Doug Cutting on the State of the Hadoop EcosystemCloudera, Inc.
Doug Cutting, Apache Hadoop Co-founder, explains how the growth of the Hadoop ecosystem has made Hadoop a much more powerful machine, and how the continued expansion will lead to great things.
Hi all, its presentation about the big data analysis done using a data mining tool known as HADOOP, which is based on Distributive file system and uses parallel computing for working.
Doug Cutting on the State of the Hadoop EcosystemCloudera, Inc.
Doug Cutting, Apache Hadoop Co-founder, explains how the growth of the Hadoop ecosystem has made Hadoop a much more powerful machine, and how the continued expansion will lead to great things.
Hi all, its presentation about the big data analysis done using a data mining tool known as HADOOP, which is based on Distributive file system and uses parallel computing for working.
About Streaming Data Solutions for HadoopLynn Langit
Whitepaper comparing capabilities for various streaming data solutions for Hadoop - includes Apache Storm, Apache Spark Streaming and commercial solutions, such as Data Torrent
Adding Location and Geospatial Analytics to Big Data Analytics (BDT210) | AWS...Amazon Web Services
(Presented by Esri)
When people analyze a problem, they often include location at the core of the analysis. Location and spatial context, combined with geographical knowledge, can make the biggest difference in understanding a problem and analyzing it in a more meaningful way.
In this session, we show how Amazon EMR can be used with location and geospatial analytics, and how the Amazon EMR API and the Python SDK were used to build tools that integrate Big Data and geospatial analysis. We also show powerful visualization options for displaying your results, using maps which can be shared in reports or distributed online and to mobile apps.
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
Developed by Doug Cutting, Mika Cafarella and Team
Open Source Project that works on MapReduce algorithm
Apache Hadoop is a registered trademark of Apache Software Foundation
Hourglass: a Library for Incremental Processing on HadoopMatthew Hayes
Slides from my talk at IEEE BigData 2013 presenting our paper "Hourglass: a Library for Incremental Processing on Hadoop"
Abstract:
Hadoop enables processing of large data sets through its relatively easy-to-use semantics. However, jobs are often written inefficiently for tasks that could be computed incrementally due to the burdensome incremental state management for the programmer. This paper introduces Hourglass, a library for developing incremental monoid computations on Hadoop. It runs on unmodified Hadoop and provides an accumulator-based interface for programmers to store and use state across successive runs; the framework ensures that only the necessary subcomputations are performed. It is successfully used at LinkedIn, one of the largest online social networks, for many use cases in dashboarding and machine learning. Hourglass is open source and freely available.
High Performance Computing and Big Data Geoffrey Fox
We propose a hybrid software stack with Large scale data systems for both research and commercial applications running on the commodity (Apache) Big Data Stack (ABDS) using High Performance Computing (HPC) enhancements typically to improve performance. We give several examples taken from bio and financial informatics.
We look in detail at parallel and distributed run-times including MPI from HPC and Apache Storm, Heron, Spark and Flink from ABDS stressing that one needs to distinguish the different needs of parallel (tightly coupled) and distributed (loosely coupled) systems.
We also study "Java Grande" or the principles to use to allow Java codes to perform as fast as those written in more traditional HPC languages. We also note the differences between capacity (individual jobs using many nodes) and capability (lots of independent jobs) computing.
We discuss how this HPC-ABDS concept allows one to discuss convergence of Big Data, Big Simulation, Cloud and HPC Systems. See http://hpc-abds.org/kaleidoscope/
A performance analysis of OpenStack Cloud vs Real System on Hadoop ClustersKumari Surabhi
It introduces the performance analysis of OpenStack Cloud with the commodity computers in the big data environments. It concludes that the data storage and analysis in hadoop cluster in cloud is more flexible and easily scalable than the real system cluster. It also concludes the cluster in commodities computers are faster than the cloud clusters.
About Streaming Data Solutions for HadoopLynn Langit
Whitepaper comparing capabilities for various streaming data solutions for Hadoop - includes Apache Storm, Apache Spark Streaming and commercial solutions, such as Data Torrent
Adding Location and Geospatial Analytics to Big Data Analytics (BDT210) | AWS...Amazon Web Services
(Presented by Esri)
When people analyze a problem, they often include location at the core of the analysis. Location and spatial context, combined with geographical knowledge, can make the biggest difference in understanding a problem and analyzing it in a more meaningful way.
In this session, we show how Amazon EMR can be used with location and geospatial analytics, and how the Amazon EMR API and the Python SDK were used to build tools that integrate Big Data and geospatial analysis. We also show powerful visualization options for displaying your results, using maps which can be shared in reports or distributed online and to mobile apps.
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
Developed by Doug Cutting, Mika Cafarella and Team
Open Source Project that works on MapReduce algorithm
Apache Hadoop is a registered trademark of Apache Software Foundation
Hourglass: a Library for Incremental Processing on HadoopMatthew Hayes
Slides from my talk at IEEE BigData 2013 presenting our paper "Hourglass: a Library for Incremental Processing on Hadoop"
Abstract:
Hadoop enables processing of large data sets through its relatively easy-to-use semantics. However, jobs are often written inefficiently for tasks that could be computed incrementally due to the burdensome incremental state management for the programmer. This paper introduces Hourglass, a library for developing incremental monoid computations on Hadoop. It runs on unmodified Hadoop and provides an accumulator-based interface for programmers to store and use state across successive runs; the framework ensures that only the necessary subcomputations are performed. It is successfully used at LinkedIn, one of the largest online social networks, for many use cases in dashboarding and machine learning. Hourglass is open source and freely available.
High Performance Computing and Big Data Geoffrey Fox
We propose a hybrid software stack with Large scale data systems for both research and commercial applications running on the commodity (Apache) Big Data Stack (ABDS) using High Performance Computing (HPC) enhancements typically to improve performance. We give several examples taken from bio and financial informatics.
We look in detail at parallel and distributed run-times including MPI from HPC and Apache Storm, Heron, Spark and Flink from ABDS stressing that one needs to distinguish the different needs of parallel (tightly coupled) and distributed (loosely coupled) systems.
We also study "Java Grande" or the principles to use to allow Java codes to perform as fast as those written in more traditional HPC languages. We also note the differences between capacity (individual jobs using many nodes) and capability (lots of independent jobs) computing.
We discuss how this HPC-ABDS concept allows one to discuss convergence of Big Data, Big Simulation, Cloud and HPC Systems. See http://hpc-abds.org/kaleidoscope/
A performance analysis of OpenStack Cloud vs Real System on Hadoop ClustersKumari Surabhi
It introduces the performance analysis of OpenStack Cloud with the commodity computers in the big data environments. It concludes that the data storage and analysis in hadoop cluster in cloud is more flexible and easily scalable than the real system cluster. It also concludes the cluster in commodities computers are faster than the cloud clusters.
it just provide information about hadoop what is hadoop and how hadoop overcomes the disadvantage of distributed system and i have also shown an example program for mapreduce
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Building a Big Data platform with the Hadoop ecosystemGregg Barrett
This presentation provides a brief insight into a Big Data platform using the Hadoop ecosystem.
To this end the presentation will touch on:
-views of the Big Data ecosystem and it’s components
-an example of a Hadoop cluster
-considerations when selecting a Hadoop distribution
-some of the Hadoop distributions available
-a recommended Hadoop distribution
Jumpstart your career with the world’s most in-demand technology: Hadoop. Hadooptrainingacademy provides best Hadoop online training with quality videos, comprehensive
online live training and detailed study material. Join today!
For more info, visit: http://www.hadooptrainingacademy.com/
Contact Us:
8121660088
732-419-2619
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Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
IBM Data Engine for Hadoop and Spark - POWER System Edition ver1 March 2016Anand Haridass
This document describes the IBM Data Engine for Hadoop and Spark (IDEHS) - Power Systems Edition, an IBM integrated solution. This solution features a technical-computing architecture that supports running Big Data-related workloads more easily and with higher performance. It includes the servers, network switches, and software needed to run MapReduce and Spark-based workloads.
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015Deanna Kosaraju
Optimal Execution Of MapReduce Jobs In Cloud
Anshul Aggarwal, Software Engineer, Cisco Systems
Session Length: 1 Hour
Tue March 10 21:30 PST
Wed March 11 0:30 EST
Wed March 11 4:30:00 UTC
Wed March 11 10:00 IST
Wed March 11 15:30 Sydney
Voices 2015 www.globaltechwomen.com
We use MapReduce programming paradigm because it lends itself well to most data-intensive analytics jobs run on cloud these days, given its ability to scale-out and leverage several machines to parallel process data. Research has demonstrates that existing approaches to provisioning other applications in the cloud are not immediately relevant to MapReduce -based applications. Provisioning a MapReduce job entails requesting optimum number of resource sets (RS) and configuring MapReduce parameters such that each resource set is maximally utilized.
Each application has a different bottleneck resource (CPU :Disk :Network), and different bottleneck resource utilization, and thus needs to pick a different combination of these parameters based on the job profile such that the bottleneck resource is maximally utilized.
The problem at hand is thus defining a resource provisioning framework for MapReduce jobs running in a cloud keeping in mind performance goals such as Optimal resource utilization with Minimum incurred cost, Lower execution time, Energy Awareness, Automatic handling of node failure and Highly scalable solution.
Analyzing Big data in R and Scala using Apache Spark 17-7-19Ahmed Elsayed
We can make a data mining to get the prediction about the future data, which is mined from an old data especially Big data using a machine learning algorithms based on two clusters. One is the intrinsic for managing the file system of Big data, which is called Hadoop. The other is essentially to make fast analysis of Big data which is called Apache Spark. In order to achieve this purpose we will use R based on Rstudio or Scala based on Zeppelin.
Best Big Data Hadoop Training in Chennai at Credo Systemz will help you learn and upgrade your knowledge in the Core components, Database concepts and Linux Operating system.
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).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
9. HADOOP FRAMEWORK
• Hadoop Common – contains libraries and utilities needed by other Hadoop modules
• Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on
commodity machines, providing very high aggregate bandwidth across the cluster
• Hadoop YARN – a platform responsible for managing computing resources in clusters
and using them for scheduling users' applications
• Hadoop MapReduce – an implementation of the MapReduce programming model for
large-scale data processing
10. ARCHITECTURE
Hadoop Common Package
comprises of:
• File system and operating system level
abstractions
• MapReduce engine (either MapReduce/
MR1 or YARN/MR2)
• Hadoop Distributed File System(HDFS)
Hadoop requires Java Runtime
Environment (JRE) 1.6 or higher
The standard startup and shutdown scripts
require that Secure Shell(SSH) be set up
between nodes in the cluster
11. MapReduce is a framework for
processing parallelizable problems
across large datasets using a large
number of computers (nodes),
collectively referred to as a cluster(if
all nodes are on the same local
network and use similar hardware) or
a grid.
Processing can occur on data stored
either in a filesystem(unstructured) or
in a database (structured).
MapReduce can take advantage of the
locality of data, processing it near the
place it is stored in order to minimize
communication overhead.
MapReduce Engine
12. MapReduce is as a 5-step parallel
and distributed computation:
1 Prepare the Map() input – the "MapReduce
system" designates Map processors,
assigns the input key value K1 that each
processor would work on, and provides that
processor with all the input data associated
with that key value.
2 Run the user-provided Map() code – Map() is
run exactly once for each K1 key value,
generating output organized by key values
K2.
3 "Shuffle" the Map output to the Reduce
processors – the MapReduce system
designates Reduce processors, assigns the
K2 key value each processor should work
on, and provides that processor with all the
Map-generated data associated with that
key value.
4 Run the user-provided Reduce() code –
Reduce() is run exactly once for each K2 key
value produced by the Map step.
5 Produce the final output – the MapReduce
system collects all the Reduce output, and
sorts it by K2 to produce the final outcome.
Map Reduce Algorithm
13. Managers in India
As stated in the article
The story of Jonathan Goldman illustrates, their greatest opportunity to add value is not in
creating reports or presentations for senior executives but in innovating with customer-facing
products and processes.
In India there are so many E-Commerce companies that are in their development stage. Like the
data scientists of big companies like Intuit, Google, GE, Zynga have worked their way out to
optimize the service contracts and maintenance intervals for industrial products, either by core
search, ad servicing algorithms, MapReduce algorithm etc..
So the Managers in India should focus more on their data analytical skills mentioned in the
above slides
instead of creating reports for senior executives.
They should be thorough with Machine Learning, R, Python, Apache Hadoop Packages,
Mathematical and Statistical knowledge.
This will help them in their business life as data science is the sexiest job of 21st century!!!!!