Data Science Without Borders (JupyterCon 2017)
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Wes McKinney discusses the evolution and future of data science in a presentation from JupyterCon 2017, highlighting the transition from closed to open source over the last decade. He emphasizes the need for standardized in-memory formats and zero-copy interchange across programming languages to enhance data accessibility and computation efficiency. The presentation also introduces Apache Arrow as a solution for flexible data manipulation and a community-driven project for optimization.
Data Science Without Borders (JupyterCon 2017)
- 1. Wes McKinney @wesmckinn DATA SCIENCE WITHOUT BORDERS WES MCKINNEY @WESMCKINN JupyterCon | August 2017
- 2. ME 2
- 3. I M P O R TA N T L E G A L I N F O R M AT I O N • The information presented here is offered for informational purposes only and should not be used for any other purpose (including, without limitation, the making of investment decisions). Examples provided herein are for illustrative purposes only and are not necessarily based on actual data. Nothing herein constitutes: an offer to sell or the solicitation of any offer to buy any security or other interest; tax advice; or investment advice. This presentation shall remain the property of Two Sigma Investments, LP (“Two Sigma”) and Two Sigma reserves the right to require the return of this presentation at any time. • Some of the images, logos or other material used herein may be protected by copyright and/or trademark. If so, such copyrights and/or trademarks are most likely owned by the entity that created the material and are used purely for identification and comment as fair use under international copyright and/or trademark laws. Use of such image, copyright or trademark does not imply any association with such organization (or endorsement of such organization) by Two Sigma, nor vice versa. • Copyright © 2017 TWO SIGMA INVESTMENTS, LP. All rights reserved Wes McKinney @wesmckinn 3
- 4. THINKING ON THE LAST 10 YEARS 4 2007 2017
- 5. CLOSED SOURCE OPEN SOURCE 5
- 6. A shared front-end for data science
- 7. THE NEXT 10 YEARS AND BEYOND 7 2017 2027 …
- 8. THE AI ARMS RACE Wes McKinney @wesmckinn 8
- 10. T DATA SCIENCE “LANGUAGE “SILOS” FRONT-END PYTHON R JVM JULIA … 10
- 11. WHAT’S IN A SILO? STORAGE / DATA ACCESS DATA STRUCTURES / IN-MEMORY FORMATS GENERAL COMPUTE ENGINE(S) ADVANCED ANALYTICS 11
- 12. WHAT’S IN A SILO? STORAGE / DATA ACCESS DATA STRUCTURES / IN-MEMORY FORMATS GENERAL COMPUTE ENGINE(S) ADVANCED ANALYTICS pandas NumPy pandas NumPy pandas scikit-learn 12
- 13. RENOVATING PANDAS Wes McKinney @wesmckinn 13
- 14. T MAKING THE SILOS “SMALLER” FRONT-END PYTHON R JVM JULIA ? … 14
- 15. PROGRAMMING LANGUAGES AS USER INTERFACES 15
- 16. GRAPHIC: Iceberg under sea (only top part visible to naked eye)
- 17. T df <- read_csv(…) df % group_by(…) % summarise(…) df = read_csv(…) df.groupby(…).aggregate(…) PYTHON R SAME ANALYSIS, DIFFERENT IMPLEMENTATION 17
- 18. T A SHARED RUNTIME FOR DATA SCIENCE FRONT-END PYTHON R JVM JULIA SHARED DATA SCIENCE RUNTIME … 18
- 19. FROM IDEA TO ACTION 19
- 20. T PART 1: STANDARD IN-MEMORY FORMAT R PYTHON JVM PORTABLE DATA FRAME Non-Portable Data Frames 20…
- 21. T PART 2: ZERO COPY INTERCHANGE RPYTHON JVM SHARED MEMORY + STANDARD MEMORY FORMATS … 21
- 22. T PART 3: HIGH PERFORMANCE DATA ACCESS BINARY COLUMNAR CSV SQL PORTABLE DATA FRAME Storage Formats/ Databases … 22
- 23. T PART 4: FLEXIBLE COMPUTATION ENGINE • Zero-overhead User-defined Functions • Portable Operator “Graphs” • “Embeddable” in Larger Systems 23
- 24. APACHE ARROW Language-agnostic Data Frame Format Zero-Copy Interchange 24
- 25. T BUILDING THE ARROW FORMAT • “Superset” of representations supported by R, pandas, SQL engines • Optimized for CPU cache affinity • ASF Governance: Open + Transparent Community Project 25
- 26. FEATHER: MINIMALIST ARROW ON DISK
- 27. Some Arrow OSS Users Feather Format Ray Project 27
- 30. Wes McKinney @wesmckinn THANK YOU WES MCKINNEY @WESMCKINN Apache Arrow: http://arrow.apache.org