Big Data
Hello
Rezaur Rahman (Jitu)
CTO, G&R Ad Network
jitu@gandr.com.bd
@jituboss
Data is growing at a exponential rate and traditional tools like
RDBMS is not enough to process
Data is everywhere:
• Flickr (87 million registered members and 3.5 million
photos per day)
• YouTube (4B videos streamed ...
Data is growing at a 40% rate, reaching nearly 45 ZB by 2020
according to IDC
1 ZB is equal to 1 billion TB
What is Big Data and what is not?
• Order details of a e-commerce site
• All Orders across 1000s of e-commerce sites
• One...
What is Big Data?
3 v’s of Big Data
Types of Data:
• Relational Data (Tables/Transaction/Legacy
Data)
• Unstructured Data – Apache weblogs
• Text Data (Web)
•...
Data Processing Tasks:
• Aggregation and Statistics - Data warehouse
• Contextual Advertising – Real Time Bidding,
Remarke...
Traditional Architecture
• Relational Data is everything
– SQL
– Embedded
– Client-Server Based
• Data Stack
– Web, CDN, L...
Traditional Scalability
• Scale-up
– Memory And Hardware has limitations
• Scale-out
– Reading
• Cache is everything
– Que...
NoSQL Solution
• Lot of companies emerged to solve data problem
• Big Table: Google started to implement massively
distrib...
Big Data Tools
Big Data Landscape
Thanks
Questions?
Upcoming SlideShare
Loading in …5
×

Presentation at Google Day on Big Data

360 views
242 views

Published on

Published in: Data & Analytics, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
360
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
6
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Presentation at Google Day on Big Data

  1. 1. Big Data
  2. 2. Hello Rezaur Rahman (Jitu) CTO, G&R Ad Network jitu@gandr.com.bd @jituboss
  3. 3. Data is growing at a exponential rate and traditional tools like RDBMS is not enough to process
  4. 4. Data is everywhere: • Flickr (87 million registered members and 3.5 million photos per day) • YouTube (4B videos streamed per day) • Yahoo! Webmap (3 trillion links, 300TB compressed, 5PB disk) • Facebook is collecting your data 500 terabytes a day • Walmart handles more than 1 million customer transactions every hour • IDC Estimates that by 2020, business transactions on the internet- business-to-business and business-to- consumer – will reach 450 billion per day.
  5. 5. Data is growing at a 40% rate, reaching nearly 45 ZB by 2020 according to IDC 1 ZB is equal to 1 billion TB
  6. 6. What is Big Data and what is not? • Order details of a e-commerce site • All Orders across 1000s of e-commerce sites • One person’s voter ID information • Every citizen’s voter ID information dataset Simple Definition: Big Data is Data, that is too big to process with a single machine
  7. 7. What is Big Data?
  8. 8. 3 v’s of Big Data
  9. 9. Types of Data: • Relational Data (Tables/Transaction/Legacy Data) • Unstructured Data – Apache weblogs • Text Data (Web) • Semi-structured Data (XML) • Graph Data • Social Network, Semantic Web (RDF) • Streaming Data
  10. 10. Data Processing Tasks: • Aggregation and Statistics - Data warehouse • Contextual Advertising – Real Time Bidding, Remarketing • Indexing, Searching, and Querying - Keyword based search, Pattern recognition • Knowledge discovery - Data Mining, Statistical Modeling
  11. 11. Traditional Architecture • Relational Data is everything – SQL – Embedded – Client-Server Based • Data Stack – Web, CDN, Load Balancers, Application, Database and Storage
  12. 12. Traditional Scalability • Scale-up – Memory And Hardware has limitations • Scale-out – Reading • Cache is everything – Query Cache – Memcache • Pre-fetching, Replication – Writes • Redundant Disk Arrays, RAID • Sharding
  13. 13. NoSQL Solution • Lot of companies emerged to solve data problem • Big Table: Google started to implement massively distributed scalable system • Many companies followed building scale-out architecture using commodity hardware • ACID was termed as bad for scaling, so relaxed consistency model came • Google Big Table and Amazon Dynamo are notable
  14. 14. Big Data Tools
  15. 15. Big Data Landscape
  16. 16. Thanks
  17. 17. Questions?

×