Submit Search
Upload
Mapreduce in Search
•
26 likes
•
11,933 views
Amund Tveit
Follow
Presentation for Information Retrieval students about mapreduce and search.
Read less
Read more
Technology
Report
Share
Report
Share
1 of 37
Recommended
DataDay Presentation in June 13, 2022 for Outrageous Ideas for Graph Databases
Outrageous Ideas for Graph Databases
Outrageous Ideas for Graph Databases
Max De Marzi
Terminology, Graph Representation, Traversal, Spanning Tree, Shortest Path & Transitive Clousure
Unit 3 graph chapter6
Unit 3 graph chapter6
DrkhanchanaR
This is an PPT of Data Structure. It include the following topic "Binary Search Tree in Data Structure ".
Binary Search Tree in Data Structure
Binary Search Tree in Data Structure
Meghaj Mallick
Big Data in Real-Time at Twitter
Big Data in Real-Time at Twitter
nkallen
by Wes McKinney (@wesmckinn) at PyData NYC 2013
Practical Medium Data Analytics with Python (10 Things I Hate About pandas, P...
Practical Medium Data Analytics with Python (10 Things I Hate About pandas, P...
Wes McKinney
Binary search works on sorted arrays. Binary search begins by comparing an element in the middle of the array with the target value. If the target value matches the element, its position in the array is returned. If the target value is less than the element, the search continues in the lower half of the array.
Binary search tree operations
Binary search tree operations
Kamran Zafar
getting started exercises direct mapping fully associative mapping set associative mapping
Cache mapping exercises
Cache mapping exercises
sawsan slii
Linked Lists
Operations on linked list
Operations on linked list
Sumathi Kv
Recommended
DataDay Presentation in June 13, 2022 for Outrageous Ideas for Graph Databases
Outrageous Ideas for Graph Databases
Outrageous Ideas for Graph Databases
Max De Marzi
Terminology, Graph Representation, Traversal, Spanning Tree, Shortest Path & Transitive Clousure
Unit 3 graph chapter6
Unit 3 graph chapter6
DrkhanchanaR
This is an PPT of Data Structure. It include the following topic "Binary Search Tree in Data Structure ".
Binary Search Tree in Data Structure
Binary Search Tree in Data Structure
Meghaj Mallick
Big Data in Real-Time at Twitter
Big Data in Real-Time at Twitter
nkallen
by Wes McKinney (@wesmckinn) at PyData NYC 2013
Practical Medium Data Analytics with Python (10 Things I Hate About pandas, P...
Practical Medium Data Analytics with Python (10 Things I Hate About pandas, P...
Wes McKinney
Binary search works on sorted arrays. Binary search begins by comparing an element in the middle of the array with the target value. If the target value matches the element, its position in the array is returned. If the target value is less than the element, the search continues in the lower half of the array.
Binary search tree operations
Binary search tree operations
Kamran Zafar
getting started exercises direct mapping fully associative mapping set associative mapping
Cache mapping exercises
Cache mapping exercises
sawsan slii
Linked Lists
Operations on linked list
Operations on linked list
Sumathi Kv
Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
Chester Chen
B trees dbms
B trees dbms
kuldeep100
A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set.
Bloom filter
Bloom filter
Hamid Feizabadi
Stack, Queue
Unit I-Data structures stack & Queue
Unit I-Data structures stack & Queue
DrkhanchanaR
Algorithm and data structures topic is Splay Tree. A full comparison between splay tree and AVL tree and learn to create splay tree with expamples
Splay tree
Splay tree
hina firdaus
2 3 tree
2 3 tree
Anju Kanjirathingal
My lectures circular queue
My lectures circular queue
Senthil Kumar
Learn about the latest developments in the open-source community with Apache Spark 3.0 and DBR 7.0The upcoming Apache Spark™ 3.0 release brings new capabilities and features to the Spark ecosystem. In this online tech talk from Databricks, we will walk through updates in the Apache Spark 3.0.0-preview2 release as part of our new Databricks Runtime 7.0 Beta, which is now available.
What’s New in the Upcoming Apache Spark 3.0
What’s New in the Upcoming Apache Spark 3.0
Databricks
These lecture slides describe the conceptual foundations of text mining and preprocessing steps.
Conceptual foundations of text mining and preprocessing steps nfaoui el_habib
Conceptual foundations of text mining and preprocessing steps nfaoui el_habib
El Habib NFAOUI
Talk on Apache Kudu, presented by Asim Jalis at SF Data Engineering Meetup on 2/23/2016. http://www.meetup.com/SF-Data-Engineering/events/228293610/ Big Data applications need to ingest streaming data and analyze it. HBase is great at ingesting streaming data but not good at analytics. HDFS is great at analytics but not at ingesting streaming data. Frequently applications ingest data into HBase and then move it to HDFS for analytics. What if you could use a single system for both use cases? What if you could use a single system for both use cases? This could dramatically simplify your data pipeline architecture. This is where Kudu comes in. Kudu is a storage system that lives between HDFS and HBase. It is good at both ingesting streaming data and good at analyzing it using Spark, MapReduce, and SQL.
Apache kudu
Apache kudu
Asim Jalis
Data Mining Techniques Hans & Kamber
Chapter 07
Chapter 07
Houw Liong The
From: DataWorks Summit 2017 - Munich - 20170406 HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.
HBase in Practice
HBase in Practice
larsgeorge
introduction to binary tree , binary search tree, operations of binary search tree, insertion, deletions, search. 3 cases of deletions, graph tree of binary search tree, complaxcity, notations,algorithms, Data Analysis
Binary Search Tree (BST)
Binary Search Tree (BST)
M Sajid R
Selection sort
Selection sort
smlagustin
Column oriented database
Column oriented database
Kanike Krishna
2017/06/04 ssmjp Special
Can We Prevent Use-after-free Attacks?
Can We Prevent Use-after-free Attacks?
inaz2
Singly LInked List Doubly Linked List
linked list
linked list
Mohaimin Rahat
These slides are part of a full series of slides which covers almost all the basic concepts of data structures and algorithms. Part 12
AVL Tree Data Structure
AVL Tree Data Structure
Afaq Mansoor Khan
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
Binary Search Tree
Binary Search Tree
Abhishek L.R
Course: MCA Subject: Computer Oriented Numerical Statistical Methods Unit-1 Solution of Non-linear Equations RAI UNIVERSITY, AHMEDABAD
MCA_UNIT-1_Computer Oriented Numerical Statistical Methods
MCA_UNIT-1_Computer Oriented Numerical Statistical Methods
Rai University
This are the slides I used to introduce Hadoop in a meetup at the Barcelona JUG (Java Users Group).
Distributed batch processing with Hadoop
Distributed batch processing with Hadoop
Ferran Galí Reniu
Presentation held at O'Reilly Strata Conference in London, UK October 1st 2012
Mapreduce Algorithms
Mapreduce Algorithms
Amund Tveit
More Related Content
What's hot
Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
Chester Chen
B trees dbms
B trees dbms
kuldeep100
A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set.
Bloom filter
Bloom filter
Hamid Feizabadi
Stack, Queue
Unit I-Data structures stack & Queue
Unit I-Data structures stack & Queue
DrkhanchanaR
Algorithm and data structures topic is Splay Tree. A full comparison between splay tree and AVL tree and learn to create splay tree with expamples
Splay tree
Splay tree
hina firdaus
2 3 tree
2 3 tree
Anju Kanjirathingal
My lectures circular queue
My lectures circular queue
Senthil Kumar
Learn about the latest developments in the open-source community with Apache Spark 3.0 and DBR 7.0The upcoming Apache Spark™ 3.0 release brings new capabilities and features to the Spark ecosystem. In this online tech talk from Databricks, we will walk through updates in the Apache Spark 3.0.0-preview2 release as part of our new Databricks Runtime 7.0 Beta, which is now available.
What’s New in the Upcoming Apache Spark 3.0
What’s New in the Upcoming Apache Spark 3.0
Databricks
These lecture slides describe the conceptual foundations of text mining and preprocessing steps.
Conceptual foundations of text mining and preprocessing steps nfaoui el_habib
Conceptual foundations of text mining and preprocessing steps nfaoui el_habib
El Habib NFAOUI
Talk on Apache Kudu, presented by Asim Jalis at SF Data Engineering Meetup on 2/23/2016. http://www.meetup.com/SF-Data-Engineering/events/228293610/ Big Data applications need to ingest streaming data and analyze it. HBase is great at ingesting streaming data but not good at analytics. HDFS is great at analytics but not at ingesting streaming data. Frequently applications ingest data into HBase and then move it to HDFS for analytics. What if you could use a single system for both use cases? What if you could use a single system for both use cases? This could dramatically simplify your data pipeline architecture. This is where Kudu comes in. Kudu is a storage system that lives between HDFS and HBase. It is good at both ingesting streaming data and good at analyzing it using Spark, MapReduce, and SQL.
Apache kudu
Apache kudu
Asim Jalis
Data Mining Techniques Hans & Kamber
Chapter 07
Chapter 07
Houw Liong The
From: DataWorks Summit 2017 - Munich - 20170406 HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.
HBase in Practice
HBase in Practice
larsgeorge
introduction to binary tree , binary search tree, operations of binary search tree, insertion, deletions, search. 3 cases of deletions, graph tree of binary search tree, complaxcity, notations,algorithms, Data Analysis
Binary Search Tree (BST)
Binary Search Tree (BST)
M Sajid R
Selection sort
Selection sort
smlagustin
Column oriented database
Column oriented database
Kanike Krishna
2017/06/04 ssmjp Special
Can We Prevent Use-after-free Attacks?
Can We Prevent Use-after-free Attacks?
inaz2
Singly LInked List Doubly Linked List
linked list
linked list
Mohaimin Rahat
These slides are part of a full series of slides which covers almost all the basic concepts of data structures and algorithms. Part 12
AVL Tree Data Structure
AVL Tree Data Structure
Afaq Mansoor Khan
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
Binary Search Tree
Binary Search Tree
Abhishek L.R
Course: MCA Subject: Computer Oriented Numerical Statistical Methods Unit-1 Solution of Non-linear Equations RAI UNIVERSITY, AHMEDABAD
MCA_UNIT-1_Computer Oriented Numerical Statistical Methods
MCA_UNIT-1_Computer Oriented Numerical Statistical Methods
Rai University
What's hot
(20)
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
B trees dbms
B trees dbms
Bloom filter
Bloom filter
Unit I-Data structures stack & Queue
Unit I-Data structures stack & Queue
Splay tree
Splay tree
2 3 tree
2 3 tree
My lectures circular queue
My lectures circular queue
What’s New in the Upcoming Apache Spark 3.0
What’s New in the Upcoming Apache Spark 3.0
Conceptual foundations of text mining and preprocessing steps nfaoui el_habib
Conceptual foundations of text mining and preprocessing steps nfaoui el_habib
Apache kudu
Apache kudu
Chapter 07
Chapter 07
HBase in Practice
HBase in Practice
Binary Search Tree (BST)
Binary Search Tree (BST)
Selection sort
Selection sort
Column oriented database
Column oriented database
Can We Prevent Use-after-free Attacks?
Can We Prevent Use-after-free Attacks?
linked list
linked list
AVL Tree Data Structure
AVL Tree Data Structure
Binary Search Tree
Binary Search Tree
MCA_UNIT-1_Computer Oriented Numerical Statistical Methods
MCA_UNIT-1_Computer Oriented Numerical Statistical Methods
Viewers also liked
This are the slides I used to introduce Hadoop in a meetup at the Barcelona JUG (Java Users Group).
Distributed batch processing with Hadoop
Distributed batch processing with Hadoop
Ferran Galí Reniu
Presentation held at O'Reilly Strata Conference in London, UK October 1st 2012
Mapreduce Algorithms
Mapreduce Algorithms
Amund Tveit
Collaborative Filtering in Map/Reduce
Collaborative Filtering in Map/Reduce
Ole-Martin Mørk
deck from my 5 part series of YouTube (SoCalDevGal channel) on Hadoop MapReduce
Hadoop MapReduce Fundamentals
Hadoop MapReduce Fundamentals
Lynn Langit
1) Total order sorting is another kind of sorting technique, where map output keys are sorted across all the reducers. 2) This technique uses, where you want to extract the most popular URLs from a web graph. 1) By default Mapreduce uses HashPartitioner as its Partitioner class, which partitions using a hash of the map output keys. 2) Also HashPartitioner ensures that all records with the same map output key goes to the same reducer, but it doesn’t perform total sorting of the map output keys across all the reducers. 3) For this reason only TotalOrderPartitioner class is introduced, which is by default packed with the Hadoop distribution. 1) If you want to work with Total order sorting, we need to create Partition file, and then we have to run Mapreduce job using TotalOrderPartitioner class. 2) We will create partition file, by using InputSampler class, which is used to do sampling of the whole dataset. 3) There are basically two kinds of samplers that we mostly use. 4) First one is RandomSampler, which is mainly used to pick random samples from the original dataset. And the second one is, IntervalSampler, which is mainly used to pick the sample for every R number of records. In the practical demonstration I have used RandomSampler class to pick the samples from Original dataset. 5) Once all the meaningful samples are extracted from the dataset, it will sort those keys, and pick N-1 keys from those sorted keys where N is number of reducers and it places in a Partition file which is used for Total order sorting. 1) This is an overview of Total Order Sorting, here it show how it generates the Partition file and also it shows how the Mapreduce job uses this Partition file during Total Order Sorting. 1) This is a code Sample for Total Order Sorting, in this we have specified the sampler object as RandomSample class. And we also set the Number of reducers using setNumReduceTasks(). And also we specified the Partionfile location unsing setPartionfile() of TotalOrderPartitioner class. And at last we have used writePartitionFile() of InputSampler class for creating Partition file.
Mapreduce total order sorting technique
Mapreduce total order sorting technique
Uday Vakalapudi
Introduction to Big Data and Apache Hadoop project. MapReduce vizualization
Intro to Big Data using Hadoop
Intro to Big Data using Hadoop
Sergejus Barinovas
Oversikt over Deep Learning på norsk An Overview of Deep Learning in Norwegian
201411 memkitedeeplearning
201411 memkitedeeplearning
Amund Tveit
Presentation from NxtMedia Conference 2014. http://nxtmediaconference.no/
Deep Learning
Deep Learning
NxtMedia Conference
Raymie Stata, ex-CTO of Yahoo, talks about YARN, Hadoop's new Resource Manager, and other improvements in Hadoop 2.0.
YARN - Hadoop's Resource Manager
YARN - Hadoop's Resource Manager
VertiCloud Inc
Slides of the talk "Gradient Boosted Regression Trees in scikit-learn" by Peter Prettenhofer and Gilles Louppe held at PyData London 2014. Abstract: This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling. GBRT is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price. I will give a brief introduction to the GBRT model and regression trees -- focusing on intuition rather than mathematical formulas. The majority of the talk will be dedicated to an in depth discussion how to apply GBRT in practice using scikit-learn. We will cover important topics such as regularization, model tuning and model interpretation that should significantly improve your score on Kaggle.
Gradient Boosted Regression Trees in scikit-learn
Gradient Boosted Regression Trees in scikit-learn
DataRobot
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithms
nextlib
Recommender Systems play a crucial role in a variety of businesses in today`s world. From E-Commerce web sites to News Portals, companies are leveraging data about their users to create a personalizes user experience, gain competitive advantage and eventually drive revenue. Dealing with the sheer quantity of data readily available can be a daunting task by itself. Consider applying machine learning algorithms on top of it and it makes the problem exponentially complex. Fortunately, tools like Hadoop and HBase make this task a little more manageable by taking out some of the complexities of dealing with a large amount of data. In this talk, we will share our success story of building a recommender system for Bloomberg.com leveraging the Hadoop ecosystem. We will describe the high level architecture of the system and discuss the pros and cons of our design choices. Bloomberg.com operates at a scale of 100s of millions of users. Building a recommendation engine for Bloomberg.com entails applying Machine Learning algorithms on terabytes of data and still being able to serve sub-second responses. We will discuss techniques for efficiently and reliably collecting data in near real-time, the notion of offline vs. online processing and most importantly, how HBase perfectly fits the bill by serving as a real-time database as well as input/output for running MapReduce.
Recommender System at Scale Using HBase and Hadoop
Recommender System at Scale Using HBase and Hadoop
DataWorks Summit
Guidelines for Development and Implementation of Massive Open Online Courses (MOOCs)
Guidelines for Swayam: India's MOOC Platform
Guidelines for Swayam: India's MOOC Platform
Class Central
The Google MapReduce presented in 2004 is the inspiration for Hadoop. Let's take a deep dive into MapReduce to better understand Hadoop.
The google MapReduce
The google MapReduce
Romain Jacotin
人工知能研究のための視覚情報処理
人工知能研究のための視覚情報処理
人工知能研究のための視覚情報処理
Koki Nakamura
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ ) Map Reduce: Beyond Word Count by Jeff Patti Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
Map reduce: beyond word count
Map reduce: beyond word count
Jeff Patti
How Google Does Big Data - DevNexus 2014
How Google Does Big Data - DevNexus 2014
James Chittenden
Hadoop Summit 2015
Surge: Rise of Scalable Machine Learning at Yahoo!
Surge: Rise of Scalable Machine Learning at Yahoo!
DataWorks Summit
An Introduction to MapReduce
An Introduction to MapReduce
Frane Bandov
Introducing Apache Giraph for Large Scale Graph Processing
Introducing Apache Giraph for Large Scale Graph Processing
sscdotopen
Viewers also liked
(20)
Distributed batch processing with Hadoop
Distributed batch processing with Hadoop
Mapreduce Algorithms
Mapreduce Algorithms
Collaborative Filtering in Map/Reduce
Collaborative Filtering in Map/Reduce
Hadoop MapReduce Fundamentals
Hadoop MapReduce Fundamentals
Mapreduce total order sorting technique
Mapreduce total order sorting technique
Intro to Big Data using Hadoop
Intro to Big Data using Hadoop
201411 memkitedeeplearning
201411 memkitedeeplearning
Deep Learning
Deep Learning
YARN - Hadoop's Resource Manager
YARN - Hadoop's Resource Manager
Gradient Boosted Regression Trees in scikit-learn
Gradient Boosted Regression Trees in scikit-learn
Item Based Collaborative Filtering Recommendation Algorithms
Item Based Collaborative Filtering Recommendation Algorithms
Recommender System at Scale Using HBase and Hadoop
Recommender System at Scale Using HBase and Hadoop
Guidelines for Swayam: India's MOOC Platform
Guidelines for Swayam: India's MOOC Platform
The google MapReduce
The google MapReduce
人工知能研究のための視覚情報処理
人工知能研究のための視覚情報処理
Map reduce: beyond word count
Map reduce: beyond word count
How Google Does Big Data - DevNexus 2014
How Google Does Big Data - DevNexus 2014
Surge: Rise of Scalable Machine Learning at Yahoo!
Surge: Rise of Scalable Machine Learning at Yahoo!
An Introduction to MapReduce
An Introduction to MapReduce
Introducing Apache Giraph for Large Scale Graph Processing
Introducing Apache Giraph for Large Scale Graph Processing
Similar to Mapreduce in Search
Silicon Valley Cloud Computing Meetup Mountain View, 2010-07-19 Examples of Hadoop Streaming, based on Python scripts running on the AWS Elastic MapReduce service, which show text mining on the "Enron Email Dataset" from Infochimps.com plus data visualization using R and Gephi Source at: http://github.com/ceteri/ceteri-mapred
Getting Started on Hadoop
Getting Started on Hadoop
Paco Nathan
An evening with Hadoop in London
Hadoop london
Hadoop london
Yahoo Developer Network
Anatomy of Google cluster & MapReduce programming ...
Google Cluster Innards
Google Cluster Innards
Martin Dvorak
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics. This slide covers the Advance Map reduce concepts of Hadoop and Big Data. For training queries you can contact us: Email: admin@designpathshala.com Call us at: +91 98 188 23045 Visit us at: http://designpathshala.com Join us at: http://www.designpathshala.com/contact-us Course details: http://www.designpathshala.com/course/view/65536 Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792 Business Analytics Course details: http://www.designpathshala.com/course/view/196608
Advance Map reduce - Apache hadoop Bigdata training by Design Pathshala
Advance Map reduce - Apache hadoop Bigdata training by Design Pathshala
Desing Pathshala
for Dr.Salem Othman Big Data
Lecture 2 part 3
Lecture 2 part 3
Jazan University
At the Dublin Fashion Insights Centre, we are exploring methods of categorising the web into a set of known fashion related topics. This raises questions such as: How many fashion related topics are there? How closely are they related to each other, or to other non-fashion topics? Furthermore, what topic hierarchies exist in this landscape? Using Clojure and MLlib to harness the data available from crowd-sourced websites such as DMOZ (a categorisation of millions of websites) and Common Crawl (a monthly crawl of billions of websites), we are answering these questions to understand fashion in a quantitative manner. The latest generation of big data tools such as Apache Spark routinely handle petabytes of data while also addressing real-world realities like node and network failures. Spark's transformations and operations on data sets are a natural fit with Clojure's everyday use of transformations and reductions. Spark MLlib's excellent implementations of distributed machine learning algorithms puts the power of large-scale analytics in the hands of Clojure developers. At Zalando's Dublin Fashion Insights Centre, we're using the Clojure bindings to Spark and MLlib to answer fashion-related questions that until recently have been nearly impossible to answer quantitatively. Hunter Kelly @retnuh tech.zalando.com
Spark + Clojure for Topic Discovery - Zalando Tech Clojure/Conj Talk
Spark + Clojure for Topic Discovery - Zalando Tech Clojure/Conj Talk
Zalando Technology
CSI conference PPT on Performance Analysis of Map/Reduce to compute the frequ...
CSI conference PPT on Performance Analysis of Map/Reduce to compute the frequ...
shravanthium111
Behm Shah Pagerank
Behm Shah Pagerank
gothicane
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Hadoop trainingin bangalore
Hadoop trainingin bangalore
appaji intelhunt
A presentation from TheEdge10 about Hadoop and Big data
TheEdge10 : Big Data is Here - Hadoop to the Rescue
TheEdge10 : Big Data is Here - Hadoop to the Rescue
Shay Sofer
creating new stats algorithms easily in R
Easy R
Easy R
Ajay Ohri
Slide deck from multiple talks on "Getting Started with Hadoop"
Getting Started with Hadoop
Getting Started with Hadoop
Josh Devins
Web Data Extraction Como2010
Web Data Extraction Como2010
Giorgio Orsi
Introductory presentation on Apache Hadoop and Apache Hive.
Hadoop
Hadoop
Scott Leberknight
Hive is an open source data warehouse systems based on Hadoop, a MapReduce implementation. This presentation introduces the motivations of developing Hive and how Hive is used in the real world situation, particularly in Facebook.
Hive Training -- Motivations and Real World Use Cases
Hive Training -- Motivations and Real World Use Cases
nzhang
This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
The Fundamentals Guide to HDP and HDInsight
The Fundamentals Guide to HDP and HDInsight
Gert Drapers
This is part of an introductory course to Big Data Tools for Artificial Intelligence. These slides introduce students to Apache Hadoop, DFS, and Map Reduce.
Apache Hadoop: DFS and Map Reduce
Apache Hadoop: DFS and Map Reduce
Victor Sanchez Anguix
Paco Nathan, Director of Community Evangelism at Databricks Apache Spark is intended as a fast and powerful general purpose engine for processing Hadoop data. Spark supports combinations of batch processing, streaming, SQL, ML, Graph, etc., for applications written in Scala, Java, Python, Clojure, and R, among others. In this talk, I'll explore how Spark fits into the Big Data landscape. In addition, I'll describe other systems with which Spark pairs nicely, and will also explain why Spark is needed for the work ahead.
Big Data Everywhere Chicago: Apache Spark Plus Many Other Frameworks -- How S...
Big Data Everywhere Chicago: Apache Spark Plus Many Other Frameworks -- How S...
BigDataEverywhere
Map Reduce
Map Reduce
Rahul Agarwal
Elasticsearch sharing Some code at https://github.com/yychen/estest
Elasticsearch intro output
Elasticsearch intro output
Tom Chen
Similar to Mapreduce in Search
(20)
Getting Started on Hadoop
Getting Started on Hadoop
Hadoop london
Hadoop london
Google Cluster Innards
Google Cluster Innards
Advance Map reduce - Apache hadoop Bigdata training by Design Pathshala
Advance Map reduce - Apache hadoop Bigdata training by Design Pathshala
Lecture 2 part 3
Lecture 2 part 3
Spark + Clojure for Topic Discovery - Zalando Tech Clojure/Conj Talk
Spark + Clojure for Topic Discovery - Zalando Tech Clojure/Conj Talk
CSI conference PPT on Performance Analysis of Map/Reduce to compute the frequ...
CSI conference PPT on Performance Analysis of Map/Reduce to compute the frequ...
Behm Shah Pagerank
Behm Shah Pagerank
Hadoop trainingin bangalore
Hadoop trainingin bangalore
TheEdge10 : Big Data is Here - Hadoop to the Rescue
TheEdge10 : Big Data is Here - Hadoop to the Rescue
Easy R
Easy R
Getting Started with Hadoop
Getting Started with Hadoop
Web Data Extraction Como2010
Web Data Extraction Como2010
Hadoop
Hadoop
Hive Training -- Motivations and Real World Use Cases
Hive Training -- Motivations and Real World Use Cases
The Fundamentals Guide to HDP and HDInsight
The Fundamentals Guide to HDP and HDInsight
Apache Hadoop: DFS and Map Reduce
Apache Hadoop: DFS and Map Reduce
Big Data Everywhere Chicago: Apache Spark Plus Many Other Frameworks -- How S...
Big Data Everywhere Chicago: Apache Spark Plus Many Other Frameworks -- How S...
Map Reduce
Map Reduce
Elasticsearch intro output
Elasticsearch intro output
Recently uploaded
Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
UK Journal
Details
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
Presented by Sergio Licea and John Hendershot
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
ThousandEyes
Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
apidays
MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Miguel Araújo
Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
Breathing New Life into MySQL Apps With Advanced Postgres Capabilities
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Drew Madelung
What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
Antenna Manufacturer Coco
These are the slides delivered in a workshop at Data Innovation Summit Stockholm April 2024, by Kristof Neys and Jonas El Reweny.
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
I've been in the field of "Cyber Security" in its many incarnations for about 25 years. In that time I've learned some lessons, some the hard way. Here are my slides presented at BSides New Orleans in April 2024.
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Rafal Los
Slides from the presentation on Machine Learning for the Arts & Humanities seminar at the University of Bologna (Digital Humanities and Digital Knowledge program)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Maria Levchenko
Read about the journey the Adobe Experience Manager team has gone through in order to become and scale API-first throughout the organisation.
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
Explore the leading Large Language Models (LLMs) and their capabilities with a comprehensive evaluation. Dive into their performance, architecture, and applications to gain insights into the state-of-the-art in natural language processing. Discover which LLM best suits your needs and stay ahead in the world of AI-driven language understanding.
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
ChristopherTHyatt
BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Delhi Call girls
How to get Oracle DBA Job as fresher.
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
Remote DBA Services
Recently uploaded
(20)
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
Mapreduce in Search
1.
Mapreduce and
(in) Search Amund Tveit (PhD) [email_address] http://atbrox.com
2.
3.
4.
Part 1 mapreduce
5.
6.
7.
8.
9.
10.
Part 2 mapreduce
in search
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
Part 3 advanced
mapreduce example
29.
30.
31.
32.
Adv. Mapreduce Example
IV
33.
34.
35.
36.
37.