Everyone wants to launch a Big Data project, but there's still lots of confusion as to how. The key, as this presentation shows, is to minimize the cost of failure and increase iteration, using a strategy based on using well-known open-source tools.
The document discusses the growth of big data, defining it as data that exceeds capabilities to capture, manage and process within a tolerable time. It notes global data grew from 800 terabytes in 2001 to over 2 zettabytes in 2012 and is projected to reach 35 zettabytes by 2020. Examples of big data sources include social media posts, web searches, medical records and sensor data. The value of big data lies in making information transparent and usable at high frequency to improve decision making, products/services and productivity. However, big data also faces challenges around skills shortage, privacy concerns and overreliance on past data.
For some, Hadoop is synonymous with “Big Data,” but Hadoop is just one component of a successful Big Data architecture. Depending on one’s application, it may not even be the most important part.
NoSQL solutions like MongoDB also play a dominant role for storage and real-time data processing, helping companies keep pace with the scale of their data requirements. But NoSQL figures even more prominently in helping enterprises consume a wide variety of data sources at speeds not currently possible in Hadoop. NoSQL, then, offers a useful complement to Hadoop, as well as the transaction-based data of traditional RDBMSs.
Tackling Big Data is not a one-tool job, and so the orchestration of the appropriate NoSQL database with Hadoop and RDBMS is essential. In this session, we’ll dig deep into the different types of NoSQL, identifying how they differ and the types of Big Data workloads for which they’re best suited. We’ll also explore the trade-offs one makes in choosing NoSQL databases like MongoDB or Neo4j over an RDBMS like MySQL, and when it makes sense to use both Hadoop and NoSQL and when it’s more appropriate to use NoSQL on its own.
This document discusses the rise of big data and its impact. It notes that while companies have always had to manage large amounts of data, new technologies and data sources have led to exponentially greater volumes, velocities, and varieties of data. Effectively analyzing this big data can provide valuable insights, but also poses major challenges in how to capture, store, manage and make sense of such diverse information. The document provides several examples of how cities and companies are now generating and analyzing big data through sensors and other technologies to improve services.
Winning the big data revolution: what businesses leaders need to knowMicrosoft Ideas
Tout le monde parle des big data. Mais peu nombreux sont ceux qui savent véritablement ce qu’ils désignent et surtout comment les entreprises peuvent les utiliser pour révéler leur véritable potentiel. Comment donner du sens à ces données ? Comment en faire un avantage compétitif majeur ? Comment sortir vainqueur de cette révolution ? A l’occasion de la sortie de son dernier ouvrage, dans lequel il explore le phénomène big data et les nouveaux business model qui sont en train d’émerger, Kenneth Cukier nous livrera, lors d’une keynote inédite, les clefs pour réussir cette transformation fondamentale.
Speakers : Kenneth Cukier (The Economist)
World Wide Web has completely changed the dynamics and conventional meaning of operating and managing a business. It has opened new avenues through seamless interaction between consumers and business houses. In the process it has flooded
The World Wide Web with unimaginable volumes of data, for instance according to an estimate the average volume of data created in a single day is easily around 2.5 quintillion bytes.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Tools and techniques adopted for big data analyticsJOSEPH FRANCIS
This document discusses tools and techniques for big data analytics. It begins by defining big data and explaining why big data analysis is important for businesses. It then outlines the characteristics and history of big data, as well as the challenges and phases of big data analysis. The document proceeds to describe several tools and techniques used for big data analytics, including machine learning, natural language processing, and visualization. It provides examples of how these tools and techniques have been applied through case studies of Indian elections, AirBnB, and Shoppers Stop.
This document provides an overview of big data. It begins with definitions of big data and its key characteristics, including volume, velocity, and variety. It then discusses how big data is stored, selected, and processed. Examples of big data sources and tools are provided. The document outlines several applications of big data across different industries like healthcare, manufacturing, and retail. It also discusses risks of big data like privacy issues and costs. The future of big data is presented, with projections that the big data market will grow significantly in coming years. In closing, references are provided for additional information on big data.
The document discusses the growth of big data, defining it as data that exceeds capabilities to capture, manage and process within a tolerable time. It notes global data grew from 800 terabytes in 2001 to over 2 zettabytes in 2012 and is projected to reach 35 zettabytes by 2020. Examples of big data sources include social media posts, web searches, medical records and sensor data. The value of big data lies in making information transparent and usable at high frequency to improve decision making, products/services and productivity. However, big data also faces challenges around skills shortage, privacy concerns and overreliance on past data.
For some, Hadoop is synonymous with “Big Data,” but Hadoop is just one component of a successful Big Data architecture. Depending on one’s application, it may not even be the most important part.
NoSQL solutions like MongoDB also play a dominant role for storage and real-time data processing, helping companies keep pace with the scale of their data requirements. But NoSQL figures even more prominently in helping enterprises consume a wide variety of data sources at speeds not currently possible in Hadoop. NoSQL, then, offers a useful complement to Hadoop, as well as the transaction-based data of traditional RDBMSs.
Tackling Big Data is not a one-tool job, and so the orchestration of the appropriate NoSQL database with Hadoop and RDBMS is essential. In this session, we’ll dig deep into the different types of NoSQL, identifying how they differ and the types of Big Data workloads for which they’re best suited. We’ll also explore the trade-offs one makes in choosing NoSQL databases like MongoDB or Neo4j over an RDBMS like MySQL, and when it makes sense to use both Hadoop and NoSQL and when it’s more appropriate to use NoSQL on its own.
This document discusses the rise of big data and its impact. It notes that while companies have always had to manage large amounts of data, new technologies and data sources have led to exponentially greater volumes, velocities, and varieties of data. Effectively analyzing this big data can provide valuable insights, but also poses major challenges in how to capture, store, manage and make sense of such diverse information. The document provides several examples of how cities and companies are now generating and analyzing big data through sensors and other technologies to improve services.
Winning the big data revolution: what businesses leaders need to knowMicrosoft Ideas
Tout le monde parle des big data. Mais peu nombreux sont ceux qui savent véritablement ce qu’ils désignent et surtout comment les entreprises peuvent les utiliser pour révéler leur véritable potentiel. Comment donner du sens à ces données ? Comment en faire un avantage compétitif majeur ? Comment sortir vainqueur de cette révolution ? A l’occasion de la sortie de son dernier ouvrage, dans lequel il explore le phénomène big data et les nouveaux business model qui sont en train d’émerger, Kenneth Cukier nous livrera, lors d’une keynote inédite, les clefs pour réussir cette transformation fondamentale.
Speakers : Kenneth Cukier (The Economist)
World Wide Web has completely changed the dynamics and conventional meaning of operating and managing a business. It has opened new avenues through seamless interaction between consumers and business houses. In the process it has flooded
The World Wide Web with unimaginable volumes of data, for instance according to an estimate the average volume of data created in a single day is easily around 2.5 quintillion bytes.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Tools and techniques adopted for big data analyticsJOSEPH FRANCIS
This document discusses tools and techniques for big data analytics. It begins by defining big data and explaining why big data analysis is important for businesses. It then outlines the characteristics and history of big data, as well as the challenges and phases of big data analysis. The document proceeds to describe several tools and techniques used for big data analytics, including machine learning, natural language processing, and visualization. It provides examples of how these tools and techniques have been applied through case studies of Indian elections, AirBnB, and Shoppers Stop.
This document provides an overview of big data. It begins with definitions of big data and its key characteristics, including volume, velocity, and variety. It then discusses how big data is stored, selected, and processed. Examples of big data sources and tools are provided. The document outlines several applications of big data across different industries like healthcare, manufacturing, and retail. It also discusses risks of big data like privacy issues and costs. The future of big data is presented, with projections that the big data market will grow significantly in coming years. In closing, references are provided for additional information on big data.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
Index:
1) The Importance of Data
2) What is Big Data Concept
3) Big Data vs. Cloud Computing
4) The basic idea behind Big Data
5) Why do we use Big Data
6) Top 10 companies using Big Data
7) What kind of data is Big Data
8) Is Privacy a value
9) Future of Big Data by 2020
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
This document provides an overview of big data. It defines big data as large volumes of data that are high in velocity and variety, requiring new techniques and tools to analyze. Examples are given of the huge amounts of data generated daily by companies like Facebook, Twitter, and YouTube. The benefits of big data analytics are described as enabling better business decisions through hidden patterns, customer insights, and competitive advantages. The future of big data is promising, with the market expected to grow substantially in both revenue and jobs required to manage large amounts of data.
Big data is a term for datasets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.
Lets ideate and discuss more:
www.extentia.com/contact-us
The document discusses the advantages and disadvantages of big data. It begins by defining big data and noting some common misconceptions. The advantages of big data include its volume, variety, velocity, and potential value. However, the disadvantages include the resources needed to work with big data, the costs associated with it, security risks, and challenges in finding the right analytics tools.
Big Data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
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
The document provides guidance on big data analytics. It discusses why big data analytics is important for companies to drive performance and results. It defines big data analytics as using analytics on large, diverse datasets to discover insights faster. The document then gives examples of how big data analytics has helped companies by increasing revenue, decreasing costs and time to insight, and improving customer acquisition, retention, and security. It addresses common questions around whether to buy or build a big data analytics solution.
Big Data Analytics and a Chartered AccountantBharath Rao
Big Data Analytics is a growing field and currently being capitalized by many businesses. Businesses leverage on Big Data to gain a keen understanding of the Consumer Behavior and Market Understanding. Additionally Big Data can be used different fields such as Financial Audit, Control Assurance and Forensics.
This presentation is made to provide an insight regarding what opportunities reside for a Chartered Accountant in order to provide suitable value creation with regards to Big Data Analytics.
This presentation was made during my GMCS 2 Course at Mangalore branch of SIRC of ICAI and hence has limited number of slides.
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
Fundamentals of Big Data in 2 minutes!!Simplify360
In today’s world where information is increasing every second, BIG DATA takes up a major role in transforming any business.
Learn the fundamentals of big data in just 2 minutes!
Big Data Analytics Trends and Industry Predictions to Watch For in 2021Way2Smile
The fact that big data is going to change the face of major industries is widely accepted. But, what Data analytics trends should we watch out for? Let's find out!
Learn More at : https://bit.ly/2BOj4hD.
The term Big Data is commonly associated with the three Vs that define properties or dimensions, Volume, Variety and Velocity. Volume refers to the amount of data; variety relates to the number of types of data and velocity refers to the speed of data processing.
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.
This document discusses big data, providing definitions and outlining its key characteristics of volume, velocity, and variety. It describes processes involved like integrating disparate data stores and employing Hadoop MapReduce. Sources of big data are identified as mobile devices, sensors, social media, etc. Tools used include distributed servers, storage, and databases. Statistics on data generated by companies like Facebook and Twitter are provided. Applications of big data include improving science, healthcare, finance, and security. Advantages include access to vast information, while disadvantages include costs and privacy issues.
The mountain of Big Data is growing, presenting immense opportunities for businesses ready to summit its peak, but the journey requires careful preparation. Integra helps businesses equip their network infrastructure to handle big requirements for Big Data—with fully-symmetrical Ethernet solutions designed to deliver low-latency, high-bandwidth connectivity between organizational peers, the cloud, and the servers where your data is stored. Our infographic, "Summiting the Mountain of Big Data" will help you understand how big "Big Data" really is; who's producing, consuming, managing and storing all that data; the business advantages you can capture by tapping into its power; and how you can prepare your organization to meet its demands—resulting in Big Gains from Big Data.
Big data is very large data that is difficult to process using traditional methods. It is characterized by high volume, velocity, and variety. Examples of real-life big data implementations include using social media to understand customer behavior, tracking social media for marketing campaigns, and analyzing medical data to predict readmissions. Challenges include integrating diverse data sources and ensuring ethical access. Common techniques for processing big data are parallel database management systems and MapReduce frameworks like Hadoop.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
Index:
1) The Importance of Data
2) What is Big Data Concept
3) Big Data vs. Cloud Computing
4) The basic idea behind Big Data
5) Why do we use Big Data
6) Top 10 companies using Big Data
7) What kind of data is Big Data
8) Is Privacy a value
9) Future of Big Data by 2020
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
This document provides an overview of big data. It defines big data as large volumes of data that are high in velocity and variety, requiring new techniques and tools to analyze. Examples are given of the huge amounts of data generated daily by companies like Facebook, Twitter, and YouTube. The benefits of big data analytics are described as enabling better business decisions through hidden patterns, customer insights, and competitive advantages. The future of big data is promising, with the market expected to grow substantially in both revenue and jobs required to manage large amounts of data.
Big data is a term for datasets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.
Lets ideate and discuss more:
www.extentia.com/contact-us
The document discusses the advantages and disadvantages of big data. It begins by defining big data and noting some common misconceptions. The advantages of big data include its volume, variety, velocity, and potential value. However, the disadvantages include the resources needed to work with big data, the costs associated with it, security risks, and challenges in finding the right analytics tools.
Big Data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
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
The document provides guidance on big data analytics. It discusses why big data analytics is important for companies to drive performance and results. It defines big data analytics as using analytics on large, diverse datasets to discover insights faster. The document then gives examples of how big data analytics has helped companies by increasing revenue, decreasing costs and time to insight, and improving customer acquisition, retention, and security. It addresses common questions around whether to buy or build a big data analytics solution.
Big Data Analytics and a Chartered AccountantBharath Rao
Big Data Analytics is a growing field and currently being capitalized by many businesses. Businesses leverage on Big Data to gain a keen understanding of the Consumer Behavior and Market Understanding. Additionally Big Data can be used different fields such as Financial Audit, Control Assurance and Forensics.
This presentation is made to provide an insight regarding what opportunities reside for a Chartered Accountant in order to provide suitable value creation with regards to Big Data Analytics.
This presentation was made during my GMCS 2 Course at Mangalore branch of SIRC of ICAI and hence has limited number of slides.
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
Fundamentals of Big Data in 2 minutes!!Simplify360
In today’s world where information is increasing every second, BIG DATA takes up a major role in transforming any business.
Learn the fundamentals of big data in just 2 minutes!
Big Data Analytics Trends and Industry Predictions to Watch For in 2021Way2Smile
The fact that big data is going to change the face of major industries is widely accepted. But, what Data analytics trends should we watch out for? Let's find out!
Learn More at : https://bit.ly/2BOj4hD.
The term Big Data is commonly associated with the three Vs that define properties or dimensions, Volume, Variety and Velocity. Volume refers to the amount of data; variety relates to the number of types of data and velocity refers to the speed of data processing.
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.
This document discusses big data, providing definitions and outlining its key characteristics of volume, velocity, and variety. It describes processes involved like integrating disparate data stores and employing Hadoop MapReduce. Sources of big data are identified as mobile devices, sensors, social media, etc. Tools used include distributed servers, storage, and databases. Statistics on data generated by companies like Facebook and Twitter are provided. Applications of big data include improving science, healthcare, finance, and security. Advantages include access to vast information, while disadvantages include costs and privacy issues.
The mountain of Big Data is growing, presenting immense opportunities for businesses ready to summit its peak, but the journey requires careful preparation. Integra helps businesses equip their network infrastructure to handle big requirements for Big Data—with fully-symmetrical Ethernet solutions designed to deliver low-latency, high-bandwidth connectivity between organizational peers, the cloud, and the servers where your data is stored. Our infographic, "Summiting the Mountain of Big Data" will help you understand how big "Big Data" really is; who's producing, consuming, managing and storing all that data; the business advantages you can capture by tapping into its power; and how you can prepare your organization to meet its demands—resulting in Big Gains from Big Data.
Big data is very large data that is difficult to process using traditional methods. It is characterized by high volume, velocity, and variety. Examples of real-life big data implementations include using social media to understand customer behavior, tracking social media for marketing campaigns, and analyzing medical data to predict readmissions. Challenges include integrating diverse data sources and ensuring ethical access. Common techniques for processing big data are parallel database management systems and MapReduce frameworks like Hadoop.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
Big data offers opportunities for companies to gain competitive advantages through improved customer intimacy, product innovation, and operations. The document discusses how various companies are leveraging big data across industries. It notes that 45% of companies have implemented big data initiatives in the past two years and over 90% of Fortune 500 companies will have initiatives underway soon. Harnessing big data's potential requires understanding where it can create value within a company and having the right organizational structure, technology investments, and plan to capture those benefits.
This presentation offers a basic understanding of Big Data. It does this by defining Big Data, offers a History of Big Data, Big Data by the Numbers and the 8 Laws of Big Data
This document provides an introduction to a training course on big data analytics. It discusses why big data has become important due to the exponential growth in data volume, velocity, and variety. The course aims to focus on cloud-based storage and processing of big data using systems like HDFS, MapReduce, HBase and Storm. It emphasizes that learning involves actively asking questions. Big data is introduced by explaining the three V's of volume, velocity and variety. Examples of big data usage are given in areas like baseball analytics, political campaigns and election predictions. Challenges of big data integration and processing large volumes of heterogeneous data are also covered.
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
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.
Matt Asay presents at Strata 2013 on how NoSQL fits into the Big Data landscape, particularly how MongoDB and Hadoop work well together. Not an infomercial.
BIG DATA
Prepared By
Muhammad Abrar Uddin
Introduction
· Big Data may well be the Next Big Thing in the IT world.
· Big data burst upon the scene in the first decade of the 21st century.
· The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
· Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
What is BIG DATA?
· ‘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.
What is BIG DATA
· Walmart handles more than 1 million customer transactions every hour.
· Facebook handles 40 billion photos from its user base.
· Decoding the human genome originally took 10years to process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
(
Volume
Data
quantity
) (
Velocity
Data
Speed
) (
Variety
Data
Types
)
1st Character of Big Data
Volume
· A typical PC might have had 10 gigabytes of storage in 2000.
· Today, Facebook ingests 500 terabytes of new data every day.
· Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
· The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.
2nd Character of Big Data
Velocity
· Clickstreams and ad impressions capture user behavior at millions of events per second
· high-frequency stock trading algorithms reflect market changes within microseconds
· machine to machine processes exchange data between billions of devices
· infrastructure and sensors generate massive log data in real- time
· on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
· Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.
· Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure.
· Big Data analysis includes different types of data
Storing Big Data
· Analyzing your data characteristics
· Selecting data sources for analysis
· Eliminating redundant data
· Establishing the role of NoSQL
· Overview of Big Data stores
· Data models: key value, graph, document, column-family
· Hadoop Distributed File System
· H.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
This document discusses big data and characteristics of big data businesses. It notes that the amount of data created daily is growing exponentially and data has become a new economic input for businesses. Big data refers to large, complex data that is analyzed in real-time to unlock intelligence. The document outlines the history and components of big data including distributed storage, computation and tools like Hadoop. It presents a taxonomy of big data companies and discusses competitive barriers for these businesses like data network effects and economies of scale. Finally, it notes that successful big data teams require data science and scalable architecture skills.
This document discusses big data and data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional techniques due to their size. It outlines the 4 Vs of big data: volume, velocity, variety, and veracity. The proposed system would use distributed parallel computing with Hadoop to identify relationships in huge amounts of data from different sources and dimensions. It discusses challenges of big data like data location, volume, privacy, and gaining insights. Solutions involve parallel programming, distributed storage, and access restrictions.
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
With the computer revolution vast amount of digital data has become available. With the Internet and smart connected product, the data is growing exponentially. It is estimated that every year, more data is generated than all history prior. And this has repeated over several years.
With all this data, it becomes a platform for something new of its own. In this lecture, we look at what big data is and look at several examples of how to use data. There are many well-know algorithms to analyse data, like clustering and machine learning.
This document discusses data mining with big data. It defines big data and data mining. Big data is characterized by its volume, variety, and velocity. The amount of data in the world is growing exponentially with 2.5 quintillion bytes created daily. The proposed system would use distributed parallel computing with Hadoop to handle large volumes of varied data types. It would provide a platform to process data across dimensions and summarize results while addressing challenges such as data location, privacy, and hardware resources.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
This document provides an overview of big data, including its definition, characteristics, sources, tools, applications, risks, benefits and future. Big data is characterized by its volume, velocity and variety. It is generated from sources like users, applications, sensors and more. Tools like Hadoop and databases are used to store, process and analyze big data. Big data analytics can provide benefits across many industries and applications. However, it also poses risks around privacy, costs and skills that must be addressed. The future of big data is promising, with the market expected to grow significantly in the coming years.
Big Data: Beyond the Hype - Why Big Data Matters to YouDATAVERSITY
This document discusses big data and its importance. It notes that big data is more prevalent than many realize, with most companies and industries now dealing with large volumes of various types of data. It also explains that effectively managing big data provides competitive advantages, with data-savvy companies experiencing much stronger growth rates. Additionally, the document introduces DataStax Enterprise as a solution for easily and effectively managing big data at scale through its support for Apache Cassandra, analytics capabilities, visualization tools, and enterprise services.
Similar to Big Data for the Rest of Us - OpenWest 2014 - Matt Asay (20)
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
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A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
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Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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Big Data for the Rest of Us - OpenWest 2014 - Matt Asay
1. MongoDB Inc. Proprietary and Confidential
Big Data for the Rest of Us
VP, Marketing & Business Development, MongoDB
Matt Asay
2. 2
Not the Future
“The relational database market is a $9
billion a year market. I want to shrink it to
$3 billion and take a third of the market.”
- Marten Mickos
3. 3
This Is the Future
“The biggest category of winners is the Big Data
practitioners. These are the business people that have
identified opportunities to use data to create new
opportunities or disrupt legacy business models. We
think this opportunity is so profound, we believe that
the dividing line between winners and losers in the
business world over the next decade will hinge on a
company’s ability to leverage data as an asset.”
- Peter Goldmacher, Cowen & Co.
4. 4
What’s at Stake
Enable a Generation of Innovative,
Modern Applications Previously
Impossible Or Too Difficult to Achieve
9. 9
Volume Is Not the Problem
“Of Gartner's "3Vs" of big data (volume, velocity, variety), the
variety of data sources is seen by our clients as both the greatest
challenge and the greatest opportunity.”
- Forrester, 2014
* From Big Data Executive Summary of 50+ execs from F100, gov orgs
What are the primary data issues driving you to consider Big Data?*
Data Variety (68%)
Data Volume (15%)
Other Data (17%)
Diverse, streaming or new data types
Greater than 100TB
Less than 100TB
14. 14
• 90% of the world’s
data was created in
the last two years
• 80% of enterprise
data is unstructured
• Unstructured data
growing 2X faster
than structured
Time to Rethink the Solution
25. 25
Shouldn’t Be Penalized for Success
“Clients can also opt to run zEC12 without a raised
datacenter floor -- a first for high-end IBM mainframes.”
IBM Press Release 28 Aug, 2012
30. 30
The Data Scientist Is You
“Organizations already have people who know
their own data better than mystical data
scientists….Learning Hadoop is easier than
learning the company’s business.”
(Gartner, 2012)