%0 Book %1 daniel2018science %A Daniel, Jesse C. %B Manning Early Access Program %D 2018 %I Manning %K 62-08-computational-methods-for-problems-for-statistics 65-04-numerical-analysis-software-source-code 65y05-parallel-computation 68q85-models-and-methods-for-concurrent-and-distributed-computing dask python %N 1 %T Data Science at Scale with Python and Dask %U https://www.manning.com/books/data-science-at-scale-with-python-and-dask %X If you're doing data analysis using Pandas, NumPy, or Scikit, you know about THE WALL. At some point, you need to introduce parallelism to your system to handle larger-scale data or analytics tasks. The problem with THE WALL is that it can require you to rewrite your code, redesign your system, or start all over using an unfamiliar technology like Spark or Flink. Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. It's built to help you parallelize your data tasks on a standalone system, a cluster, or even a massive supercomputer without radically changing the way you work. %@ 9781617295607

@book{daniel2018science, abstract = {{If you're doing data analysis using Pandas, NumPy, or Scikit, you know about THE WALL. At some point, you need to introduce parallelism to your system to handle larger-scale data or analytics tasks. The problem with THE WALL is that it can require you to rewrite your code, redesign your system, or start all over using an unfamiliar technology like Spark or Flink. Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. It's built to help you parallelize your data tasks on a standalone system, a cluster, or even a massive supercomputer without radically changing the way you work.}}, added-at = {2019-03-01T00:11:50.000+0100}, author = {Daniel, Jesse C.}, biburl = {https://www.bibsonomy.org/bibtex/2d69b5c022f58c86f7020c39f810de787/gdmcbain}, citeulike-article-id = {14595536}, citeulike-attachment-1 = {Data_Science_at_Scale_with_Python_and_Da_v1_MEAP.pdf; /pdf/user/gdmcbain/article/14595536/1137657/Data_Science_at_Scale_with_Python_and_Da_v1_MEAP.pdf; cf06f7dff8ebf9bb2ed0e316a7377518f6a023af}, citeulike-linkout-0 = {https://www.manning.com/books/data-science-at-scale-with-python-and-dask}, citeulike-linkout-1 = {https://forums.manning.com/forums/data-science-at-scale-with-python-and-dask}, comment = {(private-note)MEAP 2018-05-29}, file = {Data_Science_at_Scale_with_Python_and_Da_v1_MEAP.pdf}, interhash = {1a5a27b94b9451cd1be76f6c520aa6b9}, intrahash = {d69b5c022f58c86f7020c39f810de787}, isbn = {9781617295607}, keywords = {62-08-computational-methods-for-problems-for-statistics 65-04-numerical-analysis-software-source-code 65y05-parallel-computation 68q85-models-and-methods-for-concurrent-and-distributed-computing dask python}, number = 1, posted-at = {2018-05-29 02:00:00}, priority = {4}, publisher = {Manning}, series = {Manning Early Access Program}, timestamp = {2023-01-26T23:07:03.000+0100}, title = {Data Science at Scale with {P}ython and {D}ask}, url = {https://www.manning.com/books/data-science-at-scale-with-python-and-dask}, year = 2018 }