I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
Class lecture by Prof. Raj Jain on Big Data. The talk covers Why Big Data Now?, Big Data Applications, ACID Requirements, Terminology, Google File System, BigTable, MapReduce, MapReduce Optimization, Story of Hadoop, Hadoop, Apache Hadoop Tools, Apache Other Big Data Tools, Other Big Data Tools, Analytics, Types of Databases, Relational Databases and SQL, Non-relational Databases, NewSQL Databases, Columnar Databases. Video recording available in YouTube.
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
Big data is the term for any gathering of information sets, so expensive and complex, that it gets to be hard to process for utilizing customary information handling applications. The difficulties incorporate investigation, catch, duration, inquiry, sharing, stockpiling, Exchange, perception, and protection infringement. To reduce spot business patterns, anticipate diseases, conflict etc., we require bigger data sets when compared with the smaller data sets. Enormous information is hard to work with utilizing most social database administration frameworks and desktop measurements and perception bundles, needing rather enormously parallel programming running on tens, hundreds, or even a large number of servers. In this paper there was an observation on Hadoop architecture, different tools used for big data and its security issues.
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
this slide is for brief introduction to the big data with little bit of fun through memes.
it is prepared with the articles from different websites about big data and some of my own words so it would be great if you like it
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
Asterix Solution’s Hadoop Training is designed to help applications scale up from single servers to thousands of machines. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Duration - 25 hrs
Session - 2 per week
Live Case Studies - 6
Students - 16 per batch
Venue - Thane
Presented By :- Rahul Sharma
B-Tech (Cloud Technology & Information Security)
2nd Year 4th Sem.
Poornima University (I.Nurture),Jaipur
www.facebook.com/rahulsharmarh18
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
CLASS 11 CBSE B.St Project AIDS TO TRADE - INSURANCE
Big data and Hadoop overview
1. Big Data and Hadoop Overview
What it Big Data?
Big data is a term that describes the large volume of data – both structured and unstructured – that
inundate a business on a day-to-day basis. In short, big data is so large and complex that none of the
traditional data management tools are able to store it or process it efficiently.
Who Generates Big Data?
More and more data are being produced by an increasing number of electronic devices surrounding us
and on the internet. The amount of data and the frequency at whichthey are produced are so vast that
they are referred as “BIGData”.
Why Is Big Data Important?
The importance of big data doesn’t revolve around how much data you have, but what you do with it.
You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time
reductions, 3) new product development and optimized offerings, and 4) smart decision making. When
you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
Determining root causes of failures, issues and defects in near-real time.
Generating coupons at the point of sale based on the customer’s buying habits.
Recalculating entire risk portfoliosin minutes.
Detecting fraudulent behaviour before it affectsyour organization.
2. Brief History of Big Data
While the term “big data” is relatively new, the act of gathering and storing large amounts of
information for eventual analysis is ages old. The concept gained momentum in the early 2000s when
industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs:
Volume Organizations collect data from a variety of sources, including business transactions, social
media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a
problem – but new technologies have eased the burden.
Velocity Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID
tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
Variety Data comes in all types of formats – from structured, numeric data in traditional databases to
unstructured text documents, email, video, audio, stock ticker data and financial transactions.
Now a day’s below two Vs got added
Variability In addition to the increasing velocities and varieties of data, data flows can be highly
inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-
triggered peak data loads can be challenging to manage.
Complexity Today's data comes from multiple sources, which makes it difficult to link, match, cleanse
and transform data across systems. However, it’s necessary to connect and correlate relationships,
hierarchies and multiple data linkages or your data can quickly spiral out of control.
Categories of 'Big Data'
Big data' couldbe found in three forms:
1. Structured
2. Unstructured
3. Semi-structured
Structured:Data stored in a relational database management system is one example of
a 'structured' data.
Unstructured:Output returned by 'GoogleSearch'.
Semi-structured: Personal data stored in a XML file.
3. Evolutionof Hadoop
As the World Wide Web grew in the late 1900s and early 2000s, search engines and indexes were
created to help locate relevant information amid the text-based content. In the early years, search
results were returned by humans. But as the web grew from dozens to millions of pages, automation
was needed. Web crawlers were created, many as university-led research projects, and search engine
start-ups took off (Yahoo, AltaVista, etc.).
One such project was an open-source web search engine called Nutch – the brainchild of Doug Cutting
and Mike Cafarella. They wanted to return web search results faster by distributing data and
calculations across different computers so multiple tasks could be accomplished simultaneously. During
this time, another search engine project called Google was in progress. It was based on the same
concept – storing and processing data in a distributed, automated way so that relevant web search
results could be returned faster.
In 2006, Cutting joined Yahoo and took with him the Nutch project as well as ideas based on Google’s
early work with automating distributed data storage and processing. The Nutch project was divided –
the web crawler portion remained as Nutch and the distributed computing and processing portion
became Hadoop. In 2008, Yahoo released Hadoop as an open-source project. Today, Hadoop’s
framework and ecosystem of technologies are managed and maintained by the non-profit Apache
Software Foundation (ASF), a global community of software developers and contributors.
FunFact: "Hadoop”was thenameof a yellow toy elephant owned by the son of one of its inventors.
4. Why is Hadoop important?
Ability to store and process huge amounts of any kind of data, quickly. With data volumes and
varieties constantly increasing, especially from social media and the Internet of Things (IoT),
that's a key consideration.
Computing power. Hadoop's distributed computing model processes big data fast. The more
computing nodes you use the more processing power you have.
Fault tolerance. Data and application processing are protected against hardware failure. If a
node goes down, jobs are automatically redirected to other nodes to make sure the distributed
computing does not fail. Multiple copies of all data are stored automatically.
Flexibility. Unlike traditional relational databases, you don’t have to pre-process data before
storing it. You can store as much data as you want and decide how to use it later. That includes
unstructured data like text, images and videos.
Low cost. The open-source framework is free and uses commodity hardware to store large
quantities of data.
Scalability. You can easily grow your system to handle more data simply by adding nodes. Little
administration is required.
What are key component of Hadoop?
There are 3 core components of the Hadoop framework are:
MapReduce– A software programming model forprocessing large sets of data in parallel
HDFS – The Java-based distributed file system that can store all kinds of data withoutprior
organization.
YARN– A resource management frameworkforscheduling and handling resource requests
from distributed applications.
5. Types of Hadoop installation
There are various ways in whichHadoop can be run. Here are the various scenarios in whichHadoop
can be downloaded, installed and run.
Standalone mode
Though Hadoop is a distributed platform for working with big data, we can even install Hadoop on a
single node in a single standalone instance. This way the entire Hadoop platform runs like a system
which is running on Java. This is mostly used for the purpose of debugging. It helps if you want to check
your mapreduce applications on a single node before running on a huge cluster of Hadoop.
Fully Distributed mode
This is distributed mode that has several nodes of commodity hardware connected to form the Hadoop
cluster. In such a setup the NameNode, JobTracker and Secondary NameNode work on the master node
whereas the Datanode and the secondarydatanode work on the slave node. The other set of nodes
namely the Datanode and the TaskTracker work on the slave node.
Pseudo distributed mode
This in effect is a single node Java system that runs the entire Hadoop cluster. So the various daemons
like the NameNode, Datanode, TaskTracker and JobTracker run on the single instance of the Java
machine to form the distributed Hadoop cluster.
6. Hadoop ecosystem
HBaseA scalable distributed database that supports structured data storage forlarge tables.
HiveA data warehouse infrastructure that provides data summarization and ad hoc querying.
MahoutA Scalable machine learning and data mining library.
PigA high-level data-flow language and execution frameworkforparallel computation.
Flumeis a distributed, reliable, and available service forefficiently collecting,aggregating, and
moving large amounts of log data.
Oozieis a workflow scheduler system to manage Apache Hadoop jobs.
Sqoopis a tool designed for efficiently transferring bulk data between Apache Hadoop and
structured data stores such as relational databases.
Zookeeper isan effortto develop and maintain an open-source server which enables highly
reliable distributed coordination.
TheOracleR ConnectorforHadoop (ORCH) providesaccess to a Hadoop cluster from R,
enabling manipulation of HDFS-resident data and the execution of Mapreduce jobs.