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Big data

  1. 1. BIG DATA -Mithilesh Joshi
  2. 2. WHAT IS BIG DATA?  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 has the potential to help companies improve operations and make faster, more intelligent decisions. The data is collected from a number of sources including emails, mobile devices, applications, databases, servers and other means. This data, when captured, formatted, manipulated, stored and then analyzed, can help a company to gain useful insight to increase revenues, get or retain customers and improve operations.
  3. 3.  Is Big Data a Volume or a Technology?  While the term may seem to reference the volume of data, that isn't always the case. The term Big Data, especially when used by vendors, may refer to the technology (which includes tools and processes) that an organization requires to handle the large amounts of data and storage facilities. The term is believed to have originated with web search companies who needed to query very distributed aggregations of loosely-structured data.  An Example of Big Data  An example of Big Data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people—all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on). The data is typically loosely structured data that is often incomplete and inaccessible.
  4. 4.  Business Datasets  When dealing with larger datasets, organizations face difficulties in being able to manipulate, and manage big data. Big Data is particularly a problem in business analytics because standard tools and procedures are not designed to search and massive datasets.  As research of QuinStreet demonstrates, big data initiatives are poised for explosive growth. QuinStreet surveyed 540 enterprise decision-makers involved in big data and found the datasets of interest to many businesses today include traditional structured databases of inventories, orders, and customer information, as well as unstructured from the Web, social networking sites, and intelligent devices.  Big Data may also be called enterprise Big Data or big data.
  5. 5. BOG EXAMPLES OF “BIG-DATA” The New York Stock Exchange generates about one terabyte of new trade data per day.
  6. 6. Social Media Impact Statistic shows that 500+terabytes of new data gets ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.
  7. 7. Single Jet engine can generate 10+terabytes of data in 30 minutes of a flight time. With many thousand flights per day, generation of data reaches up to many Petabytes.
  8. 8. TOOLS FOR “BIG-DATA”  1. Cassandra  This tool is widely used today because it provides an effective management of large amounts of data. It is a database that offers high availability and scalability without compromising the performance of commodity hardware and cloud infrastructure. Among the main advantages of Cassandra highlighted by the development are fault tolerance, performance, decentralization, professional support, durability, elasticity, and scalability. Indeed, such users of Cassandra as eBay and Netflix may prove them. 2. Hadoop Another great product from Apache that has be enused by many large corporations. Among the most important features of this advanced software library is superior processing of voluminous data sets in clusters of computers using effective programming models. Corporations choose Hadoop because of its great processing capabilities plus developer provides regular updates and improvements to the product.
  9. 9.  3. Plotly  Successful big data analytics use Plotly to create great dynamic visualization even in case if the company does not have sufficient time or skills for meeting big data needs. It makes the process of creating stunning and informative graphics very easy using the online tools. Also, the platform enables sharing the findings by transporting the results into different convenient formats.  4. Bokeh  Similarly to Plotly, this tool is also great for creating easy and informative visualizations. It is used for big data analytics experts to easily and quickly create interactive data applications, dashboards, and plots. Check out the gallery of the example works that were done with Bokeh using the big data. Many experts also say Bokeh is the most advanced visual data representation tool.
  10. 10.  5. Neo4j  The official website of the tool claims that it is the world’s leading graph database. Indeed, it is, because it takes the big data business to the next level: it helps to work with the connections between them. The connections between the data drive modern intelligent applications, and Neo4j is the tool that transforms these connections to gain competitive advantage. If you are looking for additional information about how you can gain a competitive advantage of utilizing a graph database  6. Cloudera  Businesses today use this tool for creating a data repository that can be accessed by all corporate users that need the data for different purposes. It was developed in 2008 and still is the most popular provider and supporter of Apache Hadoop. This combination is known to transform businesses and reducing business risks in order to give them a competitive advantage.
  11. 11.  7. OpenRefine  Need to explore voluminous data sets with ease? This tool allows the businesses to prepare everything for the data analysis. Simply saying, OpenRefine will help to organize the data in the database that was nothing but a mess. As the result, the users can begin to process the data with the computer.  8. Storm  This tool makes the list because of its superior streaming data processing capabilities in real time. It also integrates with many other tools such as Apache Slider to manage and secure the data. The use cases of Storm include data monetization, real time customer management, cyber security analytics, operational dashboards, and threat detection. These functions provide awesome business opportunities.
  12. 12.  9. Wolfram Alpha  Want to calculate or know something new about things?  Wolfram Alpha is an awesome tool to look for information about just about everything. Doug Smith from Proessaywriting says that his company uses this platform for advanced research of financial, historical, social, and other professional areas. For example, if you type “Microsoft,” you receive input interpretation, fundamentals and financials, latest trade, price history, performance comparisons, data return analysis, correlation matrix, and many other information.  10. Rapidminer  A big data specialist needs this open source data science platform, which functions through visual programming. It allows to manipulate, analyze, model, create models, and integrate the data into business processes.
  13. 13. DETAILED TOOL WITH PRICING.  Sisense  Sisense is the only business intelligence software that makes it easy for users to prepare, analyze and visualize complex data. Sisense provides an end-to-end solution for tackling growing data sets from multiple sources, that comes out-of-the-box with the ability to crunch terabytes of data and support thousands of users--all on a single commodity server. Sisense has already won over the hearts of some of the worlds leading, most data-intensive companies, including eBay, Henry Schein, NASA  They provide trial version and demo version, if you interested so they will sell that software on your needs.  For examples they will ask what is your project type, number of users, data volumes project timelines and etc,, depends on this information they will give estimated price.
  14. 14. SPSS  Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Watch IBM's free statistics video demo to learn how to easily access, manage and analyze data sets-without previous statistics experience; virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making.  It will provide trial version also  Paid information and plans :  1. IBM SPSS Statistics Standard  2. IBM SPSS Statistics Premium  3. IBM SPSS Statistics Professional  All plans starts with 99.00USD but it will increased by your needs.
  15. 15. ENVISION  Envision is a cloud data analytics platform for embedded, Big Data, and IoT applications. It provides secure and rapid free-form data visualization and exploration in real time, scalable throughout the enterprise.  Starting price - $10.00/month/user  And its also provide free trial.
  16. 16. DATAPLAY  By using DataPlay you can significantly cut your time spent on analysis and presentation of data. DataPlay Suite automates MS PowerPoint presentation generation through a set of data analysis, data visualization and data storage solutions. DataPlay applications boost the productivity of researchers by empowering them with intuitive tools for automated SPSS data visualization. All DataPlay applications are securely stored in DataPlay Cloud, which enables users to securely share the information.  Starting price - $140.00/month/user  Its also have free trial and free version.
  17. 17. IMAGES & VIDEOS: WITH BIG DATA  The human brain simultaneously processes millions of images, movement, sound and other esoteric information from multiple sources. The brain is exceptionally efficient and effective in its capacity to prescribe and direct a course of action and eclipses any computing power available today. Smartphones now record and share images, audios and videos at an incredibly increasing rate, forcing our brains to process more.  Technology is catching up to the brain. Google’s image recognition in “Self-taught Software” is working to replicate the brain’s capacity to learn through experience. In parallel, prescriptive analytics is becoming far more intelligent and capable than predictive analytics. Like the brain, prescriptive analytics learns and adapts as it processes images, videos, audios, text and numbers to prescribe a course of action.
  18. 18. IMAGE ANALYTICS: TECHNOLOGY PROCESS  Image analytics is the automatic algorithmic extraction and logical analysis of information found in image data using digital image processing techniques. The use of bar codes and QR codes are simple examples, but interesting examples are as complex as facial recognition and position and movement analysis.  Today, images and image sequences (videos) make up about 80 percent of all corporate and public unstructured big data. As growth of unstructured data increases, analytical systems must assimilate and interpret images and videos as well as they interpret structured data such as text and numbers.
  19. 19.  An image is a set of signals sensed by the human eye and processed by the visual cortex in the brain creating a vivid experience of a scene that is instantly associated with concepts and objects previously perceived and recorded in one’s memory. To a computer, images are either a raster image or a vector image. Simply put, raster images are a sequence of pixels with discreet numerical values for color; vector images are a set of color-annotated polygons. To perform analytics on images or videos, the geometric encoding must be transformed into constructs depicting physical features, objects and movement represented by the image or video. These constructs can then be logically analyzed by a computer.
  20. 20. TOOLS FOR IMAGE AND VIDEO PROCESSING.
  21. 21. PENTAHO  Within a single platform, our solution provides big data tools to extract, prepare and blend your data, plus the visualizations and analytics that will change the way you run your business. From Hadoop and Spark to NoSQL, Pentaho allows you to turn big data into big insights.  It is freeware but you should mail them for demo and they will provide you link.
  22. 22. AZURE  Azure HDInsight offers fully managed and supported 100% Apache Hadoop®, Spark, HBase and Storm clusters. You can get up and running quickly on any of these workloads with a few clicks and within a few minutes without buying hardware or hiring specialised operations teams typically associated with big data infrastructure.  Starting price ₹9,899rs to ₹1,32,380.75/month
  23. 23. IMPORT.IO  Import.io is the number one tool for data extraction. Import.io enables users to convert websites into structured, machine readable data with no coding required. Using a simple point and click UI, we take a webpage and transform it into an easy to use spreadsheet that you can then analyze, visualize, and use to make data-driven decisions. Features include Authenticated Extractions behind a login, flexible scheduling, and fully documented public APIs. Customers use the data for machine learning, market and academic research, lead generation, app development, and price monitoring.  Essential-$299 Queries expire after 1 month  Professional- $1,999  Enterprise-$4,999
  24. 24. THANK YOU.  For more information visit www.mithileshjoshi.blogspot.com

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