1. Big Data

3. What is Big Data? Analog starage vs digital. The FOUR V’s of Big Data. Who’s Generating Big Data The importance of Big Data. Optimalization HDFC

4. Definition Big data is the term for 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. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.

6. The FOUR V’s of Big Data From traffic patterns and music downloads to web history and medical records, data is recorded, stored, and analyzed to enable that technology and services that the world relies on every day. But what exactly is big data be used? According to IBM scientists big data can be break into four dimensions: Volume, Velocity, Variety and Veracity.

7. The FOUR V’s of Big Data

8. Volume. Many factors contribute to the increase in data volume. Transaction-based data stored through the years. Unstructured data streaming in from social media. Increasing amounts of sensor and machine-to-machine data being collected. In the past, excessive data volume was a storage issue. But with decreasing storage costs, other issues emerge, including how to determine relevance within large data volumes and how to use analytics to create value from relevant data.

9. The FOUR V’s of Big Data

10. Variety. Data today comes in all types of formats. Structured, numeric data in traditional databases. Information created from line-of-business applications. Unstructured text documents, email, video, audio, stock ticker data and financial transactions. Managing, merging and governing different varieties of data is something many organizations still grapple with.

11. The FOUR V’s of Big Data

12. Velocity. Data is streaming in at unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time. Reacting quickly enough to deal with data velocity is a challenge for most organizations.

13. The FOUR V’s of Big Data

14. Veracity - Big Data Veracity refers to the biases, noise and abnormality in data. Is the data that is being stored, and mined meaningful to the problem being analyzed. Inderpal feel veracity in data analysis is the biggest challenge when compares to things like volume and velocity. In scoping out your big data strategy you need to have your team and partners work to help keep your data clean and processes to keep ‘dirty data’ from accumulating in your systems.

15. Who’s Generating Big Data Social media and networks (all of us are generating data) Scientific instruments (collecting all sorts of data) Mobile devices (tracking all objects all the time) Sensor technology and networks (measuring all kinds of data) The progress and innovation is no longer hindered by the ability to collect data But, by the ability to manage, analyze, summarize, visualize, and discover knowledge from the collected data in a timely manner and in a scalable fashion 15

16. The importance of Big Data The real issue is not that you are acquiring large amounts of data. It's what you do with the data that counts. The hopeful vision is that organizations will be able to take data from any source, harness relevant data and analyze it to find answers that enable: Cost reductions Time reductions New product development and optimized offerings Smarter business decision making

18. The importance of Big Data For instance, by combining big data and high-powered analytics, it is possible to: Determine root causes of failures, issues and defects in near-real time, potentially saving billions of dollars annually. Optimize routes for many thousands of package delivery vehicles while they are on the road. Analyze millions of SKUs to determine prices that maximize profit and clear inventory. Generate retail coupons at the point of sale based on the customer's current and past purchases. Send tailored recommendations to mobile devices while customers are in the right area to take advantage of offers. Recalculate entire risk portfolios in minutes. Quickly identify customers who matter the most. Use clickstream analysis and data mining to detect fraudulent behavior

19. HDFS / Hadoop Data in a HDFS cluster is broken down into smaller pieces (called blocks) and distributed throughout the cluster. In this way, the map and reduce functions can be executed on smaller subsets of your larger data sets, and this provides the scalability that is needed for big data processing. The goal of Hadoop is to use commonly available servers in a very large cluster, where each server has a set of inexpensive internal disk drives.

20. PROS OF HDFS Scalable – New nodes can be added as needed, and added without needing to change data formats, how data is loaded, how jobs are written, or the applications on top. Cost effective – Hadoop brings massively parallel computing to commodity servers. The result is a sizeable decrease in the cost per terabyte of storage, which in turn makes it affordable to model all your data. Flexible – Hadoop is schema-less, and can absorb any type of data, structured or not, from any number of sources. Data from multiple sources can be joined and aggregated in arbitrary ways enabling deeper analyses than any one system can provide. Fault tolerant – When you lose a node, the system redirects work to another location of the data and continues processing without missing a beat.

24. Sources McKinsey Global Institute Cisco Gartner EMC, SAS IBM MEPTEC

25. Thank you for your attention. Authors: Tomasz Wis Krzysztof Rudnicki