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Data Processing with Optimus

You're reading from Data Processing with Optimus Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801079563

Length 300 pages

Edition 1st Edition

Languages

Processing

Tools

Apache Spark

Concepts

Big Data

Authors (2):

Dr. Argenis Leon

Luis Aguirre Contreras

View More author details

Table of Contents (16) Chapters

Preface

1. Section 1: Getting Started with Optimus

2. Chapter 1: Hi Optimus! FREE CHAPTER

3. Chapter 2: Data Loading, Saving, and File Formats

4. Section 2: Optimus – Transform and Rollout

5. Chapter 3: Data Wrangling

6. Chapter 4: Combining, Reshaping, and Aggregating Data

7. Chapter 5: Data Visualization and Profiling

8. Chapter 6: String Clustering

9. Chapter 7: Feature Engineering

10. Section 3: Advanced Features of Optimus

11. Chapter 8: Machine Learning

12. Chapter 9: Natural Language Processing

13. Chapter 10: Hacking Optimus

14. Chapter 11: Optimus as a Web Service

15. Other Books You May Enjoy

Limitations

We are working hard to create a unified API from the most popular dataframe libraries. However, all the technologies are in different development stages. Many issues have been flying under the radar, but here, we want to highlight some of the most important ones.

Right now, the main limitations are as follows:

Creating a UDF for string processing in cuDF and Dask-cuDF since they are not supported yet: https://github.com/rapidsai/cudf/issues/7301.
cuDF, Dask-cuDF, and Vaex database connections are handled using Dask, which needs to load data as pandas dataframes and then convert them into the appropriate format based on a certain engine.
Regex cuDF support is limited. For example, it still can handle lowercase and uppercase characters at the same time: https://github.com/rapidsai/cudf/issues/5217.

The rest of the chapter is locked

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Authors (2)

Dr. Argenis Leon

Dr. Argenis Leon

Argenis Leon created Optimus, an open-source python library built over PySpark aimed to provide an easy-to-use API to clean, process, and merge data at scale. Since 2012, Argenis has been working on big data-related projects using Postgres, MongoDB, Elasticsearch for social media data collection and analytics. In 2015, he started working on Machine learning projects in Retail, AdTech, and Real Estate in Venezuela and Mexico. In 2019 he created Bumblebee, a low-code open-source web platform to clean and wrangle big data using CPU and GPUs using NVIDIA RAPIDS. Nowadays Argenis is Co-founder and CTO of boitas (backed by YCombinator) a wholesale marketplace for SMB in Latin America.

See other products by Dr. Argenis Leon

Luis Aguirre Contreras

Luis Aguirre Contreras

Luis Aguirre began working with web development projects for Mood Agency in 2018, creating sites for brands from all across Latin America. One year later he started working on Bumblebee, a low-code web platform to transform data that uses Optimus. In mid-2020 he started participating in the Optimus project as a core developer; focusing on creating the easiest-to-use experience for both projects. In 2021 he started working on the Optimus REST API, a tool to allow requests from the web focused on data wrangling.

See other products by Luis Aguirre Contreras

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