1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Introduction to Big Data & Big Data 1.0 SystemPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
Today we will start with a brief introduction to Big Data. We will talk about how Big Data is generated, where we can apply it and also about the first world-wide famous platform of BigData 1.0 System, which is Hadoop.
#CHEDTEB
www.chedteb.eu
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Introduction to Big Data & Big Data 1.0 SystemPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
Today we will start with a brief introduction to Big Data. We will talk about how Big Data is generated, where we can apply it and also about the first world-wide famous platform of BigData 1.0 System, which is Hadoop.
#CHEDTEB
www.chedteb.eu
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big 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/
Big Data vs. Small Data...what's the difference?Anna Kuhn
What is big data? A 3-pg summary of the key differences between "big data" and "small data."
Includes comparison of data jargon, high level technologies, staffing / people, and the nature of the data itself.
Perfect for data-savvy marketers & agencies, and beginner-to-intermediate data and analytics professionals.
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.
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
Introduction to Big Data (non-technical) and the importance of Data Science to create meaning.
First of all we define Big Data in the light of the 3 Vs: volume, velocity and variety; next we move on to redefine Big Data, and we touch the topic of a data lake. We envision that Big Data will become mainstream for small organisations as well, what we can do with Big Data, how to tackle Big Data projects, what challenges lie ahead, but what opportunities are there to reap. And of course how important data science is to find the meaning in all the data.
Today, we have data – lots of it. We can process information – in many ways. And with these two tools and a little bit of creativity, we are discovering the vast depths of human behavior and by extension, a way to accurately predict the future -- and our future happiness. In fact, we can quantify human movement, behaviors, desires, and even moods on a scale that wasn’t possible before a series of advances in processing power, developments in psychology and social network science, and most importantly, access to data.
In advertising, industry, and humanity, we have experienced the evolution from Web 1.0 (informational) to Web 2.0 (platform) to Web 3.0 (semantic) to elements of Web 4.0 (anticipatory) – In this anticipatory era, what can we dream of next? Beyond addressability and increasing ad relevance, how can businesses utilize these advances in product development and other market initiatives? Can we make the leap from inductive logic to human-paralleled intuition? Can this make up for our human brain mechanics that make predicting our own happiness so difficult?
In this talk we’ll cover the evolutions in data access, models for information processing, and the science of collaboration to see not only how they have been leveraged in businesses but also how they are used to better understand human behavior, and hopefully in the near future, a little bit of happiness.
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/
Big Data vs. Small Data...what's the difference?Anna Kuhn
What is big data? A 3-pg summary of the key differences between "big data" and "small data."
Includes comparison of data jargon, high level technologies, staffing / people, and the nature of the data itself.
Perfect for data-savvy marketers & agencies, and beginner-to-intermediate data and analytics professionals.
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.
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
Introduction to Big Data (non-technical) and the importance of Data Science to create meaning.
First of all we define Big Data in the light of the 3 Vs: volume, velocity and variety; next we move on to redefine Big Data, and we touch the topic of a data lake. We envision that Big Data will become mainstream for small organisations as well, what we can do with Big Data, how to tackle Big Data projects, what challenges lie ahead, but what opportunities are there to reap. And of course how important data science is to find the meaning in all the data.
Today, we have data – lots of it. We can process information – in many ways. And with these two tools and a little bit of creativity, we are discovering the vast depths of human behavior and by extension, a way to accurately predict the future -- and our future happiness. In fact, we can quantify human movement, behaviors, desires, and even moods on a scale that wasn’t possible before a series of advances in processing power, developments in psychology and social network science, and most importantly, access to data.
In advertising, industry, and humanity, we have experienced the evolution from Web 1.0 (informational) to Web 2.0 (platform) to Web 3.0 (semantic) to elements of Web 4.0 (anticipatory) – In this anticipatory era, what can we dream of next? Beyond addressability and increasing ad relevance, how can businesses utilize these advances in product development and other market initiatives? Can we make the leap from inductive logic to human-paralleled intuition? Can this make up for our human brain mechanics that make predicting our own happiness so difficult?
In this talk we’ll cover the evolutions in data access, models for information processing, and the science of collaboration to see not only how they have been leveraged in businesses but also how they are used to better understand human behavior, and hopefully in the near future, a little bit of happiness.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
Introduction
Why big data is required
Big data
Big data facts
Big data 3 V’s
Why big data is important
Examples where big data is used
Analytics
Approach to analytic development
Analysis of data through senser.
Analytics can help in
Big data analytics
Big data analytics in practice
How big data is used in twitter to get patterns
Human resource cost and risk of big data.
Big data analytics tools and technology
Conclusions
references
Quontra solutions is your premier online IT educational destination in UK. It provides online IT courses like Selenium , Hadoop ,CCNA ,Cloud Computing ,Business Analyst and Many other IT courses. All the courses are designed by experienced instructors and designers. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment there is an urgent need for IT professional to keep themselves in trend with Hadoop and Big Data technologies
.
Quontra Specialties :
***All the courses are designed by Experienced Instructors and Designers.
***. Trainers are not limited to the syllabus, they explain off –the-shelf content also.
*** 24X7 technical support team .
***Unlimited access to all recorded sessions ,available after every live class.
***Syllabus built based on professional standards and employer insights.
***Trainers are Certified Experts in their corresponding field and they bring years of industry experience in to the training classes
Experfy.com - This Big Data training gives one the background necessary to start doing analyst work on Big Data. It covers - areas like Big Data basics, Hadoop basics and tools like Hive and Pig - which allows one to load large data sets on Hadoop and start playing around with SQL Like queries over it using Hive and do analysis and Data Wrangling work with Pig.
The Big Data online course also teaches Machine Learning Basics and Data Science using R and also covers Mahout briefly - a Recommendation, Clustering Engine on Large data sets. The course includes hands-on exercises with Hadoop, Hive , Pig and R with some examples of using R to do Machine Learning and Data Science work
See more at: https://www.experfy.com/training/courses/big-data-analyst
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacydbpublications
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. There have been a various different privacy preserving mechanisms developed for protecting privacy at different stages (e.g. data generation, data storage, data processing) of big data life cycle. The goal of this paper is to provide a complete overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms and also we illustrate the infrastructure of big data and state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. This paper focus on the anonymization process, which significantly improve the scalability and efficiency of TDS (top-down-specialization) for data anonymization over existing approaches. Also, we discuss the challenges and future research directions related to preserving privacy in big data.
Big Data is one of the emerging areas in today's technological world. In this socially active world, data is growing at a tremendous pace of 2.5 quintillion bytes a day roughly that is only set to increase over the coming years.
Here is a guide for all beginners who express interest in this new field - Big Data.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
SWOT of Bigdata Security Using Machine Learning Techniquesijistjournal
This paper gives complete guidelines on BigData, Different Views of BigData, etc.How the BigData is useful to us and what are the factors affecting BigData all the things are covered under this paper. The paper also contains the BigData Machine learning techniques and how the Hadoop comes into the picture. It also contains the what is importance of BigData security. The paper mostly covers all the main point that affect Big Data and Machine Learning.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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.
3. 1.BIG DATA
2.CHARACTERS
TICS OF BIG
DATA
3.WHAT IS BIG
DATA
ANALY TICS
4.T YPES OF BIG
DATA
ANALY TICS
5.DOMAINS
USING BIG
DATA
ANALY TICS
CONTENT
4. We create 2.5 quintillions of
data everyday.
Data comes in from various
sources and has different
formats.
This enormous collection of
data is big data .
EXPLOSION OF DATA____________
6. “big data analytics examines large and different
types of data to uncover hidden
patterns,correlations and other insights”
WHAT IS BIG DATA ANALYTICS?
7. WHAT IS HAPPENING NOW BASED
ON INCOMING DATA.
DESCRIPTIVE ANALYSIS BASICALLY
ANSWERS YOUR QUESTION WHAT
HAS HAPPENED .
IT DESCRIBES AND SUMMARIZE THE
RAW DATA AND IT MAKES IT
SOMETHING WHICH IS
INTERPRETABLE BY HUMANS.
BEST EXAMPLE FOR DESCRIPTIVE
ANALYSIS IS GOOGLE ANALYTICS
TOOL.
TYPES OF BIG DATA ANALYTICS
9. PRESCRIPTIVE ANALYSIS
What action should be taken
Google’s self driving car is a
perfect example of
prescriptive analytics.It
analyses the environment
and decides the direction to
take based on data.
V
10. Diagnostic analytics
WHY DID IT HAPPEN
For a social media marketing
campaign,you can use
diagnostic analytics to
assess the number of
posts,mentions,followers,fan
s,page,views,reviews etc.and
analyse the failure and
success rate of the campaign
at a fundamental level.