Rahul Bachhwalia is pursuing a B.E. in Automobile Engineering from Rustam Ji Institute of Technology in Gwalior. He has a GPA of 6.95 and scored over 80% in his high school and intermediate exams. Rahul has experience participating in go-kart championships and workshops on automotive technologies. He seeks to pursue a career in a reputed firm where he can professionally excel and contribute through dedicated work.
La pandemia de COVID-19 ha tenido un impacto significativo en la economía mundial y las vidas de las personas. Muchos países han implementado medidas de confinamiento que han cerrado negocios y escuelas, y han pedido a las personas que practiquen el distanciamiento social. Aunque estas medidas han ayudado a reducir la propagación del virus, también han causado un aumento en el desempleo y problemas económicos. Se espera que la recuperación económica lleve tiempo a medida que los países reabran gradualmente y las personas se sientan seguras para
http://mindpersuasion.com/ir/
In order to maximize your ability to leverage the collective unconscious, you've got to understand what it is, and how to interface with it. Learn How: http://mindpersuasion.com/ir/
The document outlines the key steps in an online training program for Hadoop including setting up a virtual Hadoop cluster, loading and parsing payment data from XML files into databases incrementally using scheduling, building a migration flow from databases into Hadoop and Hive, running Hive queries and exporting data back to databases, and visualizing output data in reports. The training will be delivered online over 20 hours using tools like GoToMeeting.
This document provides examples and explanations of key concepts in Hive Query Language (HQL) including how to create and populate tables, load data into Hive, write queries, and descriptions of managed vs external tables, partitions, and buckets. It also summarizes Hive architecture, clients, metastore configurations, and HiveQL capabilities compared to SQL standards.
This document provides an overview of Hadoop and how it can be used for data consolidation, schema flexibility, and query flexibility compared to a relational database. It describes the key components of Hadoop including HDFS for storage and MapReduce for distributed processing. Examples of industry use cases are also presented, showing how Hadoop enables affordable long-term storage and scalable processing of large amounts of structured and unstructured data.
Using Hadoop and Hive to Optimize Travel Search, WindyCityDB 2010Jonathan Seidman
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Using Hadoop and Hive, Orbitz analyzed large amounts of web analytics data to optimize travel search and gain insights. They loaded over 500GB of daily log data into Hadoop and used Hive to run SQL-like queries to derive metrics like the position of booked hotels in search results and booking position trends by location. Statistical analysis in R helped explore trends, correlations and outliers in the Hive datasets to help machine learning applications.
The document discusses Hive, an open source data warehousing system built on Hadoop that allows users to query large datasets using SQL. It describes Hive's data model, architecture, query language features like joins and aggregations, optimizations, and provides examples of how queries are executed using MapReduce. The document also covers Hive's metastore, external tables, data types, and extensibility features.
Analytical Queries with Hive: SQL Windowing and Table FunctionsDataWorks Summit
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Hive Query Language (HQL) is excellent for productivity and enables reuse of SQL skills, but falls short in advanced analytic queries. Hive`s Map & Reduce scripts mechanism lacks the simplicity of SQL and specifying new analysis is cumbersome. We developed SQLWindowing for Hive(SQW) to overcome these issues. SQW introduces both Windowing and Table Functions to the Hive user. SQW appears as a HQL extension with table functions and windowing clauses interspersed with HQL. This means the user stays within a SQL-like interface, while simultaneously having these capabilities available. SQW has been published as an open source project. It is available as both a CLI and an embeddable jar with a simple query API. There are pre-built functions for windowing to do Ranking, Aggregation, Navigation and Linear Regression. There are Table functions to do Time Series Analysis, Allocations, and Data Densification. Functions can be chained for more complex analysis. Under the covers MR mechanics are used to partition and order data. The fundamental interface is the tableFunction, whose core job is to operate on data partitions. Function implemenations are isolated from MR mechanics, focus purely on computation logic. Groovy scripting can be used for core implementation and parameterizing behavior. Writing functions typically involves extending one of the existing Abstract functions.
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
This slide show you how to create a keyboard-extension, that is the new feature for expanding iOS system, by using Xcode 6. And in this slide also explain how keyboard-extensions working and connecting.
The document discusses Hive, an open source data warehousing system built on Hadoop that allows users to query large datasets using SQL. It describes Hive's data model, architecture, query language features like joins and aggregations, optimizations, and provides examples of how queries are executed using MapReduce. The document also covers Hive's metastore, external tables, data types, and extensibility features.
Analytical Queries with Hive: SQL Windowing and Table FunctionsDataWorks Summit
?
Hive Query Language (HQL) is excellent for productivity and enables reuse of SQL skills, but falls short in advanced analytic queries. Hive`s Map & Reduce scripts mechanism lacks the simplicity of SQL and specifying new analysis is cumbersome. We developed SQLWindowing for Hive(SQW) to overcome these issues. SQW introduces both Windowing and Table Functions to the Hive user. SQW appears as a HQL extension with table functions and windowing clauses interspersed with HQL. This means the user stays within a SQL-like interface, while simultaneously having these capabilities available. SQW has been published as an open source project. It is available as both a CLI and an embeddable jar with a simple query API. There are pre-built functions for windowing to do Ranking, Aggregation, Navigation and Linear Regression. There are Table functions to do Time Series Analysis, Allocations, and Data Densification. Functions can be chained for more complex analysis. Under the covers MR mechanics are used to partition and order data. The fundamental interface is the tableFunction, whose core job is to operate on data partitions. Function implemenations are isolated from MR mechanics, focus purely on computation logic. Groovy scripting can be used for core implementation and parameterizing behavior. Writing functions typically involves extending one of the existing Abstract functions.
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
This slide show you how to create a keyboard-extension, that is the new feature for expanding iOS system, by using Xcode 6. And in this slide also explain how keyboard-extensions working and connecting.
This document discusses the four types of inner classes in Java: static inner classes, member inner classes, local inner classes, and anonymous inner classes. Static inner classes can access only static members of the enclosing class and are compiled separately. Member inner classes are like instance variables and can access all members of the enclosing class. Local inner classes are defined within a method and can only access final local variables. Anonymous inner classes do not have a class name and implicitly extend or implement classes and interfaces.
21. JComponent Class
JComponent Class API:
它从Component and Container 继承了许多方法,同时
也提供了一些新的方法。它为它的继承者提供了如下
常用功能:
? Customizing Component Appearance
? Setting Component State
? Handling Events
? Painting Components
? Dealing with the Containment Hierarchy
? Laying Out Components
? Getting Size and Position Information
? Specifying Absolute Size and Position