 8. Accessing container log output.

Technique 82 Examining task logs. Accessing container start scripts. Technique 83 Figuring out the container startup command. Technique 84 Force container JVMs to generate a heap dump.

Part 1 Background and Fundamentals

MapReduce coding guidelines for effective debugging. Technique 85 Augmenting MapReduce code for better debugging. Testing MapReduce jobs 8. Essential ingredients for effective unit testing. Technique 87 Heavyweight job testing with the LocalJobRunner.

Hadoop in practice

SQL on Hadoop 9. Hive 9. Hive basics. Technique 89 Working with text files. Technique 90 Exporting data to local disk. User-defined functions in Hive. Impala 9. Impala vs. Technique 95 Working with Parquet. Technique 96 Refreshing metadata. User-defined functions in Impala. Spark SQL 9. Spark Technique 99 Language-integrated queries.

Writing a YARN application Fundamentals of building a YARN application The mechanics of a YARN application. Technique A bare-bones ApplicationMaster. Technique Running the application and accessing logs.

Technique Debugging using an unmanaged application master. Additional YARN application capabilities RPC between components. Checkpointing application progress.

YARN programming abstractions Appendix A: Installing Hadoop and friends A. Code for the book. Integrating R and Hadoop for statistics and more Comparing R and MapReduce integrations.

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R and streaming Streaming and map-only R. Technique Calculate the daily mean for stocks. Streaming, R, and full MapReduce.

Technique Calculate the cumulative moving average for stocks. Predictive analytics with Mahout Using recommenders to make product suggestions Visualizing similarity metrics. Technique Item-based recommenders using movie ratings. Classification Writing a homemade naive Bayesian classifier. A scalable spam-detection classification system.

Technique Using Mahout to train and test a spam classifier. Additional classification algorithms. Clustering with K-means A gentle introduction. Technique K-means with a synthetic 2D dataset. Other Mahout clustering algorithms. About the book It's always a good time to upgrade your Hadoop skills!

Readers need to know a programming language like Java and have basic familiarity with Hadoop. About the author Alex Holmes works on tough big-data problems. Hadoop in Practice, Second Edition combo added to cart. Your book will ship via to:.

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