Programming Hive: Data Warehouse and Query Language for Hadoop #ad - You’ll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data. Use hive to create, filtering, grouping, and drop databases, functions, tables, and indexescustomize data formats and storage options, alter, views, from files to external databasesLoad and extract data from tables—and use queries, joining, and other conventional query methodsGain best practices for creating user defined functions UDFsLearn Hive patterns you should use and anti-patterns you should avoidIntegrate Hive with other data processing programsUse storage handlers for NoSQL databases and other datastoresLearn the pros and cons of running Hive on Amazon’s Elastic MapReduce .
Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop’s data warehouse infrastructure.
Hadoop: The Definitive Guide: Storage and Analysis at Internet ScaleO'Reilly Media #ad - This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters. Using hadoop 2 exclusively, crunch, flume, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, and Spark. Get ready to unlock the power of your data.
Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale #ad - With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. You’ll learn about recent changes to Hadoop, and explore new case studies on Hadoop’s role in healthcare systems and genomics data processing. Learn fundamental components such as mapreduce, including steps for developing applications with itset up and maintain a hadoop cluster running HDFS and MapReduce on YARNLearn two data formats: Avro for data serialization and Parquet for nested dataUse data ingestion tools such as Flume for streaming data and Sqoop for bulk data transferUnderstand how high-level data processing tools like Pig, Hive, and YARNExplore MapReduce in depth, HDFS, Crunch, and Spark work with HadoopLearn the HBase distributed database and the ZooKeeper distributed configuration service O reilly Media.
Learning Spark: Lightning-Fast Big Data AnalysisO'Reilly Media #ad - With spark, java, you can tackle big datasets quickly through simple APIs in Python, and Scala. O reilly Media. How can you work with it efficiently? Recently updated for Spark 1. 3, this book introduces apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run.
This edition includes new information on Spark SQL, setup, Spark Streaming, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.
Learning Spark: Lightning-Fast Big Data Analysis #ad - Quickly dive into spark capabilities such as distributed datasets, and the interactive shellleverage spark’s powerful built-in libraries, Mahout, in-memory caching, Spark Streaming, and MLlibUse one programming paradigm instead of mixing and matching tools like Hive, and StormLearn how to deploy interactive, and streaming applicationsConnect to data sources including HDFS, Hive, Hadoop, including Spark SQL, JSON, batch, and S3Master advanced topics like data partitioning and shared variables O reilly Media.
. Data in all domains is getting bigger.
Spark: The Definitive Guide: Big Data Processing Made SimpleO'Reilly Media #ad - O reilly Media. With an emphasis on improvements and new features in Spark 2. 0, authors bill chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications.
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. Developers and system administrators will learn the fundamentals of monitoring, and explore machine learning techniques and scenarios for employing MLlib, tuning, and debugging Spark, Spark’s scalable machine-learning library.
Spark: The Definitive Guide: Big Data Processing Made Simple #ad - Get a gentle overview of big data and sparklearn about dataframes, and tune spark clusters and applicationslearn the power of structured Streaming, Spark’s stream-processing engineLearn how you can apply MLlib to a variety of problems, and Datasets—Spark’s core APIs—through worked examplesDive into Spark’s low-level APIs, and execution of SQL and DataFramesUnderstand how Spark runs on a clusterDebug, RDDs, monitor, SQL, including classification or recommendation O reilly Media.
Data Analytics with Hadoop: An Introduction for Data ScientistsO'Reilly Media #ad - You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind hadoop and cluster computinguse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, clustering, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, mining, and collaborative filtering with Spark’s MLlib O reilly Media.
Instead of deployment, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, operations, or software development usually associated with distributed computing, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, Hive, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, and HBase.
Data Analytics with Hadoop: An Introduction for Data Scientists #ad - O reilly Media. Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. O reilly Media.
Programming Pig: Dataflow Scripting with HadoopO'Reilly Media #ad - With pig, you can batch-process data without having to create a full-fledged application, making it easy to experiment with new datasets. Updated with use cases and programming examples, this second edition is the ideal learning tool for new and experienced users alike. For many organizations, Hadoop is the first step for dealing with massive amounts of data.
O reilly Media. When you need to analyze terabytes of data, this book shows you how to do it efficiently with Pig. Delve into pig’s data model, join, group, including scalar and complex data typeswrite pig latin scripts to sort, project, and filter your dataUse Grunt to work with the Hadoop Distributed File System HDFSBuild complex data processing pipelines with Pig’s macros and modularity featuresEmbed Pig Latin in Python for iterative processing and other advanced tasksUse Pig with Apache Tez to build high-performance batch and interactive data processing applicationsCreate your own load and store functions to handle data formats and storage mechanisms O reilly Media.
Programming Pig: Dataflow Scripting with Hadoop #ad - The next step? processing and analyzing datasets with the Apache Pig scripting platform. You’ll find comprehensive coverage on key features such as the Pig Latin scripting language and the Grunt shell. O reilly Media. O reilly Media.
HBase: The Definitive Guide: Random Access to Your Planet-Size DataO'Reilly Media #ad - If you're looking for a scalable storage solution to accommodate a virtually endless amount of data, this book shows you how Apache HBase can fulfill your needs. O reilly Media. Many it executives are asking pointed questions about HBase. O reilly Media. O reilly Media. As the open source implementation of Google's BigTable architecture, HBase scales to billions of rows and millions of columns, while ensuring that write and read performance remain constant.
HBase: The Definitive Guide: Random Access to Your Planet-Size Data #ad - . This book provides meaningful answers, whether you’re evaluating this non-relational database or planning to put it into practice right away. Discover how tight integration with hadoop makes scalability with hbase easierdistribute large datasets across an inexpensive cluster of commodity serversAccess HBase with native Java clients, including the storage format, Avro, and moreIntegrate HBase with Hadoop's MapReduce framework for massively parallelized data processing jobsLearn how to tune clusters, or with gateway servers providing REST, design schemas, background processes, import bulk data, write-ahead log, copy tables, decommission nodes, or Thrift APIsGet details on HBase’s architecture, and many other tasks O reilly Media.
Kafka: The Definitive Guide: Real-Time Data and Stream Processing at ScaleO'Reilly Media #ad - O reilly Media. Every enterprise application creates data, metrics, whether it’s log messages, outgoing messages, user activity, or something else. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.
Engineers from confluent and linkedin who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. O reilly Media. Through detailed examples, you’ll learn kafka’s design principles, the controller, including the replication protocol, and architecture details, key APIs, reliability guarantees, and the storage layer.
Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale #ad - Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore kafka producers and consumers for writing and reading messagesunderstand kafka patterns and use-case requirements to ensure reliable data deliveryGet best practices for building data pipelines and applications with KafkaManage Kafka in production, tuning, and learn to perform monitoring, and maintenance tasksLearn the most critical metrics among Kafka’s operational measurementsExplore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems O reilly Media.
And how to move all of this data becomes nearly as important as the data itself. O reilly Media.
Getting Started with Impala: Interactive SQL for Apache HadoopO'Reilly Media #ad - Learn how to write, tune, and port sql queries and other statements for a Big Data environment, using Impala—the massively parallel processing SQL query engine for Apache Hadoop. O reilly Media. O reilly Media. O reilly Media. Ideal for database developers and business analysts, incremental statistics, subqueries, complex types, the latest revision covers analytics functions, and submission to the Apache incubator.
Getting started with impala includes advice from Cloudera’s development team, as well as insights from its consulting engagements with customers. Learn how impala integrates with a wide range of hadoop componentsattain high performance and scalability for huge data sets on production clustersExplore common developer tasks, date- and time-based values, such as porting code to Impala and optimizing performanceUse tutorials for working with billion-row tables, and other techniquesLearn how to transition from rigid schemas to a flexible model that evolves as needs changeTake a deep dive into joins and the roles of statistics O reilly Media.
Getting Started with Impala: Interactive SQL for Apache Hadoop #ad - The best practices in this practical guide help you design database schemas that not only interoperate with other Hadoop components, and are convenient for administers to manage and monitor, but also accommodate future expansion in data size and evolution of software capabilities. Written by john russell, documentation lead for the Cloudera Impala project, this book gets you working with the most recent Impala releases quickly.
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Hadoop Application Architectures: Designing Real-World Big Data ApplicationsO'Reilly Media #ad - O reilly Media. O reilly Media. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case. To reinforce those lessons, the book’s second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications.
Hadoop Application Architectures: Designing Real-World Big Data Applications #ad - . O reilly Media. Whether you’re designing a new hadoop application, or planning to integrate Hadoop into your existing data infrastructure, Hadoop Application Architectures will skillfully guide you through the process. This book covers:factors to consider when using hadoop to store and model databest practices for moving data in and out of the systemData processing frameworks, and HiveCommon Hadoop processing patterns, and Apache FlumeArchitecture examples for clickstream analysis, including MapReduce, GraphX, and other tools for large graph processing on HadoopUsing workflow orchestration and scheduling tools such as Apache OozieNear-real-time stream processing with Apache Storm, Apache Spark Streaming, fraud detection, Spark, such as removing duplicate records and using windowing analyticsGiraph, and data warehousing O reilly Media.
O reilly Media. O reilly Media. Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop.
Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational DatabaseO'Reilly Media #ad - O reilly Media. O reilly Media. The authors provide mysql, netezza, teradata, oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, or other relational systems. Transfer data from a single database table into your hadoop ecosystemkeep table data and hadoop in sync by importing data incrementallyImport data from more than one database tableCustomize transferred data by calling various database functionsExport generated, processed, or backed-up data from Hadoop to your databaseRun Sqoop within Oozie, Hadoop’s specialized workflow schedulerLoad data into Hadoop’s data warehouse Hive or database HBaseHandle installation, connection, and syntax issues common to specific database vendors O reilly Media.
O reilly Media. O reilly Media. O reilly Media. O reilly Media. Integrating data from multiple sources is essential in the age of big data, but it can be a challenging and time-consuming task. This handy cookbook provides dozens of ready-to-use recipes for using Apache Sqoop, the command-line interface application that optimizes data transfers between relational databases and Hadoop.
Apache Sqoop Cookbook: Unlocking Hadoop for Your Relational Database #ad - Sqoop is both powerful and bewildering, but with this cookbook’s problem-solution-discussion format, you’ll quickly learn how to deploy and then apply Sqoop in your environment.