Insight Data Science and Engineering Talk on Big Data for the Data For Action course at Stanford's Graduate School of Business

1. What is Big Data in a Nutshell?:
An Introduction to Problems and
Bottlenecks in Data Systems
Zach Gazak
David E Drummond
Insight Data Science & Engineering

3. Program mentors are data teams from top
technology companies including:
500+
Fellows
100+
Companies

4. Goals
• Understand what can be done with “Big Data” and
the scale of the data.
• Understand the hardware bottlenecks that dictate
the technology “stack”.
• Understand different stacks that are used for
different types of companies, and why.

5. Facebook is Data

6. Types of Data
• Audio / Visual:
Images and Videos
• Text: Comments,
Notes, Profile Content
• Interactions: Likes,
Friendships, Groups
• Site usage: Log in,
Scroll, Click, Post, etc.

7. Types of Data
• Audio / Visual:
Images and Videos
• Text: Comments,
Notes, Profile Content
• Interactions: Likes,
Friendships, Groups
• Site usage: Log in,
Scroll, Click, Post, etc.
Unstructured
Structured

8. How is it Used?
Business Intelligence / Analytics Customer engagement

9. How is it Used?
Research and Development
Product Iteration and Improvement

10. How is it Used?

11. How much data is there?
For Zach:
• ~1 MB per month
• Unstructured data only

12. How much data is there?
For 1.2 billion Zachs ~ 1.2 petabytes per month

13. How is this done?

14. Hardware basics

15. Various ports
(I/O)
up to ~ 10GB/s
CPU
(processor)
~ 1GHz
Hard Drive
(storage)
~ 250GB
RAM
(memory)
~ 8GB

16. Various ports
(I/O)
up to ~ 10GB/s
RAM
(memory)
~ 8GB
CPU
(processor)
~ 1GHz
Hard Drive (storage)
~ 250GB
Network Processing Storage

17. Bottlenecks in Data Systems
Proper data system design should consider these
limiting bottlenecks:
• Processing time by the CPU
• Loading data into the CPU and memory
• Finding data on the disk
• Reading data from the disk
• Moving data across the network

18. Bottlenecks: Processing Data
• All data that is processed must be loaded into the CPU
Disk Storage
Memory
CPU
Price
Speed

19. Bottlenecks: Processing Data
• All data that is processed must be loaded into the CPU
Disk Storage
Memory
CPU
Price
Speed
• Solution: Storage Hierachy, Supercomputers, Distributed Systems

20. Bottlenecks: Finding Data
• Finding a new file on disk (known as random seeks)
Actuator arm
with head that reads from disk
End of Desired File
Beginning of Desired File

21. Bottlenecks: Finding Data
• Finding a new file on disk (known as random seeks)
• Solution: SSD and structured databases for specific use cases
Actuator arm
with head that reads from disk
End of Desired File
Beginning of Desired File

22. Bottlenecks: Moving Data
• Moving data from machine to machine over a network

23. Bottlenecks: Moving Data
• Solution: Keeping data close to the processors (MapReduce)
• Moving data from machine to machine over a network

24. Bottlenecks: Example
• Processing a 2 kB transaction in memory, sequentially and
randomly on disk, or across the network
100 :1 200 :1 50 :1

25. Open Questions
• Will processors continue to improve?
• Are there new types of processing?
• What if memory replaced hard
disks?

26. Quantum Computing

27. GPU and Deep Learning

28. Memory Optimized

29. Tech Stacks for Companies
Depending on your growth plans:
• Single system with small data
• Distributed data center with large data
• Renting computers for flexibility (cloud)

30. Small Firms with Small Data
• Example: Small medical firm with slow growth
• Pros: Easy to maintain, data locality, inexpensive
• Cons: Difficult to grow quickly, risky, not ideal for analysis

31. Small Firms with Small Data
• Example: Small medical firm with slow growth
• Pros: Easy to maintain, data locality, inexpensive
• Cons: Difficult to grow quickly, risky, not ideal for analysis

32. Small Firms with Small Data

33. Large Firms with Stable Growth
• Example: Facebook with steadily growing data centers
• Pros: Economies of scale, redundancy, innovative design
• Cons: Upfront capital, dedicated maintenance
• >100 PB of Data
• 7 PB / Day
• 1 kW / TB
• ~$20 / TB / Month

34. Start-Ups with Exponential Growth
• Example: AirBnB - rent processing and storage from AWS
• Pros: Scales easily, no maintenance, no upfront capital
• Cons: Expensive in the long run, depend on data provider
• 50 GB / Day
• $20-50 / TB / Mo

35. Start-Ups with Exponential Growth
• Example: Netflix - AWS fails on Christmas Eve
• Con: You can rent the computers, but you own the failure

36. Questions?
• info@insightdatascience.com
• jzgazak@gmail.com
• david@insightdatascience.com