Slides from my talk at Big Data Spain 2014 in Madrid. In this talk, we will discuss our approach to bring large scale deep analytics to the masses. R is an extremely popular numerical computer environment, but scientific data processing frequently hits its memory limits. On the other hand, system to execute data intensive tasks like Hadoop or Stratosphere are not popular among R users because writing programs using these paradigms is cumbersome. We present an innovative approach to overcome these limitations using the Stratosphere/Apache Flink big data platform by means of a R package and ready-to-use distributed algorithm. This solution allows the user, with small modifications in the R code, to easily execute distributed scenarios using popular machine learning techniques. We will cover the implementation details of the proposed solution including the architecture of the system, the functionality implemented and working examples. In addition, we will cover what are the differences between our approach and other solutions that integrate R with Hadoop or other large-scale analytics systems. Finally, the results of the performance tests show that this solution is competitive with the already existing R implementations for small amounts of data and able to scale-up to gigabyte level.

1. Big Data Analytics
to the masses
Why it has failed and how we can fix it
Jose Luis Lopez Pino @jllopezpino

2. Who am I?
BI Consultant
Large-Scale & Distributed
Founding
Data Engineer

3. Big Data is like Tourism
But if you aren’t an expert,
you can’t make the most of it
It seems easy to do

4. Struggle to analyze Big Data
Harlan Harris, Sean Murphy, and Marck Vaisman. Analyzing the Analyzers: An Introspective Survey of Data
Scientists and Their Work. O’Reilly Media, Inc., 2013
Also: Sean Kandel, Andreas Paepcke, Joseph M Hellerstein, and Jeffrey Heer. Enterprise data analysis and
visualization: An interview study. Visualization and Computer Graphics, IEEE Transactions

5. Tools
Volker Markl. Breaking the chains: On declarative data analysis and data independence in the big data era.
Proceedings of the VLDB Endowment, 7(13), 2014

6. Tools (Now)
Original: Volker Markl. Breaking the chains: On declarative data analysis and data independence in the
big data era. Proceedings of the VLDB Endowment, 7(13), 2014

7. Deep analytics

8. We need libraries...
Libraries!
Query languages
Write your own
MR/RDD/Transformations

9. … comprehensive ones!

10. Say it with memes!
When you do
Deep analytics in small data
using R and CRAN packages
When you do
deep analytics in BIG data
using R and CRAN packages

11. When you try to program it
using MapReduce
When you try to program it
using Apache Spark /
Apache Flink
When you try to use a library
scalable to large data sets

12. Can’t we do it better?
- Make it similar to normal R
programs.
- Hide complexity.
- Make file manipulation easier.
- Part of the computing in the
cluster and part of the
computer in the client.

13. Our approach

14. Our approach

15. Behind the scenes: Before

16. Behind the scenes: After

17. Without writing significantly different code

18. Competitive or even faster than R native code in small data

19. Competitive even in highly iterative programs in small data

20. And it scales

21. Some relevant findings
- Transmission time was not significant.
- Stratosphere/Flink was competitive even in
small datasets.
- Changes in the code were required.
- Ensemble scenarios are the most exciting
ones.

22. 4 Takeaways from this talk
- We still need to bring Big Data to the right
people in the right place.
- We need comprehensive libraries.
- We need to move data back and forth.
- Use a syntax that the users are familiar with.

23. That’s all!
- Have you found this talk interesting?
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Senior Analyst)
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