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Intro to Hadoop Presentation at Carnegie Mellon - Silicon Valley
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John Sing's Edge 2013 presentation, detailing when/where/how external storage products and/or system software (i.e. GPFS) can be effectively used in a Hadoop storage environment. Many Hadoop situations absolutely required direct attached storage. However, there are many intelligent situations where shared external storage may make sense in a Hadoop environment. This presentation details how/why/where, and promotes taking an intelligent, Hadoop-aware approach to deciding between internal storage and external shared storage. Having full awareness of Hadoop considerations is essential to selecting either internal or external shared storage in Hadoop environment.
Hadoop_Its_Not_Just_Internal_Storage_V14
Hadoop_Its_Not_Just_Internal_Storage_V14
John Sing
Marcel Kornacker is a tech lead at Cloudera In this talk from Impala architect Marcel Kornacker, you will explore: How Impala's architecture supports query speed over Hadoop data that not only convincingly exceeds that of Hive, but also that of a proprietary analytic DBMS over its own native columnar format. The current state of, and roadmap for, Impala's analytic SQL functionality. An example configuration and benchmark suite that demonstrate how Impala offers a high level of performance, functionality, and ability to handle a multi-user workload, while retaining Hadoop’s traditional strengths of flexibility and ease of scaling.
Building a Hadoop Data Warehouse with Impala
Building a Hadoop Data Warehouse with Impala
huguk
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker. Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
Building a Hadoop Data Warehouse with Impala
Building a Hadoop Data Warehouse with Impala
Swiss Big Data User Group
Data ingest is a deceptively hard problem. In the world of big data processing, it becomes exponentially more difficult. It's not sufficient to simply land data on a system, that data must be ready for processing and analysis. The Kite SDK is a data API designed for solving the issues related to data infest and preparation. In this talk you'll see how Kite can be used for everything from simple tasks to production ready data pipelines in minutes.
Building data pipelines with kite
Building data pipelines with kite
Joey Echeverria
Cloudera's Mark Grover presents Application Architectures with Hadoop at Data Day Texas 2015.
Application Architectures with Hadoop | Data Day Texas 2015
Application Architectures with Hadoop | Data Day Texas 2015
Cloudera, Inc.
Brief Description on Big data and hadoop
Big data and hadoop
Big data and hadoop
Prashanth Yennampelli
Hadoop Summit 2015
Enterprise-Grade Rolling Upgrade for a Live Hadoop Cluster
Enterprise-Grade Rolling Upgrade for a Live Hadoop Cluster
DataWorks Summit
Hadoop
Hadoop
Nishant Gandhi
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Application architectures with Hadoop – Big Data TechCon 2014
Application architectures with Hadoop – Big Data TechCon 2014
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Webinar: Productionizing Hadoop: Lessons Learned - 20101208
Hadoop Essentials -- The What, Why and How to Meet Agency Objectives
Hadoop Essentials -- The What, Why and How to Meet Agency Objectives
Big data - Online Training
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SpringPeople Introduction to Apache Hadoop
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Hadoop Infrastructure (Oct. 3rd, 2012)
Hadoop Infrastructure (Oct. 3rd, 2012)
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q3
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q3
Hadoop and Hive in Enterprises
Hadoop and Hive in Enterprises
Strata NY 2014 - Architectural considerations for Hadoop applications tutorial
Strata NY 2014 - Architectural considerations for Hadoop applications tutorial
Open-BDA Hadoop Summit 2014 - Mr. Slim Baltagi (Building a Modern Data Archit...
Open-BDA Hadoop Summit 2014 - Mr. Slim Baltagi (Building a Modern Data Archit...
Hortonworks - What's Possible with a Modern Data Architecture?
Hortonworks - What's Possible with a Modern Data Architecture?
Hadoop_Its_Not_Just_Internal_Storage_V14
Hadoop_Its_Not_Just_Internal_Storage_V14
Building a Hadoop Data Warehouse with Impala
Building a Hadoop Data Warehouse with Impala
Building a Hadoop Data Warehouse with Impala
Building a Hadoop Data Warehouse with Impala
Building data pipelines with kite
Building data pipelines with kite
Application Architectures with Hadoop | Data Day Texas 2015
Application Architectures with Hadoop | Data Day Texas 2015
Big data and hadoop
Big data and hadoop
Enterprise-Grade Rolling Upgrade for a Live Hadoop Cluster
Enterprise-Grade Rolling Upgrade for a Live Hadoop Cluster
Hadoop
Hadoop
More from markgrover
Presentation at SF Big Analytics meetup on Jan 12, 2021. https://www.meetup.com/SF-Big-Analytics/events/275217663/
From discovering to trusting data
From discovering to trusting data
markgrover
Presentation from Marcos Iglesias and Daniel Won on designs for Data Lineage in Amundsen
Amundsen lineage designs - community meeting, Dec 2020
Amundsen lineage designs - community meeting, Dec 2020
markgrover
Talk by Joe Atkins-Turkish at Brex on why Brex chose Amundsen and their recent addition of Looker integration to Amundsen on November 5, 2020.
Amundsen at Brex and Looker integration
Amundsen at Brex and Looker integration
markgrover
REA Group's journey with Data Cataloging. Presented at Amundsen community meeting on November 5th, 2020. Presented by Stacy Sterling, Abhinay Kathuria and Alex Kompos at REA Group.
REA Group's journey with Data Cataloging and Amundsen
REA Group's journey with Data Cataloging and Amundsen
markgrover
Presentation on Amundsen gremlin proxy by Josh Hoskins at Square.
Amundsen gremlin proxy design
Amundsen gremlin proxy design
markgrover
Hear about how Lyft and Square are solving data discovery and data security challenges using a shared open source project - Amundsen. Talk details and abstract: https://www.datacouncil.ai/talks/amundsen-from-discovering-data-to-securing-data
Amundsen: From discovering to security data
Amundsen: From discovering to security data
markgrover
Hear about how Lyft and Square are solving data discovery and data security challenges using a shared open source project - Amundsen. Talk details and abstract: https://www.datacouncil.ai/talks/amundsen-from-discovering-data-to-securing-data
Amundsen: From discovering to security data
Amundsen: From discovering to security data
markgrover
Data Discovery & Trust through Metadata by Mark Grover. Jan 2020.
Data Discovery & Trust through Metadata
Data Discovery & Trust through Metadata
markgrover
Talk on Data Discovery and Metadata by Mark Grover from July 2019. Goes into detail of the problem, build/buy/adopt analysis and Lyft's solution - Amundsen, along with thoughts on the future.
Data Discovery and Metadata
Data Discovery and Metadata
markgrover
Talk on Lyft data platform by Mark Grover and Deepak Tiwari at Strata in May 2019
The Lyft data platform: Now and in the future
The Lyft data platform: Now and in the future
markgrover
Presentation on Disrupting Data Discovery at Strata in May 2019.
Disrupting Data Discovery
Disrupting Data Discovery
markgrover
Talk on TFX and Beam by Robert Crowe, developer advocate at Google, focussed on TensorFlow. Learn how the TensorFlow Extended (TFX) project is utilizing Apache Beam to simplify pre- and post-processing for ML pipelines. TFX provides a framework for managing all of necessary pieces of a real-world machine learning project beyond simply training and utilizing models. Robert will provide an overview of TFX, and talk in a little more detail about the pieces of the framework (tf.Transform and tf.ModelAnalysis) which are powered by Apache Beam.
TensorFlow Extension (TFX) and Apache Beam
TensorFlow Extension (TFX) and Apache Beam
markgrover
In this Strata 2018 presentation, Ted Malaska and Mark Grover discuss how to make the most of big data at speed. https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/72396
Big Data at Speed
Big Data at Speed
markgrover
Near real-time anomaly detection at Lyft, by Mark Grover and Thomas Weise at Strata NY 2018. https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/69155
Near real-time anomaly detection at Lyft
Near real-time anomaly detection at Lyft
markgrover
Presentation on dogfooding data at Lyft by Mark Grover and Arup Malakar on Oct 25, 2017 at Big Analytics Meetup (https://www.meetup.com/SF-Big-Analytics/events/243896328/)
Dogfooding data at Lyft
Dogfooding data at Lyft
markgrover
How to fight cyber security threats using Big Data tools - An introduction to Apache Spot (incubating)
Fighting cybersecurity threats with Apache Spot
Fighting cybersecurity threats with Apache Spot
markgrover
Abridged version of the Fraud Detection tutorial by Hadoop Application Architectures co-authors in London at Strata + Hadoop World 2016
Fraud Detection with Hadoop
Fraud Detection with Hadoop
markgrover
Top 5 mistakes when writing Spark applications at Seattle Big Data meetup
Top 5 mistakes when writing Spark applications
Top 5 mistakes when writing Spark applications
markgrover
This is a talk given at Advanced Spark meetup in San Francisco (http://www.meetup.com/Advanced-Apache-Spark-Meetup/events/223668878/). It focusses on common mistakes when writing Spark applications and how to avoid them.
Top 5 mistakes when writing Spark applications
Top 5 mistakes when writing Spark applications
markgrover
Impala presentation by Mark Grover at GlueCon in Broomfield, CO on May 22, 2014
Introduction to Impala
Introduction to Impala
markgrover
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From discovering to trusting data
From discovering to trusting data
Amundsen lineage designs - community meeting, Dec 2020
Amundsen lineage designs - community meeting, Dec 2020
Amundsen at Brex and Looker integration
Amundsen at Brex and Looker integration
REA Group's journey with Data Cataloging and Amundsen
REA Group's journey with Data Cataloging and Amundsen
Amundsen gremlin proxy design
Amundsen gremlin proxy design
Amundsen: From discovering to security data
Amundsen: From discovering to security data
Amundsen: From discovering to security data
Amundsen: From discovering to security data
Data Discovery & Trust through Metadata
Data Discovery & Trust through Metadata
Data Discovery and Metadata
Data Discovery and Metadata
The Lyft data platform: Now and in the future
The Lyft data platform: Now and in the future
Disrupting Data Discovery
Disrupting Data Discovery
TensorFlow Extension (TFX) and Apache Beam
TensorFlow Extension (TFX) and Apache Beam
Big Data at Speed
Big Data at Speed
Near real-time anomaly detection at Lyft
Near real-time anomaly detection at Lyft
Dogfooding data at Lyft
Dogfooding data at Lyft
Fighting cybersecurity threats with Apache Spot
Fighting cybersecurity threats with Apache Spot
Fraud Detection with Hadoop
Fraud Detection with Hadoop
Top 5 mistakes when writing Spark applications
Top 5 mistakes when writing Spark applications
Top 5 mistakes when writing Spark applications
Top 5 mistakes when writing Spark applications
Introduction to Impala
Introduction to Impala
Recently uploaded
Blood Donation Management System is a web database application that enables the public to make online session reservation, to view nationwide blood donation events online and at the same time provides centralized donor and blood stock database. This application is developed by using ASP.NET technology from Visual Studio with the MySQL 5.0 as the database management system. The methodology used to develop this system as a whole is Object Oriented Analysis and Design; whilst, the database for BDMS is developed by following the steps in Database Life Cycle. The targeted users for this application are the public who is eligible to donate blood ,'system moderator, administrator from National Blood Center and the staffs who are working in the blood banks of the participating hospitals. The main objective of the development of this application is to overcome the problems that exist in the current system, which are the lack of facilities for online session reservation and online advertising on the nationwide blood donation events, and also decentralized donor and blood stock database. Besides, extra features in the system such as security protection by using password, generating reports, reminders of blood stock shortage and workflow tracking can even enhance the efficiency of the management in the blood banks. The final result of this project is the development of web database application, which is the BDMS.
Online blood donation management system project.pdf
Online blood donation management system project.pdf
Kamal Acharya
Mining Data Stream
KIT-601 Lecture Notes-UNIT-3.pdf Mining Data Stream
KIT-601 Lecture Notes-UNIT-3.pdf Mining Data Stream
Dr. Radhey Shyam
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on. My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways. In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Event Management System Vb Net Project Report.pdf
Event Management System Vb Net Project Report.pdf
Kamal Acharya
Schematic diagram of INDIAN RAILWAYS Braking System With AutoCAD
BRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWING
BRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWING
KOUSTAV SARKAR
This project is being considered in order to reduce and totally eliminate loss of customers to competitors, and save the company from folding up. The current system is manual and it is time consuming. It is also cost ineffective, and average return is low and diminishing. Currently, customers can call or walk-in in order to rent or reserve a vehicle. The staff of the company will check their file to see which vehicle is available for rental. The current system is error prone and customers are dissatisfied. The goal of this project is to automate vehicle rental and reservation so that customers do not need to walk-in or call in order to reserve a vehicle. They can go online and reserve any kind of vehicle they want and that is available. Even when a customer chooses to walk-in, computers are available for him to go online and perform his reservation. When he choose to reserve by phone, any of the customer service representatives can help him reserve the vehicle speedily and issue him a reservation number. The VRS will maintain the database of all vehicles the company has. It will also keep track of all vehicle reservation and return. Reports will be generated bi-weekly. Reports for the Accounts Manager will detail the cost incurred to maintain each vehicle and revenue accrued on each vehicle. Reports for the Maintenance Manager will detail the present mileage of the car in order for him to take care of the vehicle servicing, and when each vehicle will be due for tag renewal. The Branch Manager’s report will detail total cost incurred and total revenue accrued, and the status of each vehicle so that he can decide whether to sell the vehicle or still keep it.
ONLINE VEHICLE RENTAL SYSTEM PROJECT REPORT.pdf
ONLINE VEHICLE RENTAL SYSTEM PROJECT REPORT.pdf
Kamal Acharya
An online book store is a virtual store on the Internet where customers can browse the catalog and select books of interest. At checkout time, the items in the e-library will be presented as an order. At that time, more information will be needed to complete the request. Usually, the customer will be asked to fill online form. An e- mail notification is sent to the customer as soon as the order is placed. This project intends different types of forms with many types of books like story, drama, romance, history, adventures, etc. it can manage studying of books online, customers can choose many types of books categories, etc. Here, the user may select desired book and view its price. The user may even search for specific books on the website. Once the user selects a book, he then has to fill in a form and the book is provided for the user.
Online book store management system project.pdf
Online book store management system project.pdf
Kamal Acharya
read it
Digital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdf
AbrahamGadissa
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Quality defects in TMT Bars, Possible causes and Potential Solutions.
Quality defects in TMT Bars, Possible causes and Potential Solutions.
PrashantGoswami42
Online movie ticket booking system for movies is a web-based program. This application allows users to purchase cinema tickets over the portal. To buy tickets, people must first register or log in. This website's backend is PHP and JavaScript, and the front end is HTML and CSS. All phases of the software development life cycle are efficiently managed in order to design and implement software. On the website, there are two panels: one for administrators and one for customers/users. The admin has the ability to add cinemas, movies, delete, halt execution, and add screens, among other things. The website is simple to navigate and appealing, saving the end user time.
A CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdf
A CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdf
Kamal Acharya
The Project deals with the development of the computerized system for maintaining the regular records and services that are undertaken in the furniture business. This project titled "Web Based Integrated Furniture Showroom Management System" has been aimed to design and computerized system that can handle various activities are been carried out at the Furniture Showroom. This application has been developed using PHP Programming Language as its front end and the back end is MYSQL Server In the existing system all the activities and record maintenance of the furniture showroom are done manually by the manager. The Project deals with the development of the computerized system for maintaining the regular records and services that are undertaken in this most important and large business oriented furniture business. This Project also enables the users to perform all the day to day business operations in the furniture showroom business most efficiently.
Furniture showroom management system project.pdf
Furniture showroom management system project.pdf
Kamal Acharya
This document presents the calculation of the electric field and electric potential in a coaxial cable using Maxwell's equations in the electrostatic case in an analytical and simulated manner using COMSOL Multiphysics.
Electrostatic field in a coaxial transmission line
Electrostatic field in a coaxial transmission line
JulioCesarSalazarHer1
The export maintenance system is a fully featured application that can help we manage fruit delivery business and achieve more control and information at a very low cost of total ownership. A fruit export maintains automatically monitors purchase, sales, supplier information. The system includes receiving fruit from the different supplier. Customer order is placed in the system, based on the order fruit has been sales to the customer. The report contains the details about product, purchase, sales, stock, and invoice. The main objective of this project is to computerize the company activities and to provide details about the production process at the fruit export maintenance system. The demand of fresh fruit fruits and processed food items in international and domestic market has shown a decent increase. This estimation is creating a necessity for growing more and more fruit fruits to cater the growing demand of domestic & international market. The customers effectively and hence help for establishing good relation between customer and fruit shop organization. It contains various customized modules for effectively maintaining fruit and stock information accurately and safely. When the fruits are sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting fruits for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item. The proposed project is developed to manage the fruit shop in the fruits for shop. The first module is the login. The admin should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
Fruit shop management system project report.pdf
Fruit shop management system project report.pdf
Kamal Acharya
it is about a data structure and algorithm mini project
retail automation billing system ppt.pptx
retail automation billing system ppt.pptx
faamieahmd
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems! Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected. R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production. An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred. R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance. Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production. It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call! Work done in cooperation with James Malloy and David Moelling from Tetra Engineering. More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
R&R Consult
The project developers created a system entitled Resort Management and Reservation System; it will provide better management and monitoring of the services in every resort business, especially D’ Rock Resort. To accommodate those out-of-town guests who want to remain and utilize the resort's services, the proponents planned to automate the business procedures of the resort and implement the system. As a result, it aims to improve business profitability, lower expenses, and speed up the resort's transaction processing. The resort will now be able to serve those potential guests, especially during the high season. Using websites for faster transactions to reserve on your desired time and date is another step toward technological advancement. Customers don’t need to walk in and hold in line for several hours. There is no problem in converting a paper-based transaction online; it's just the system that will be used that will help the resort expand. Moreover, Gerard (2012) stated that “The flexible online information structure was developed as a tool for the reservation theory's two primary applications. Computer use is more efficient, accurate, and faster than a manual or present lifestyle of operation. Using a computer has a vital role in our daily life and the advantages of the devices we use.
RESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdf
RESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdf
Kamal Acharya
5 robust and dependable Soil testing instruments by aimil check out! these instruments are use to measure different parameter of site soil before starting the construction works. Aimil is india's one of the top manufacturers and suppliers of engineering testing instruments ,specially for civil engineering domain. We are in instrumentation business for the past 90+ years. #aimil #soiltesting #soiltestinginstruments #testinginstruments #instrumentation #soiltest
Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...
Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...
Aimil Ltd
Teaching effects after 128 hours of Building Information Modeling course in Cracow, Poland. Natalia works in Revit, Navisworks and Dynamo for BIM Coordination position. More https://bim.edu.pl or https://bimedu.eu
Natalia Rutkowska - BIM School Course in Kraków
Natalia Rutkowska - BIM School Course in Kraków
bim.edu.pl
Now a day’s education plays a great role in development of any country. Many of education organizations try to increase education quality. One of the aspects of this improvement is managing of school resources. Education Management System carried on by any individual or institution engaged in providing a services to students, teachers, guardians and other persons are intermediary that performs one or more of the following functionalities – Student Admission, Employee Registration, Student List, Employee List, Student Attendance, Employee Attendance, Student Routine, Result Management, Payroll & Accounts. Education Management System (EMS) is such a service which provides all services for an educational institute to make your life easier and faster by assuring its performance. Easy User Management System, Easy Admission Process, Easy Attendance System. EMS is a system that will provide you a bird’s eye view of the functioning of the entire educational institution. It is a management information system helps to manage the different processes in an educational institution like General Administration, Staff Management, Academics, Student Management, and Accounts etc. The information is made using the latest technologies and help’s to make decision making a lot faster, effective and easier than ever before. Also helps to improve the overall quality of education of the institution. We use database and database technology are having a major impact on the growing use of computers. The implementation of the system was done using c# and SQL Server 2012 technologies, allowing system to be run in Windows OS. In a nutshell, Education Management Software managed your education institution by simplifying and automating processes and addressing the needs of all stakeholders helping them to be more efficient in their respective roles.
School management system project report.pdf
School management system project report.pdf
Kamal Acharya
Frame Works and Visualization
KIT-601 Lecture Notes-UNIT-5.pdf Frame Works and Visualization
KIT-601 Lecture Notes-UNIT-5.pdf Frame Works and Visualization
Dr. Radhey Shyam
PHP and MySQL project on Hall Booking System is a web based project and it has been developed in PHP and MySQL and we can manage Payment, Booking, Inventory, Booking Dates, Customers and Hall from this project. The main objective to develop Hall Booking System PHP, MySQL, JAVA SCRIPT and BOOTSRAP Project is to overcome the manual errors and make a computerized system. In this project, there are various type of modules available to manage Customers, Booking, Payment. We can also generate reports for Booking, Payment, Booking Dates, Hall. Here the Payment module manage all the operations of Payment, Booking module can manage Booking, Inventory module is normally developed for managing Inventory, Booking Dates module manages Booking Dates operations, Customers module has been implemented to manage Customers. In this project all the modules like Payment, Booking Dates, Booking are tightly coupled and we can track the information easily. Ifyou are looking for Free Hall Booking System Project in PHP and MySQL then you can visit our free projects section. We can easily get the list of wedding halls & lawns in Nagpur. Also we have detailed contact information for some particular hall. But we cannot get the availability about hall. So background behind this web portal is that it gives the area wise listing of wedding halls & lawns with the detailed information of individual and also display for particular date the hall is available or not. Just dial is the system in which we can only find the name of Hall and Lawns in city. In just dial we cannot find Halls in specific area. This system cannot show all information about any Hall. This system is not able to book the Halls online. The A Web Based Hall Booking Management System is designed to overcome the disadvantage of previous system.We can easily get the list of Wedding Halls. But we cannot get the availability about Hall. So background behind this web portal is that it gives the area wise listing of Wedding Halls with the detailed information of individual and also display for particular date the Hall is available or not. This is a special type of web portal to easily get the information of all Wedding Halls in Nagpur which display separate calendar for separate Hall. For particular date the Hall. We can availability of Hall as well as Lawns detailed information about individuals Hall in our web portal . It provides all facilities to clients with lowest cost and lowest maintenance problems.
Hall booking system project report .pdf
Hall booking system project report .pdf
Kamal Acharya
Recently uploaded
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Online blood donation management system project.pdf
Online blood donation management system project.pdf
KIT-601 Lecture Notes-UNIT-3.pdf Mining Data Stream
KIT-601 Lecture Notes-UNIT-3.pdf Mining Data Stream
Event Management System Vb Net Project Report.pdf
Event Management System Vb Net Project Report.pdf
BRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWING
BRAKING SYSTEM IN INDIAN RAILWAY AutoCAD DRAWING
ONLINE VEHICLE RENTAL SYSTEM PROJECT REPORT.pdf
ONLINE VEHICLE RENTAL SYSTEM PROJECT REPORT.pdf
Online book store management system project.pdf
Online book store management system project.pdf
Digital Signal Processing Lecture notes n.pdf
Digital Signal Processing Lecture notes n.pdf
Quality defects in TMT Bars, Possible causes and Potential Solutions.
Quality defects in TMT Bars, Possible causes and Potential Solutions.
A CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdf
A CASE STUDY ON ONLINE TICKET BOOKING SYSTEM PROJECT.pdf
Furniture showroom management system project.pdf
Furniture showroom management system project.pdf
Electrostatic field in a coaxial transmission line
Electrostatic field in a coaxial transmission line
Fruit shop management system project report.pdf
Fruit shop management system project report.pdf
retail automation billing system ppt.pptx
retail automation billing system ppt.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
RESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdf
RESORT MANAGEMENT AND RESERVATION SYSTEM PROJECT REPORT.pdf
Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...
Soil Testing Instruments by aimil ltd.- California Bearing Ratio apparatus, c...
Natalia Rutkowska - BIM School Course in Kraków
Natalia Rutkowska - BIM School Course in Kraków
School management system project report.pdf
School management system project report.pdf
KIT-601 Lecture Notes-UNIT-5.pdf Frame Works and Visualization
KIT-601 Lecture Notes-UNIT-5.pdf Frame Works and Visualization
Hall booking system project report .pdf
Hall booking system project report .pdf
Intro to Hadoop Presentation at Carnegie Mellon - Silicon Valley
1.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved Introduc=on to Apache Hadoop and its Ecosystem Mark Grover | Intro to Cloud Compu=ng, Carnegie Mellon SV github.com/markgrover/hadoop-‐intro-‐fast © Copyright 2010-‐2014 Cloudera, Inc. All rights reserved.
2.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved About Me • CommiNer on Apache Bigtop, commiNer and PPMC member on Apache Sentry (incuba=ng). • Contributor to Apache Hadoop, Hive, Spark, Sqoop, Flume. • SoUware developer at Cloudera • @mark_grover • www.linkedin.com/in/grovermark
3.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved Co-‐author O’Reilly book • @hadooparchbook • hadooparchitecturebook.com • To be released early 2015
4.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved About the Presenta=on… • What’s ahead • Fundamental Concepts • HDFS: The Hadoop Distributed File System • Data Processing with MapReduce • Demo • Conclusion + Q&A
5.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved Fundamental Concepts Why the World Needs Hadoop
6.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved What’s the craze about Hadoop? • Volume • More and more data being generated • Machine generated data increasing • Velocity • Data coming it at higher speed • Variety • Audio, video, images, log files, web pages, social network connec=ons, etc.
7.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved We Need a System that Scales • Too much data for tradi=onal tools • Two key problems • How to reliably store this data at a reasonable cost • How to we process all the data we’ve stored
8.
© 2010 –
2015 Cloudera, Inc. All Rights Reserved What is Apache Hadoop? • Scalable data storage and processing • Distributed and fault-‐tolerant • Runs on standard hardware • Two main components • Storage: Hadoop Distributed File System (HDFS) • Processing: MapReduce • Hadoop clusters are composed of computers called nodes • Clusters range from a single node up to several thousand nodes
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved How Did Apache Hadoop Originate? • Heavily influenced by Google’s architecture • Notably, the Google Filesystem and MapReduce papers • Other Web companies quickly saw the benefits • Early adop=on by Yahoo, Facebook and others 2002 2003 2004 2005 2006 Google publishes MapReduce paper Nutch rewritten for MapReduce Hadoop becomes Lucene subproject Nutch spun off from Lucene Google publishes GFS paper
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Comparing Hadoop to Other Systems • Monolithic systems don’t scale • Modern high-‐performance compu=ng systems are distributed • They spread computa=ons across many machines in parallel • Widely-‐used used for scien=fic applica=ons • Let’s examine how a typical HPC system works
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Architecture of a Typical HPC System Storage System Compute Nodes Fast Network
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Architecture of a Typical HPC System Storage System Compute Nodes Step 1: Copy input data Fast Network
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Architecture of a Typical HPC System Storage System Compute Nodes Step 2: Process the data Fast Network
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Architecture of a Typical HPC System Storage System Compute Nodes Step 3: Copy output data Fast Network
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved You Don’t Just Need Speed… • The problem is that we have way more data than code $ du -ks code/ 1,087 $ du –ks data/ 854,632,947,314
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved You Need Speed At Scale Storage System Compute Nodes Bottleneck
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Hadoop Design Fundamental: Data Locality • This is a hallmark of Hadoop’s design • Don’t bring the data to the computa=on • Bring the computa=on to the data • Hadoop uses the same machines for storage and processing • Significantly reduces need to transfer data across network
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Other Hadoop Design Fundamentals • Machine failure is unavoidable – embrace it • Build reliability into the system • “More” is usually beNer than “faster” • Throughput maNers more than latency
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved The Hadoop Distributed Filesystem HDFS
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved HDFS: Hadoop Distributed File System • Inspired by the Google File System • Reliable, low-‐cost storage for massive amounts of data • Similar to a UNIX filesystem in some ways • Hierarchical • UNIX-‐style paths (e.g., /sales/alice.txt) • UNIX-‐style file ownership and permissions
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved HDFS: Hadoop Distributed File System • There are also some major devia=ons from UNIX filesystems • Highly-‐op=mized for processing data with MapReduce • Designed for sequen=al access to large files • Cannot modify file content once wriNen • It’s actually a user-‐space Java process • Accessed using special commands or APIs • No concept of a current working directory
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Copying Local Data To and From HDFS • Remember that HDFS is dis=nct from your local filesystem • hadoop fs –put copies local files to HDFS • hadoop fs –get fetches a local copy of a file from HDFS $ hadoop fs -put sales.txt /reports Hadoop Cluster Client Machine $ hadoop fs -get /reports/sales.txt
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved HDFS Demo • I will now demonstrate the following 1. How to list the contents of a directory 2. How to create a directory in HDFS 3. How to copy a local file to HDFS 4. How to display the contents of a file in HDFS 5. How to remove a file from HDFS
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved A Scalable Data Processing Framework Data Processing with MapReduce
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved What is MapReduce? • MapReduce is a programming model • It’s a way of processing data • You can implement MapReduce in any language
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Understanding Map and Reduce • You supply two func=ons to process data: Map and Reduce • Map: typically used to transform, parse, or filter data • Reduce: typically used to summarize results • The Map func=on always runs first • The Reduce func=on runs aUerwards, but is op=onal • Each piece is simple, but can be powerful when combined
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved MapReduce Benefits • Scalability • Hadoop divides the processing job into individual tasks • Tasks execute in parallel (independently) across cluster • Simplicity • Processes one record at a =me • Ease of use • Hadoop provides job scheduling and other infrastructure • Far simpler for developers than typical distributed compu=ng
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved MapReduce in Hadoop • MapReduce processing in Hadoop is batch-‐oriented • A MapReduce job is broken down into smaller tasks • Tasks run concurrently • Each processes a small amount of overall input • MapReduce code for Hadoop is usually wriNen in Java • This uses Hadoop’s API directly • You can do basic MapReduce in other languages • Using the Hadoop Streaming wrapper program • Some advanced features require Java code
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved MapReduce Example in Python • The following example uses Python • Via Hadoop Streaming • It processes log files and summarizes events by type • I’ll explain both the data flow and the code
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Job Input • Here’s the job input • Each map task gets a chunk of this data to process • Typically corresponds to a single block in HDFS 2013-06-29 22:16:49.391 CDT INFO "This can wait" 2013-06-29 22:16:52.143 CDT INFO "Blah blah blah" 2013-06-29 22:16:54.276 CDT WARN "This seems bad" 2013-06-29 22:16:57.471 CDT INFO "More blather" 2013-06-29 22:17:01.290 CDT WARN "Not looking good" 2013-06-29 22:17:03.812 CDT INFO "Fairly unimportant" 2013-06-29 22:17:05.362 CDT ERROR "Out of memory!"
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved #!/usr/bin/env python import sys levels = ['TRACE', 'DEBUG', 'INFO', 'WARN', 'ERROR', 'FATAL'] for line in sys.stdin: fields = line.split() level = fields[3].upper() if level in levels: print "%st1" % level 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Python Code for Map Func=on If it matches a known level, print it, a tab separator, and the literal value 1 (since the level can only occur once per line) Read records from standard input. Use whitespace to split into fields. Define list of known log levels Extract “level” field and convert to uppercase for consistency.
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Output of Map Func=on • The map func=on produces key/value pairs as output INFO 1 INFO 1 WARN 1 INFO 1 WARN 1 INFO 1 ERROR 1
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved The “Shuffle and Sort” • Hadoop automa9cally merges, sorts, and groups map output • The result is passed as input to the reduce func=on • More on this later… INFO 1 INFO 1 WARN 1 INFO 1 WARN 1 INFO 1 ERROR 1 ERROR 1 INFO 1 INFO 1 INFO 1 INFO 1 WARN 1 WARN 1 Shuffle and Sort Map Output Reduce Input
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Input to Reduce Func=on • Reduce func=on receives a key and all values for that key • Keys are always passed to reducers in sorted order • Although not obvious here, values are unordered ERROR 1 INFO 1 INFO 1 INFO 1 INFO 1 WARN 1 WARN 1
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Python Code for Reduce Func=on #!/usr/bin/env python import sys previous_key = None sum = 0 for line in sys.stdin: key, value = line.split() if key == previous_key: sum = sum + int(value) # continued on next slide 1 2 3 4 5 6 7 8 9 10 11 12 13 Ini=alize loop variables Extract the key and value passed via standard input If key unchanged, increment the count
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Python Code for Reduce Func=on # continued from previous slide else: if previous_key: print '%st%i' % (previous_key, sum) previous_key = key sum = 1 print '%st%i' % (previous_key, sum) 14 15 16 17 18 19 20 21 22 Print data for the final key If key changed, print data for old level Start tracking data for the new record
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Output of Reduce Func=on • Its output is a sum for each level ERROR 1 INFO 4 WARN 2
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Recap of Data Flow ERROR 1 INFO 4 WARN 2 INFO 1 INFO 1 WARN 1 INFO 1 WARN 1 INFO 1 ERROR 1 ERROR 1 INFO 1 INFO 1 INFO 1 INFO 1 WARN 1 WARN 1 Map input Map output Reduce input Reduce output 2013-06-29 22:16:49.391 CDT INFO "This can wait" 2013-06-29 22:16:52.143 CDT INFO "Blah blah blah" 2013-06-29 22:16:54.276 CDT WARN "This seems bad" 2013-06-29 22:16:57.471 CDT INFO "More blather" 2013-06-29 22:17:01.290 CDT WARN "Not looking good" 2013-06-29 22:17:03.812 CDT INFO "Fairly unimportant" 2013-06-29 22:17:05.362 CDT ERROR "Out of memory!" Shuffle and sort
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved How to Run a Hadoop Streaming Job • I’ll demonstrate this now…
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Open Source Tools that Complement Hadoop The Hadoop Ecosystem
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved The Hadoop Ecosystem • "Core Hadoop" consists of HDFS and MapReduce • These are the kernel of a much broader plauorm • Hadoop has many related projects • Some help you integrate Hadoop with other systems • Others help you analyze your data • These are not considered “core Hadoop” • Rather, they’re part of the Hadoop ecosystem • Many are also open source Apache projects
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Visual Overview of a Complete Workflow Import Transaction Data from RDBMSSessionize Web Log Data with Pig Analyst uses Impala for business intelligence Sentiment Analysis on Social Media with Hive Hadoop Cluster with Impala Generate Nightly Reports using Pig, Hive, or Impala Build product recommendations for Web site
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Key Points • We’re genera=ng massive volumes of data • This data can be extremely valuable • Companies can now analyze what they previously discarded • Hadoop supports large-‐scale data storage and processing • Heavily influenced by Google's architecture • Already in produc=on by thousands of organiza=ons • HDFS is Hadoop's storage layer • MapReduce is Hadoop's processing framework • Many ecosystem projects complement Hadoop • Some help you to integrate Hadoop with exis=ng systems • Others help you analyze the data you’ve stored
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Highly Recommended Books Author: Tom White ISBN: 1-‐449-‐31152-‐0 Author: Eric Sammer ISBN: 1-‐449-‐32705-‐2
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© 2010 –
2015 Cloudera, Inc. All Rights Reserved Ques=ons? • Thank you for aNending! • I’ll be happy to answer any addi=onal ques=ons now… • Demo and slides at github.com/markgrover/hadoop-‐intro-‐fast • TwiNer: mark_grover • Survey page: =ny.cloudera.com/mark
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