Summary of A Survey Of Pipeline Tools For Data Engineering, by Anthony Mbata and Yaji Sripada and Mingjun Zhong
A Survey of Pipeline Tools for Data Engineering
by Anthony Mbata, Yaji Sripada, Mingjun Zhong
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Computation (stat.CO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents a comprehensive survey of pipeline tools used in data engineering. It categorizes these tools into four broad groups: Extract Transform Load/Extract Load Transform (ETL/ELT), pipelines for Data Integration, Ingestion, and Transformation, Data Pipeline Orchestration and Workflow Management, and Machine Learning Pipelines. The authors provide a detailed outline of each category, including examples and utilization cases. The survey also presents case studies highlighting the usage of pipeline tools for data engineering, including first-user application experiences, complexities, and approaches to preparing data for machine learning. The paper aims to provide a broad overview of the current landscape of pipeline tools, facilitating informed decision-making by data scientists. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at the different tools that help with data engineering tasks, like getting data ready for use in machine learning models. It groups these tools into categories based on what they do and how they’re designed. The author provides examples of each category and shows how people have used these tools to prepare data for analysis. The goal is to give a general overview of the current state of pipeline tools, so data scientists can make informed decisions about which ones to use. |
Keywords
» Artificial intelligence » Machine learning
