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Building Big Data Processing and Visualization Pipeline through Apache Zeppelin

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Duen Horng ChauAuthors Info & Claims

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

Article No.: 57, Pages 1 - 7

https://doi.org/10.1145/3219104.3229288

Published: 22 July 2018 Publication History

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Abstract

Big data analytics pipeline becomes popular for large volume data processing, Apache Zeppelin provides an integrated environment for data ingestion, data discovery, data analytics and data visualization and collaboration with an extended framework which allows different programming languages and data processing back ends to be plugged in. The supported languages include Scala, Python, SQL, and Shell script as well as big data processing back ends including Hadoop, Spark and Hive. With the necessary tool sets, an interactive and dynamic data analysis can be done on the fly with heterogeneous programming interfaces. Although Zeppelin is great for code development and interactive analysis with small scale data set for proof-of-concept or use-case presentations, running the data processing pipeline in the batch mode is still needed for performance, robustness to fit in an automated workflow in some cases. We are developing a tool to convert Zeppelin notebook into a workflow with a set of codes that can run in a batch mode through command line interface without requiring running Zeppelin, so that the prototype code can be seamlessly deployed on the production cluster after demo stage. The entire workflow can be preserved, configured manually and run automatically. Zeppelin also provides a flexible way to integrate the visualization functionality, another contribution of this paper is to extend the Zeppelin's existing built-in visualization component for D3Network. With two added features described above, Zeppelin can help users to develop big data pipeline and visualizing graph data quickly and efficiently.

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Index Terms

Building Big Data Processing and Visualization Pipeline through Apache Zeppelin
1. Human-centered computing
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Published In

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

July 2018

652 pages

ISBN:9781450364461

DOI:10.1145/3219104

General Chair:
Sergiu Sanielevici
Pittsburgh Supercomputing Center

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 22 July 2018

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PEARC '18: Practice and Experience in Advanced Research Computing

July 22 - 26, 2018

PA, Pittsburgh, USA

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PEARC '18 Paper Acceptance Rate 79 of 123 submissions, 64%;

Overall Acceptance Rate 133 of 202 submissions, 66%

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