Data warehouse Vs Big Data
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This document compares and contrasts data warehouses and big data. It discusses how big data has evolved from data warehousing technologies and involves new technologies like Hadoop and MapReduce. While data warehouses ensure consistent decision making using prior hypotheses, big data uses statistics to extract new hypotheses from very large and diverse datasets, including clickstream logs, sensor data, social media, and more. Both hard and soft data sources are important for businesses to analyze and extract value from immense and growing amounts of information.
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General outline created by SKILLWISE about BIGDATA, which gives you a clear idea about what is bigdata?
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This document provides an overview of big data analytics. It discusses the characteristics of big data, known as the 5 V's: volume, velocity, variety, veracity, and value. It describes how Hadoop has become the standard for storing and processing large datasets across clusters of servers. The challenges of big data are also summarized, such as dealing with the speed, scale, and inconsistencies of data from a variety of structured and unstructured sources.
DataScienceIntroduction.pptx
The document discusses key concepts related to data science including:
- The difference between data and information
- An overview of the volume, velocity, variety, and veracity (4 V's) of big data
- The steps involved in data analytics from data collection to model building
- Applications of data science in various industries like retail, manufacturing, and insurance
Introduction to Big Data
Every day we roughly create 2.5 Quintillion bytes of data; 90% of the worlds collected data has been generated only in the last 2 years. In this slide, learn the all about big data
in a simple and easiest way.
Big data data lake and beyond
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
Thilga
This document provides an introduction to big data including:
- An overview of what big data is and the challenges it presents in terms of capture, curation, storage, search, sharing, transfer, analysis and visualization of large, complex datasets.
- The 3Vs of big data - volume, velocity and variety - and examples of the scale of data being generated every day from sources like social media, sensors and scientific instruments.
- The technologies and architectural approaches needed to harness big data including Hadoop, Spark, data warehouses, graph databases, and cloud computing platforms.
Big Data By Vijay Bhaskar Semwal
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.
Presentation on Big Data
Big data is large and complex data that cannot be processed by traditional data management tools. It is characterized by high volume, velocity, and variety. Big data comes from many sources and in many formats, including structured, unstructured, and semi-structured data. Storing and processing big data requires specialized systems like Hadoop and NoSQL databases. Big data analytics can provide benefits like improved business decisions and customer satisfaction when applied to areas such as healthcare, security, and manufacturing. However, big data also presents risks regarding privacy, costs, and being overwhelmed by the volume of data.
Big data.pptx
This document discusses an introduction to data and big data. It defines data and differentiates it from information. It then discusses the volume, variety, and velocity characteristics of big data known as the 3Vs. The document contrasts big data with small data and notes the challenges of working with big data due to its scale. Finally, it briefly mentions some major players in big data like Google and Hadoop and lists some common tools used for big data.
Big data
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software are commonly used to store and analyze big data across large clusters of commodity servers in a scalable, cost-effective manner.
Big data
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software are commonly used to store and analyze big data across clusters of commodity servers in a scalable, cost-effective manner.
Big data
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software are commonly used to store and analyze big data across large clusters of commodity servers in a scalable, cost-effective manner.
Big data
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software platform provide scalable and cost-effective infrastructure for storing and processing big data across commodity servers in a cluster.
Big data
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software are commonly used to store and analyze big data across clusters of commodity servers in a scalable, cost-effective manner.
Big data
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software are commonly used to store and analyze big data across large clusters of commodity servers in a scalable, cost-effective manner.
Big data
Big data is large and complex data sets that are difficult to process using traditional data management tools. There are four characteristics of big data: volume, velocity, variety, and veracity. Volume refers to the large amount of data being collected and stored. Velocity refers to the speed at which data is being generated and processed. Variety means data comes in all types of formats. Veracity refers to the noise and biases present in the data. Many sources are generating big data including social media, scientific instruments, mobile devices, and sensor technology. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software platform provide
Data mining with big data
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.
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Big Data, NoSQL, NewSQL & The Future of Data Management
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Data warehouse Vs Big Data
- 1. vs Big Data Big Data Big Data Lisette Zounon , CC, ALB Advanced Manual : Technical Presentations Project #4 : Presenting a Technical Paper Time : 10-12 minutes
- 2. Deliver business value through the analysis of data Data Warehouse or Big Data
- 3. A taxonomy of data mind over matter
- 4. Matter Data • Data from physical world . • Measurement data : sourced from various sensors connected to computers and internet . • Atomic data : comprised of physical events specific to human interactions. • Derived data: from mathematical manipulation of atomic data , create for meaningful view of business information to humans.
- 5. Mind Data • Soft information originating from the way , humans perceive the world and interact socially within it . • Multiplex data : image , video and audio information , very large files . • Textual data : suited for statistical analysis • Compounded data : hard and soft information , metadata is a significant subset . social media information
- 6. Data Warehouse • Originated from relational database • Data are sourced solely from other databases within the organization • Financial systems , customer informations and billing informations etc..
- 8. Analytic database Better performance , faster query , efficient reporting.
- 10. Data sizes constantly moving target, as big as you want , getting ever larger. Allow business to analyze and extract business value from the immense data sets .
- 11. clickstream logs , sensor data , location data ,customer support emails , chat transcripts ,surveillance video, etc..
- 12. Big data components • Big data is in many ways evolution of data warehousing. • New technologies : Hadoop , MapReduce , NoSQL queries or databases .
- 14. Data warehouse Vs Big data • Data warehouse : ensure consistent and trusted decision making • Business intelligence works more from prior hypotheses , whereas big data uses statistics to extract hypotheses .
- 15. Data warehouse vs Big data • Single logical storehouse • Significant overlap in the world of matter : Meaning • Big Data is the superset of information and processes that have characterized data warehousing .
- 16. Conclusion Data to make effective decision making Consistent and trusted information Hard and soft information highly complimentary and mandatory for all forward looking businesses.
- 17. • References • http://strata.oreilly.com/2011/01/data-warehouse- big-data.html • oracle database 12c for Data warehousing and Big Data
- 18. – Criss Jami, Venus in Arms “In the age of technology there is constant access to vast amounts of information. The basket overflows; people get overwhelmed; the eye of the storm is not so much what goes on in the world, it is the confusion of how to think, feel, digest, and react to what goes on."
- 19. Thank you . end
Editor's Notes
- meseaurement data: location , velocity , flow rate , event count , chemical signal and many more , widely used in science and engineerin applications . When such data is combined in a useful way , it becomes a commercial data Atomic data : set of location , G force , measurements in a specific pattern and time , record from atm transaction , financial institutions , web retailers , customer behavior Derived data ; banking transactions to create account status and balance information
- data not captured in operational databeses