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Hadoop real-world solutions cookbook /
Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and MahoutAbout This Book Implement outstanding Machine Learning use cases on your own analytics models and processes. Solutions to common problems when wor...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing
2016.
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| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 520 | |a Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and MahoutAbout This Book Implement outstanding Machine Learning use cases on your own analytics models and processes. Solutions to common problems when working with the Hadoop ecosystem. Step-by-step implementation of end-to-end big data use cases. Who This Book Is For Readers who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes. What You Will Learn Installing and maintaining Hadoop 2.X cluster and its ecosystem. Write advanced Map Reduce programs and understand design patterns. Advanced Data Analysis using the Hive, Pig, and Map Reduce programs. Import and export data from various sources using Sqoop and Flume. Data storage in various file formats such as Text, Sequential, Parquet, ORC, and RC Files. Machine learning principles with libraries such as Mahout Batch and Stream data processing using Apache SparkIn Detail Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business. Style and approach An easy-to-follow guide that walks you through world of big data. Each tool in the Hadoop ecosystem is explained in detail and the recipes are placed in such a manner that readers can implement them sequentially. Plenty of reference links are provided for advanced reading. | ||
| 588 | 0 | |a Vendor-supplied metadata. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; Acknowledgements; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Hadoop 2.X; Introduction; Installing a Single Node Hadoop Cluster; Installing a multi-node Hadoop cluster; Adding new nodes to existing Hadoop clusters; Executing balancer command for uniform data distribution; Entering and exiting from the safe mode in a Hadoop cluster; Decommissioning DataNodes; Performing benchmarking on a Hadoop cluster; Chapter 2: Exploring HDFS; Introduction; Loading data from a local machine to HDFS. | |
| 505 | 8 | |a Exporting data from HDFS to local machineChanging the replication factor of an existing file in HDFS; Setting the HDFS block size for all the files in a cluster; Setting the HDFS block size for a specific file in a cluster; Enabling transparent encryption for HDFS; Importing data from another Hadoop cluster; Recycling deleted data from trash to HDFS; Saving compressed data in HDFS; Chapter 3: Mastering Map Reduce Programs; Introduction; Writing the Map Reduce program in Java to analyze web log data; Executing the Map Reduce program in a Hadoop cluster. | |
| 505 | 8 | |a Adding support for a new writable data type in HadoopImplementing a user-defined counter in a Map Reduce program; Map Reduce program to find the top X; Map Reduce program to find distinct values; Map Reduce program to partition data using a custom partitioner; Writing Map Reduce results to multiple output files; Performing Reduce side Joins using Map Reduce; Unit testing the Map Reduce code using MRUnit; Chapter 4: Data Analysis Using Hive, Pig, and Hbase; Introduction; Storing and processing Hive data in a sequential file format; Storing and processing Hive data in the ORC file format. | |
| 505 | 8 | |a Storing and processing Hive data in the ORC file formatStoring and processing Hive data in the Parquet file format; Performing FILTER By queries in Pig; Performing Group By queries in Pig; Performing Order By queries in Pig; Performing JOINS in Pig; Writing a user-defined function in Pig; Analyzing web log data using Pig; Performing the Hbase operation in CLI; Performing Hbase operations in Java; Executing the MapReduce programming with an Hbase Table; Chapter 5: Advanced Data Analysis Using Hive; Introduction; Processing JSON data using Hive JSON SerDe. | |
| 505 | 8 | |a Processing XML data using Hive XML SerDeProcessing Hive data in AVRO format; Writing User Defined functions in Hive; Performing table joins in Hive; Executing map side joins in Hive; Performing context Ngram in Hive; Call Data Record Analytics using Hive; Twitter sentiment analysis using Hive; Implementing Change Data Capture using Hive; Multiple table inserting using Hive; Chapter 6: Data Import/Export Using Sqoop and Flume; Introduction; Importing data from RDMBS to HDFS using Sqoop; Exporting data from HDFS to RDBMS; Using query operator in Sqoop import. | |
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