MarkTechPost@AI 2024年06月14日
A Comprehensive Overview of Data Engineering Pipeline Tools
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The paper “A Survey of Pipeline Tools for Data Engineering” thoroughly examines various pipeline tools and frameworks used in data engineering. Let’s look into these tools’ different categories, functionalities, and applications in data engineering tasks.

Introduction to Data Engineering

Categories of Pipeline Tools

Pipeline tools for data engineering are broadly categorized based on their design and functionality:

    Extract Transform Load (ETL) / Extract Load Transform (ELT) Pipelines:
      ETL Pipelines: Designed for data integration, these pipelines extract data from sources, transform it into the required format, and load it into the destination.ELT Pipelines: Typically used for big data, these pipelines extract data, load it into data warehouses or lakes, and then transform it.
    Data Integration, Ingestion, and Transformation Pipelines:
      These pipelines handle the organization of data from multiple sources, ensuring that it is properly integrated and transformed for use.
    Pipeline Orchestration and Workflow Management:
      These pipelines manage the workflow and coordination of data processes, ensuring data moves seamlessly through the pipeline.
    Machine Learning Pipelines:
      These pipelines, specifically designed for machine learning tasks, handle machine learning models’ preparation, training, and deployment.

Detailed Examination of Tools

Apache Spark:

An open-source platform supporting multiple languages (Python, Java, SQL, Scala, and R). It is suitable for distributed and scalable large-scale data processing, providing quick big-data query and analysis capabilities.

AWS Glue:

A serverless ETL service that simplifies the monitoring and management of data pipelines. It supports multiple languages & integrates well with other AWS machine learning and analytics tools.

Apache Kafka:

An open-source platform supporting real-time data processing with high speed and low latency. It can ingest, read, write, and process data in local and cloud environments.

Microsoft SQL Server Integration Services (SSIS):

A closed-source platform for building ETL, data integration, and transformation pipeline workflows. It supports multiple data sources & destinations and can run on-premises or integrate with the cloud.

Apache Airflow:

An open-source tool for workflow orchestration and management, supporting parallel processing and integration with multiple tools.

TensorFlow Extended (TFX):

An open-source machine learning pipeline platform supporting end-to-end ML workflows. It provides components for data ingestion, validation, and feature extraction.

Conclusion

The selection of an appropriate data engineering pipeline tool depends on many factors, including the specific requirements of the data engineering tasks, the nature of the data, and the user’s familiarity with the tool. Each tool has strengths and weaknesses, making them suitable for different scenarios. Combining multiple pipeline tools might provide a more comprehensive solution to complex data engineering challenges.


Source: https://arxiv.org/pdf/2406.08335

The post A Comprehensive Overview of Data Engineering Pipeline Tools appeared first on MarkTechPost.

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