Introduction to Hadoop.
What are Hadoop, MapReeduce, and Hadoop Distributed File System.
Who uses Hadoop?
How to run Hadoop?
What are Pig, Hive, Mahout?
Hadoop Training, Enhance your Big data subject knowledge with Online Training without wasting your time. Register for Free LIVE DEMO Class.
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• What is MapReduce?
• What are MapReduce implementations?
Facing these questions I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
Introduction to Hadoop.
What are Hadoop, MapReeduce, and Hadoop Distributed File System.
Who uses Hadoop?
How to run Hadoop?
What are Pig, Hive, Mahout?
Hadoop Training, Enhance your Big data subject knowledge with Online Training without wasting your time. Register for Free LIVE DEMO Class.
For more info: http://www.hadooponlinetutor.com
Contact Us:
8121660044
732-419-2619
http://www.hadooponlinetutor.com
• What is MapReduce?
• What are MapReduce implementations?
Facing these questions I have make a personal research, and realize a synthesis, which has help me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
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.
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
A guide to using Apache Hadoop as your open source big data platform of choice, including the vendors that make various Hadoop flavors, related open source tools, Hadoop capabilities and suitable applications.
Similar to Hadoop foundation for analytics,B Monica II M.sc computer science ,BON SECOURS COLLEGE FOR WOMEN (20)
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting primitives for graph : SHORT REPORT / NOTES
Hadoop foundation for analytics,B Monica II M.sc computer science ,BON SECOURS COLLEGE FOR WOMEN
1. HADOOP FOUNDATION FOR ANALYTICS
BY
Name : B.MONICA
Class :II M.SC COMPUTER SCIENCE
Batch : 2017 -2019
Incharge Staff : Ms. M. Florence Dayana
1
2. HADOOP
It is an open-source software framework
licensed under the Apache v2 license
It includes:
– Map Reduce : offline computing engine
– HDFS : Hadoop distributed file system
EXAMPLE
2
3. HADOOP GOALS
Scalable: It can reliably store and process petabytes.
Economical: It distributes the data
Efficient: it can process it in parallel on the nodes where the
data is located.
Reliable: It automatically maintains multiple copies of data
3
4. USES FOR HADOOP
Data-intensive text processing
Assembly of large genomes
Graph mining
Machine learning and data mining
Large scale social network analysis
4
5. HADOOP: ASSUMPTIONS
Hardware will fail.
Applications need a write-once-read-many access model.
EXAMPLE
Facebook:
- To store copies of internal log and dimension
data sources
- it as a source for reporting/analytics and
machine learning
- 320 machine cluster with 2,560 cores and
about 1.3 PB raw storage 5
7. HISTORY OF HADOOP
Hadoop was started by Doug Cutting to support
two of his other well known projects, Lucene and
Nutch
Hadoop has been inspired by Google's File
System (GFS) which was detailed in a paper by
released by Google in 2003
Hadoop, originally called Nutch Distributed File
System (NDFS) split from Nutch in 2006 to
become a sub-project of Lucene. At this point it
was renamed to Hadoop.
7
8. EXAMPLE
Google search engine
2013 - Hadoop 1.1.2 and Hadoop 2.0.3 alpha.
- Ambari , Cassandra, Mahout have been
added
8
9. • Hadoop is in use at most organizations that
handle big data:
o Yahoo!
o Facebook
o Amazon
o Netflix
9
10. APACHE MAP REDUCE
A software framework for distributed processing of
large data sets
The framework takes care of scheduling tasks,
monitoring them and re-executing any failed tasks.
It splits the input data set into independent chunks.
Map Reduce framework sorts the outputs of the maps,
which are then input to the reduce tasks..
10
12. MAP REDUCE DATAFLOW
An input reader
A Map function
A partition function
A compare function
A Reduce function
An output writer
EXAMPLE:
JOB TRACKER
TASK TRACKER 12
13. MAP REDUCE-FAULT TOLERANCE
Worker failure: The master pings every worker
periodically.
Master Failure: It is easy to make the master write
periodic checkpoints of the master data structures
13
14. JOB TRACKER
Tracking Map Reduce jobs in Hadoop
Job Tracker performs following actions in Hadoop
It accepts the Map Reduce Jobs from client
applications
Talks to Name Node to determine data location
Locates available Task Tracker Node
Submits the work to the chosen Task Tracker
Node
14
15. OTHER TOOLS
Hive
Hadoop processing with SQL
Pig
Hadoop processing with scripting
Cascading
Pipe and Filter processing model
H Base
Database model built on top of Hadoop
Flume
Designed for large scale data movement
15
