Big data is characterized by 3 V's - volume, velocity, and variety. It refers to large and complex datasets that are difficult to process using traditional database management tools. Key technologies to handle big data include distributed file systems, Apache Hadoop, data-intensive computing, and tools like MapReduce. Common tools used are infrastructure management tools like Chef and Puppet, monitoring tools like Nagios and Ganglia, and analytics platforms like Netezza and Greenplum.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
Big Data refers to the bulk amount of data while Hadoop is a framework to process this data.
There are various technologies and fields under Big Data. Big Data finds its applications in various areas like healthcare, military and various other fields.
http://www.techsparks.co.in/thesis-topics-in-big-data-and-hadoop/
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Big Data refers to the bulk amount of data while Hadoop is a framework to process this data.
There are various technologies and fields under Big Data. Big Data finds its applications in various areas like healthcare, military and various other fields.
http://www.techsparks.co.in/thesis-topics-in-big-data-and-hadoop/
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
Introduction To Big Data Analytics On Hadoop - SpringPeopleSpringPeople
48 hours of video are uploaded to YouTube every minute, resulting in nearly 8 years of content every day.
This is where comes the role of Big Data analytics so that huge amount of data can be maintained easily.
A brief introduction to Big Data Analytics On Hadoop.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
A discussion on IT trends forecast for the year ahead in respect to entrepreneurs & students.
The transcript of my oral notes for the presentation are added to the last slides.
Big Data, Big Opportunity: A Primer for Understanding The Big Data FrontierCA Technologies
The promise of discovering business opportunities by leveraging untapped enterprise data has created big data ecosystems that have many complex, moving parts. Whether your top priority is crafting a strategy for a new big data initiative, modernizing core technologies such as mainframe or just to stay ahead of new technology innovations and trends, this session will help you to better understand this evolving and dynamic market. Learn how you can leverage big data and related innovations for self-service and data discovery techniques and help deliver more agile and responsive services for your business. For more information, please visit http://cainc.to/Nv2VOe
Following 50 years deploying Systems of Record, enterprises are beginning the journey to Systems of Intelligence.
These apps anticipate and influence consumers at the point of interaction for better outcomes for customer and vendor
Data Management In Cellular Networks Using Activity MiningIDES Editor
In the recent technology advances, an increasing
number of users are accessing various information systems
via wireless communication. The majority users in a mobile
environment are moving and accessing wireless services for
the activities they are currently unavailable inside. We
propose the idea of complex activity for characterizing the
continuously changing complex behavior patterns of mobile
users. For the purpose of data management, a complex activity
is copy as a sequence of location movement, service requests,
the coincidence of location and service, or the interleaving of
all above. An activity may be composed of sub activities.
Different activities may exhibit dependencies that affect user
behaviors. We argue that the complex activity concept provides
a more specific, rich, and detail description of user behavioral
patterns which are very useful for data management in mobile
environments. Correct exploration of user activities has the
possible of providing much higher quality and personalized
services to individual user at the right place on the right time.
We, therefore, propose new methods for complex activity
mining, incremental maintenance, online detection and
proactive data management based on user activities. In
particular, we develop pre-fetching and pushing techniques
with cost sensitive control to make easy analytical data
allocation. First round implementation and simulation results
shows that the proposed framework and techniques can
significantly increase local availability, conserve execution
cost, reduce response time, and improve cache utilization.
This presentation contains a broad introduction to big data and its technologies.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.
Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.
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.
Vikram Andem Big Data Strategy @ IATA Technology Roadmap IT Strategy Group
Vikram Andem, Senior Manager, United Airlines, A case for Bigdata Program and Strategy @ IATA Technology Roadmap 2014, October 13th, 2014, Montréal, Canada
4. • Wikipedia : In information technology, big data is a collection
of data sets so large and complex that it becomes difficult to
process using on-hand database management tools. The
challenges include capture, curation, storage, search, sharing,
analysis, and visualization.
• IDC : Big data technologies describe a new generation of
technologies and architectures, designed to economically
extract value from very large volumes of a wide variety of
data, by enabling high velocity capture, discovery, and/or
analysis.
• IBM says that ”three characteristics define big data:”
– Volume (Terabytes -> Zettabytes)
– Variety (Structured -> Semi-structured -> Unstructured)
– Velocity (Batch -> Streaming Data)
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28. What Big data should achieve:
Volume: Turn 12 terabytes of Tweets created each day into improved product
sentiment analysis
Convert 350 billion annual meter readings to better predict power consumption
Velocity: Sometimes 2 minutes is too late.
Scrutinize 5 million trade events created each day to identify potential fraud
Analyze 500 million daily call detail records in real-time to predict customer churn
faster
Variety: Big data is any type of data - structured and unstructured data such as
text, sensor data, audio, video, click streams, log files and more.
Monitor 100’s of live video feeds from surveillance cameras to target points of
interest
Exploit the 80% data growth in images, video and documents to improve
customer satisfaction
Veracity: Establishing trust in big data presents a huge challenge as the variety
and number of sources grows.
29. Big data is more than simply a matter of size; it is an opportunity to find
insights in new and emerging types of data and content, to make your
business more agile, and to answer questions that were previously
considered beyond your reach.
Here are three key technologies that can help you get a handle on big data –
and even more importantly, extract meaningful business value from it .
• Information management for big data. Manage data as a strategic, core
asset, with ongoing process control for big data analytics .
•High-performance analytics for big data. Gain rapid insights from big
data and the ability to solve increasingly complex problems using more
data .
•Flexible deployment options for big data. Choose between options for
on premises or hosted, software-as-a-service (SaaS) approaches for big
data and big data analytics
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31. • Massively Parallel Processing (MPP) – This involves a coordinated
processing of a program by multiple processors (200 or more in number).
Each of the processors makes use of its own operating system and
memory and works on different parts of the program. Each part
communicates via messaging interface. An MPP system is also known as
“loosely coupled” or “shared nothing” system.
• Distributed file system or network file system allows client nodes to
access files through a computer network. This way a number of users
working on multiple machines will be able to share files and storage
resources. The client nodes will not be able to access the block storage
but can interact through a network protocol. This enables a restricted
access to the file system depending on the access lists or capabilities on
both servers and clients which is again dependent on the protocol.
32. • Apache Hadoop is key technology used to handle big data, its analytics
and stream computing. Apache Hadoop is an open source software
project that enables the distributed processing of large data sets across
clusters of commodity servers. It can be scaled up from a single server to
thousands of machines and with a very high degree of fault tolerance.
Instead of relying on high-end hardware, the resiliency of these clusters
comes from the software’s ability to detect and handle failures at the
application layer.
• Data Intensive Computing is a class of parallel computing application
which uses a data parallel approach to process big data. This works based
on the principle of collocation of data and programs or algorithms used to
perform computation. Parallel and distributed system of inter-connected
stand alone computers that work together as a single integrated
computing resource is used to process / analyze big data.
33. • HDFS:
- Large files are split into parts
- Move file parts into a cluster
- Fault-tolerant through replication across nodes while being rack-aware
- Bookkeeping via NameNode
• MapReduce
- Move algorithms close to the data by structuring them for parallel
execution so that each task works on a part of the data. The power of
Simplicity!
• noSQL:
- Old technology – before RDBMS
- Sequential files
- Hierarchical DB
- Network DB
- Graph DB (Neo4j, AllegroGraph)
- Document Stores (Lotus Domino, MongoDB, JackRabbit)
- Memory Caches
34. • Common Tools:
- Infrastructure Management:
- Chef & Puppet from DevOps movement
- Grid Monitoring tools:
- Operational insight with Nagios, Ganglia, Hyperic and ZenOSS
- Development support is a need of the hour
• Operational Tools:
– Chef & Puppet from DevOps movement + Ganglia
• Analytics Platforms (DW Solutions, BI solutions and analytics)
– Netezza, GreenPlum, Vertica
• Universal Data (All data storage needs at enterprise level)
– Lily & Spire
IDC = Definition critical with regards to introduction of Gartner’s principle of 3V’s of Big DataIBM = adds the benchmark of what the 3V’s should be talking about for Big Data
Can markets still do without big data?
Source links as well as additional information on the subject