Hive cookbook pdf free download

Git stats 1 commit. Failed to load latest commit information. View code. HCatalog server and hcat CLI HCatalog is a table and storage management layer for Hadoop that enables users with different data processing tools — including Pig and MapReduce — to more easily read and write data on the grid. For development Ruby Bundler bundler. About No description, website, or topics provided.

Releases No releases published. Packages 0 No packages published. You signed in with another tab or window. Reload to refresh your session. 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. 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. 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. It is full of useful recipes from industry experts, who will help you master your Tableau skills.

The complexity of tasks increase gradually, all the way to mastering advanced functionality through bite-sized, detailed recipes. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala.

However, you do not need to be acquainted with the Spark ML libraries and ecosystem. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to bleeding edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered.

It also highlights some key issues developers face while thinking about Scala for machine learning and during the switch over to Spark. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, clustering and learning systems. Towards the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Who This Book Is ForReaders who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes. Style and approachAn easy-to-follow guide that walks you through world of big data.

This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. This book takes you through different techniques for performing big data engineering using Microsoft cloud services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow.

Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You'll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs.

In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer.

By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure. Technical architects and database architects with experience in designing data or ETL applications either on-premise or on any other cloud vendor who want to learn Azure Data engineering concepts will also find this book useful. Prior knowledge of Azure fundamentals and data engineering concepts is needed. Basic experience with data science implementation tasks is expected.

Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning.

Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark apt for iterative algorithms , which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals.

The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations.

It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning.

The book also covers the source code explanation of latest Hive version. Hive Query Language is being used by other frameworks including spark. Towards the end you will cover integration of Hive with these frameworks. Starting with the basics and covering the core concepts with the practical usage, this book is a complete guide to learn and explore Hive offerings.

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