The document discusses the challenges of big data including volume, velocity, variety, and veracity. It proposes using PostgreSQL and Hadoop together as a solution, with PostgreSQL serving as a multi-model database server that can handle structured, semi-structured, and unstructured data in relational, object-relational, nested-relational, array, key-value, document, and range formats. Hadoop is described as a distributed file structure that can perform ETL to convert unstructured to structured data for analytics, easily handle unstructured data like logs and streaming data, and enable parallel processing via MapReduce at the petabyte scale. The takeaway is that together, PostgreSQL and Hadoop can solve "most" big

1. Two Elephants
in The Room!!
Denish Patel
Database Architect

2. Challenge
Big Data !

3. Challenge Simplified
1. Volume
a. Size
2. Velocity
a. streaming
b. response time
3. Variety
a. structured
b. semi structured
c. unstructured
4. Veracity
a. uncertainty
b. truthfulness

4. Solution
Sqoop

5. PostgreSQL
Multi Model Database Server
● Relational
● Object Relational
● Nested Relational
● Array Stores
● Key-Value Store (hstore)
● Document Store (XML,JSON)
● Range Types ( PostgreSQL 9.2)

6. Hadoop
A Distributed File Structure
● ETL
○ Helps to convert unstructured data into Structured
data for Analytics
● Ease of working on unstructured data
○ Log Processing
○ Data streaming in real time
● Parallel Processing
○ MapReduce
● Scale at Petabytes

7. Take away?
Two Elephants (PostgreSQL and Hadoop)
works together very well & can solve "most" of
the Big data challenges.