![Understanding
Big Data
[ A Business Perspective ]
by
Shiva Dasharathi](https://image.slidesharecdn.com/understandingbigdataabusinessperspective-140302121810-phpapp01/85/Understanding-big-data-a-business-perspective-1-320.jpg)
Understanding big data, a business perspective
- 1. Understanding Big Data [ A Business Perspective ] by Shiva Dasharathi
- 2. 1: Big Data ? 3: How different it is from BI, HANA etc. ? 2: Benefits ? 4: Infrastructure? hadoop? Nosql? 5: Challenges in Migrating to big data?
- 3. 1: Big Data ? What is big data? Terminology Big Data evolution Anatomy of usecases
- 4. Big Data “Any Data that is worth analysing” Variety of data
- 5. Real time analysis : for, - Feedbacks, complaints about your products & services in social media -Capturing Customer behaviour -Predicting things before they happen Velocity & Volume of data
- 6. Big Data Characteristics of big data: 1. Volume of data 2. Velocity of Data 3. Variety of Data * Difficult to handle in traditional ways Complex
- 7. Big Data > Terminology Structured data: NAME NATURE TAG Shiva Thinking -- Forgetful Philosophy Shravanthi Innocent – Sensitive -- Journalist Champion Subhash Artistic -- Descretive Champion Shreyas Logical -- Passionate Champion Adithya Logical -- Articulative Champion Pallavi Outspeaking -- Friendly Champion Sonal Dancer -- Sportive Champion Nikhil Poetic -- Sportive Champion Gayathri Prude -- Honest Champion Anitha Blesser -- Gentle Champion Amandeep Aesthetic -- Independent Champion Malathi un known Champion Ankitha Cricketer -- Gentle Champion Vikram Logical -- Articulative Champion Ankesh Honest – Passionate Champion Tejo Managerial – Patience Leading CJ Quick – Logical Champion Deba Dedication – Honest Champion Charu Managerial – Independent Leading Ashok Social – Helping Leading Sijesh Analytical – Eager -- Helping Balanced Leading Tarun Shrewed – Responsive Leading Pavan Optimization – Shrewed Administrative Bhargav Balancing – Friendly Champion Surya Enthusiasm – Learning Versatile Swarnav Social – Outspeaking Administrative Bidisha Outspeakiing – Social Administrative Niranjan Articulative -- Friendly Leading
- 10. Big Data evolution Commodity/Cheap Hardware Open source software Valuable Data Data mining / Data analysis using statistical modelling techniques
- 11. Health care Banking Energy & Utilities Telecom Supply chain Retail Realestate Agriculture Sustainability etc.. Social networking web sites Search engines Job portals News portals Travel Recommendation Apps Online movie stores Animation industry etc.. Space research Bio research Image processing etc.. Enterprise Analytics Social media Apps & Analytics Research Oriented Fields Big Data > Anatomy of usecases?
- 12. 2: Benefits ? Advanced Predictions use of predictions
- 13. 2: Benefits ? Advanced Predictions Source 1 Source 2 Source 3 Knowledge Models Predictions: Revenue / spend forecasts; Sentiment analysis; Customer Behaviour analysis etc.. Data mining techniques
- 14. 2: Benefits ? use of predictions - Helps to understand & optimize the complex business processes - predict opportunities / risks - Understand strengths / weaknesses - Optimizing resource usages *At much cheaper costs.
- 15. 3: How different it is from traditional DBs? Big Data Vs RDBMS Big Data Vs BW, HANA
- 16. Big Data Vs RDBMS . Big Data tools -> - More for data Analysis - Lack Transaction system capabilities - Do not fully comply with A C I D properties - Doesn’t allow to apply constraints at data level * Do not compete with OLTP systems
- 17. Big Data Vs BW (or) HANA - Limited by Scalability (vertical scaling) -Can not deal with unstructured data -Not fault tolerant - Not well integrated with open source analytical / data mining tools
- 18. 4. Infrastructure? hadoop? Nosql? What is NOSQL What is Hadoop
- 19. What is Hadoop A distributed, parallel, data processing system (a type of NOSQL) map map map map reduce reduce Storage : HDFS Programming api: Mapreduce Well established Eco system: Hive, Pig, Hbase, Sqoop, oozy, Flume, Mahout, zookeeper etc… Input files Output files
- 20. What is NOSQL Nosql -> Not Only SQL Highly Available Distributed Fault tolerant Schema less NOSQL DBs: Key Value based: Hadoop Columnar based: Hbase, Cassandra Graph based: Neo4j, Orient DB Document based: Mango DB, Couch DB
- 21. 5: Challenges ? What to consider ? What data your organization has? Size & Sources? What Analytical usecases to be implemented ? What infrastructure you have? Which hardware to buy? What configuration? What NOSQL DBs you need ? Which vendor to approach? Migration Plan?
- 22. 1. What is Big Data? 2. How will I benefit? 3. I don’t have huge data, should I still consider big data? 4. I already have BI or HANA etc. setup, Can I leverage them? 5. What challenges may I face? 6. How much can I Save (Questions Covered in the session)