Easy, hands-on recipes to help you understand Hive and its integration with frameworks that are used widely in today's big data world About This Book Grasp a complete reference of different Hive topics. Get to know the latest recipes in development in Hive including CRUD operations Understand Hive internals and integration of Hive with different frameworks used in today's world. Who This Book Is For The book is intended for those who want to start in Hive or who have basic understanding of Hive framework. Prior knowledge of basic SQL command is also required What You Will Learn Learn different features and offering on the latest Hive Understand the working and structure of the Hive internals Get an insight on the latest development in Hive framework Grasp the concepts of Hive Data Model Master the key concepts like Partition, Buckets and Statistics Know how to integrate Hive with other frameworks such as Spark, Accumulo, etc In Detail Hive was developed by Facebook and later open sourced in Apache community. Hive provides SQL like interface to run queries on Big Data frameworks. Hive provides SQL like syntax also called as HiveQL that includes all SQL capabilities like analytical functions which are the need of the hour in today's Big Data world. This book provides you easy installation steps with different types of metastores supported by Hive. This book has simple and easy to learn recipes for configuring Hive clients and services. You would also learn different Hive optimizations including Partitions and Bucketing. 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. Style and approach 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.

Easy, hands-on recipes to help you understand Hive and its integration with frameworks that are used widely in today's big data world About This Book Grasp a complete reference of different Hive topics. Get to know the latest recipes in development in Hive including CRUD operations Understand Hive internals and integration of Hive with different frameworks used in today's world. Who This Book Is For The book is intended for those who want to start in Hive or who have basic understanding of Hive framework. Prior knowledge of basic SQL command is also required What You Will Learn Learn different features and offering on the latest Hive Understand the working and structure of the Hive internals Get an insight on the latest development in Hive framework Grasp the concepts of Hive Data Model Master the key concepts like Partition, Buckets and Statistics Know how to integrate Hive with other frameworks such as Spark, Accumulo, etc In Detail Hive was developed by Facebook and later open sourced in Apache community. Hive provides SQL like interface to run queries on Big Data frameworks. Hive provides SQL like syntax also called as HiveQL that includes all SQL capabilities like analytical functions which are the need of the hour in today's Big Data world. This book provides you easy installation steps with different types of metastores supported by Hive. This book has simple and easy to learn recipes for configuring Hive clients and services. You would also learn different Hive optimizations including Partitions and Bucketing. 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. Style and approach 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.

If you are a Big Data enthusiast and wish to use Hadoop v2 to solve your problems, then this book is for you. This book is for Java programmers with little to moderate knowledge of Hadoop MapReduce. This is also a one-stop reference for developers and system admins who want to quickly get up to speed with using Hadoop v2. It would be helpful to have a basic knowledge of software development using Java and a basic working knowledge of Linux.

Over 100 practical recipes to help you become an expert Hadoop administrator About This Book Become an expert Hadoop administrator and perform tasks to optimize your Hadoop Cluster Import and export data into Hive and use Oozie to manage workflow. Practical recipes will help you plan and secure your Hadoop cluster, and make it highly available Who This Book Is For If you are a system administrator with a basic understanding of Hadoop and you want to get into Hadoop administration, this book is for you. It's also ideal if you are a Hadoop administrator who wants a quick reference guide to all the Hadoop administration-related tasks and solutions to commonly occurring problems What You Will Learn Set up the Hadoop architecture to run a Hadoop cluster smoothly Maintain a Hadoop cluster on HDFS, YARN, and MapReduce Understand high availability with Zookeeper and Journal Node Configure Flume for data ingestion and Oozie to run various workflows Tune the Hadoop cluster for optimal performance Schedule jobs on a Hadoop cluster using the Fair and Capacity scheduler Secure your cluster and troubleshoot it for various common pain points In Detail Hadoop enables the distributed storage and processing of large datasets across clusters of computers. Learning how to administer Hadoop is crucial to exploit its unique features. With this book, you will be able to overcome common problems encountered in Hadoop administration. The book begins with laying the foundation by showing you the steps needed to set up a Hadoop cluster and its various nodes. You will get a better understanding of how to maintain Hadoop cluster, especially on the HDFS layer and using YARN and MapReduce. Further on, you will explore durability and high availability of a Hadoop cluster. You'll get a better understanding of the schedulers in Hadoop and how to configure and use them for your tasks. You will also get hands-on experience with the backup and recovery options and the performance tuning aspects of Hadoop. Finally, you will get a better understanding of troubleshooting, diagnostics, and best practices in Hadoop administration. By the end of this book, you will have a proper understanding of working with Hadoop clusters and will also be able to secure, encrypt it, and configure auditing for your Hadoop clusters. Style and approach This book contains short recipes that will help you run a Hadoop cluster efficiently. The recipes are solutions to real-life problems that administrators encounter while working with a Hadoop cluster

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 Mahout About 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 Spark In 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.

Perform advanced dashboard, visualization, and analytical techniques with Tableau Desktop, Tableau Prep, and Tableau Server Key FeaturesUnique problem-solution approach to aid effective business decision-makingCreate interactive dashboards and implement powerful business intelligence solutionsIncludes best practices on using Tableau with modern cloud analytics servicesBook Description Tableau has been one of the most popular business intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. Tableau 2019.x Cookbook is full of useful recipes from industry experts, who will help you master Tableau skills and learn each aspect of Tableau's ecosystem. This book is enriched with features such as Tableau extracts, Tableau advanced calculations, geospatial analysis, and building dashboards. It will guide you with exciting data manipulation, storytelling, advanced filtering, expert visualization, and forecasting techniques using real-world examples. From basic functionalities of Tableau to complex deployment on Linux, you will cover it all. Moreover, you will learn advanced features of Tableau using R, Python, and various APIs. You will learn how to prepare data for analysis using the latest Tableau Prep. In the concluding chapters, you will learn how Tableau fits the modern world of analytics and works with modern data platforms such as Snowflake and Redshift. In addition, you will learn about the best practices of integrating Tableau with ETL using Matillion ETL. By the end of the book, you will be ready to tackle business intelligence challenges using Tableau's features. What you will learnUnderstand the basic and advanced skills of Tableau DesktopImplement best practices of visualization, dashboard, and storytellingLearn advanced analytics with the use of build in statisticsDeploy the multi-node server on Linux and WindowsUse Tableau with big data sources such as Hadoop, Athena, and SpectrumCover Tableau built-in functions for forecasting using R packagesCombine, shape, and clean data for analysis using Tableau PrepExtend Tableau’s functionalities with REST API and R/PythonWho this book is for Tableau 2019.x Cookbook is for data analysts, data engineers, BI developers, and users who are looking for quick solutions to common and not-so-common problems faced while using Tableau products. Put each recipe into practice by bringing the latest offerings of Tableau 2019.x to solve real-world analytics and business intelligence challenges. Some understanding of BI concepts and Tableau is required.

If you are a data analyst, developer, or simply someone who wants to use Hive to explore and analyze data in Hadoop, this is the book for you. Whether you are new to big data or an expert, with this book, you will be able to master both the basic and the advanced features of Hive. Since Hive is an SQL-like language, some previous experience with the SQL language and databases is useful to have a better understanding of this book.

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. 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. The authors provide MySQL, Oracle, and PostgreSQL database examples on GitHub that you can easily adapt for SQL Server, Netezza, Teradata, or other relational systems. Transfer data from a single database table into your Hadoop ecosystem Keep table data and Hadoop in sync by importing data incrementally Import data from more than one database table Customize transferred data by calling various database functions Export generated, processed, or backed-up data from Hadoop to your database Run Sqoop within Oozie, Hadoop’s specialized workflow scheduler Load data into Hadoop’s data warehouse (Hive) or database (HBase) Handle installation, connection, and syntax issues common to specific database vendors

Over 90 recipes to help you orchestrate modern ETL/ELT workflows and perform analytics using Azure services more easily Key FeaturesBuild highly efficient ETL pipelines using the Microsoft Azure Data servicesCreate and execute real-time processing solutions using Azure Databricks, Azure Stream Analytics, and Azure Data ExplorerDesign and execute batch processing solutions using Azure Data FactoryBook Description Data engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. 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. It takes you through different techniques for performing big data engineering using Microsoft Azure Data 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. You'll then work with different Cosmos DB APIs and Azure SQL Database. 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. What you will learnUse Azure Blob storage for storing large amounts of unstructured dataPerform CRUD operations on the Cosmos Table APIImplement elastic pools and business continuity with Azure SQL DatabaseIngest and analyze data using Azure Synapse AnalyticsDevelop Data Factory data flows to extract data from multiple sourcesManage, maintain, and secure Azure Data Factory pipelinesProcess streaming data using Azure Stream Analytics and Data ExplorerWho this book is for This book is for Data Engineers, Database administrators, Database developers, and extract, load, transform (ETL) developers looking to build expertise in Azure Data engineering using a recipe-based approach. Technical architects and database architects with experience in designing data or ETL applications either on-premise or on any other cloud vendor who wants to learn Azure Data engineering concepts will also find this book useful. Prior knowledge of Azure fundamentals and data engineering concepts is needed.

As part of Packt's cookbook series, each recipe offers a practical, step-by-step solution to common problems found in HBase administration. This book is for HBase administrators, developers, and will even help Hadoop administrators. You are not required to have HBase experience, but are expected to have a basic understanding of Hadoop and MapReduce.