Scalable Scientific Computing with Dask

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Dask Tutorial at PyConDE / PyData Karlsruhe 2018. These were the introductory slides that mainly contain the link to Matthew Rocklin's Dask workshop at PyData NYC 2018 whereon this workshop was based.

Datos y análisis

1
PyCon.DE / PyData Karlsruhe 2018
Uwe L. Korn
Scalable Scientific Computing with
Dask

2
• Senior Data Scientist at Blue Yonder
(@BlueYonderTech)
• Apache {Arrow, Parquet} PMC
• Data Engineer and Architect with heavy
focus around Pandas
About me
xhochy
mail@uwekorn.com

3
• Execution and definition of task graphs
• a parallel computing library that scales the existing Python ecosystem.
• scales down to your laptop laptop
• sclaes up to a cluster
What is Dask?

4
• multi-core and distributed parallel execution
• low-level: task schedulers for computation graphs
• high-level: Array, Bag and DataFrame
More than a single CPU

5
Dask is
• More light-weight
• In Python, operates well with C/C++/Fortran/LLVM or other natively
compiled code
• Part of the Python ecosystem
What about Spark?

6
Spark is
• Written in Scala and works well within the JVM
• Python support is very limited
• Brings its own ecosystem
• Able to provide more higher level optimizations
What about Spark?

https://github.com/mrocklin/
pydata-nyc-2018-tutorial
7

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Scalable Scientific Computing with Dask

1. 1 PyCon.DE / PyData Karlsruhe 2018 Uwe L. Korn Scalable Scientific Computing with Dask

2. 2 • Senior Data Scientist at Blue Yonder (@BlueYonderTech) • Apache {Arrow, Parquet} PMC • Data Engineer and Architect with heavy focus around Pandas About me xhochy mail@uwekorn.com

3. 3 • Execution and definition of task graphs • a parallel computing library that scales the existing Python ecosystem. • scales down to your laptop laptop • sclaes up to a cluster What is Dask?

4. 4 • multi-core and distributed parallel execution • low-level: task schedulers for computation graphs • high-level: Array, Bag and DataFrame More than a single CPU

5. 5 Dask is • More light-weight • In Python, operates well with C/C++/Fortran/LLVM or other natively compiled code • Part of the Python ecosystem What about Spark?

6. 6 Spark is • Written in Scala and works well within the JVM • Python support is very limited • Brings its own ecosystem • Able to provide more higher level optimizations What about Spark?

7. https://github.com/mrocklin/ pydata-nyc-2018-tutorial 7

Scalable Scientific Computing with Dask

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