A Survey of Pipeline Tools for Data Engineering (2406.08335v1)

Abstract: Currently, a variety of pipeline tools are available for use in data engineering. Data scientists can use these tools to resolve data wrangling issues associated with data and accomplish some data engineering tasks from data ingestion through data preparation to utilization as input for ML. Some of these tools have essential built-in components or can be combined with other tools to perform desired data engineering operations. While some tools are wholly or partly commercial, several open-source tools are available to perform expert-level data engineering tasks. This survey examines the broad categories and examples of pipeline tools based on their design and data engineering intentions. These categories are 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 survey also provides a broad outline of the utilization with examples within these broad groups and finally, a discussion is presented with case studies indicating the usage of pipeline tools for data engineering. The studies present some first-user application experiences with sample data, some complexities of the applied pipeline, and a summary note of approaches to using these tools to prepare data for machine learning.

A Survey of Pipeline Tools for Data Engineering

"A Survey of Pipeline Tools for Data Engineering" by Anthony Mbata, Yaji Sripada, and Mingjun Zhong provides a comprehensive examination of various tools and frameworks designed to address data engineering challenges. The paper categorically appraises the functionalities and capabilities of several data pipeline tools, emphasizing their roles in data wrangling, integration, orchestration, and machine learning. The authors present a methodical survey to guide data scientists and engineers in selecting appropriate pipeline solutions based on specific data engineering requirements.

Introduction

The main objective of data engineering is to transform raw data into structured formats suitable for downstream analytics and machine learning tasks. This encompasses obtaining, organizing, extracting, and formatting data—operations that are both time-consuming and labor-intensive. According to the paper, it is estimated that data scientists spend approximately 80% of their time on data engineering tasks.

To mitigate the complexities inherent in data engineering, various tools and frameworks have been developed. These tools are meant to implement data engineering procedures as a series of semi-automated or automated operations, thereby improving efficiency and accuracy. The tools vary in their design and functionalities, each aimed at resolving different data engineering challenges. The paper surveys these tools, grouping them based on their core functionalities: ETL/ELT processes, data integration, ingestion, transformation pipelines, orchestration and workflow management, and machine learning pipelines.

Categories of Data Engineering Pipelines

The surveyed tools are grouped into four primary categories:

1. ETL/ELT Pipelines:
   • Apache Spark: An open-source platform that supports multiple languages such as Python, Java, SQL, Scala, and R. Spark is designed for scalable large-scale parallel data processing, making it advantageous for both batch and real-time data processing scenarios. It integrates seamlessly with other tools and offers robust capabilities for data integration and transformation.
   • AWS Glue: A serverless ETL/ELT service that simplifies the process of building, monitoring, and managing data pipelines. AWS Glue supports multiple languages and frameworks, providing a comprehensive set of built-in capabilities for data organization and transformation.
2. Integration, Ingestion, and Transformation Pipelines:
   • Apache Kafka: An open-source platform known for its high-speed data processing and low latency. Apache Kafka excels in distributed event streaming and integrates well with other systems, making it ideal for real-time data integration and ingestion.
   • Microsoft SQL Server Integration Services (SSIS): A closed-source platform that offers graphical user interface-based design tools for building ETL workflows. It supports multiple data sources and provides extensive capabilities for data integration and transformation, rendering it suitable for both batch processing and real-time data handling.
3. Orchestration and Workflow Management Pipelines:
   • Apache Airflow: An open-source tool that provides robust orchestration and workflow management capabilities. It supports Python and can manage complex dependencies between tasks using Directed Acyclic Graphs (DAGs). Airflow is highly extensible and can integrate with various tools to offer a holistic solution for workflow automation.
   • Apache Beam: Another open-source tool that supports multiple languages and provides parallel and distributed processing capabilities. It is designed to handle both batch and streaming data seamlessly.
4. Machine Learning and Model Deployment Pipelines:
   • TFX (TensorFlow Extended): An open-source pipeline for building end-to-end machine learning workflows. TFX is scalable and integrates easily with other orchestration tools like Apache Airflow and Kubeflow. It provides a robust framework for data ingestion, validation, transformation, model training, analysis, and deployment.
   • AutoKeras: An open-source AutoML framework that simplifies the process of building and optimizing machine learning models. It supports TensorFlow and offers automated hyperparameter tuning and built-in visualization tools.

Case Study: IDEAL Household Energy Dataset

The authors provide case studies to illustrate the application of these pipeline tools in addressing real-world data engineering challenges. Using the IDEAL Household Energy Dataset, they demonstrate how each category of pipeline tools can be employed to resolve data integration, data quality, and feature engineering issues. For instance:

• Apache Spark: Used to parse, integrate, and transform sensor data from the IDEAL dataset, showcasing its robustness in handling large-scale data transformations and integrations.
• Microsoft SSIS: Demonstrates its graphical user interface capabilities in setting up ETL tasks to clean and transform the IDEAL dataset.
• Apache Airflow: Highlights its ability to manage complex workflows and dependencies through DAGs, effectively orchestrating the end-to-end data processing pipeline.
• TFX: Illustrates its effectiveness in data transformation and feature engineering, preparing the IDEAL dataset for machine learning model training and analysis.

Discussion and Recommendation

The paper identifies trends in the features and functionalities of the surveyed tools. The authors note that while each tool has its strengths and weaknesses, the choice of a pipeline tool largely depends on the specific requirements of the data engineering task at hand. They recommend Apache Airflow for its scalability, flexibility, and robust integration capabilities, making it a versatile choice for managing complex data engineering workflows.

Conclusion

The paper concludes by emphasizing that the selection of data pipeline tools should be driven by the specific data engineering challenges to be addressed. While no single tool offers a one-size-fits-all solution, the surveyed tools collectively provide a comprehensive suite of capabilities to tackle a wide range of data engineering tasks, from data parsing and integration to feature engineering and machine learning model deployment. The authors' detailed examination and practical case studies offer valuable insights for researchers and practitioners in the field of data engineering.