Paper “A survey of pipeline tools for data engineering” takes an in-depth look at various pipeline tools and frameworks used in data engineering. Let's analyze the different categories, functionalities and applications of these tools in data engineering tasks.
Introduction to data engineering
- Data engineering challenges: Data engineering involves obtaining, organizing, understanding, extracting and formatting data for analysis, a tedious and time-consuming task. Data scientists typically spend up to 80% of their time doing data engineering on data science projects.
- Objective of Data Engineering: The main goal is to transform raw data into structured data suitable for downstream tasks such as machine learning. This involves a series of semi-automated or automated operations implemented through data engineering pipeline frameworks.
Pipe Tool Categories
Pipeline tools for data engineering are broadly classified based on their design and functionality:
- Extraction Load Transformation (ETL)/Extraction Load Transformation (ELT) Pipes:
- ETL Pipelines: Designed for data integration, these pipelines extract data from sources, transform it to the required format, and load it to the destination.
- ELT Pipelines: Typically used for big data, these pipelines extract data, load it into warehouses or data lakes, and then transform it.
- Data integration, ingestion and transformation pipelines:
- These channels handle the organization of data from multiple sources, ensuring that it is appropriately integrated and transformed for use.
- Pipeline Orchestration and Workflow Management:
- These channels manage the workflow and coordination of data processes, ensuring that data moves smoothly through the channel.
- Machine Learning Pipelines:
- These pipelines, designed specifically for machine learning tasks, handle the preparation, training, and deployment of machine learning models.
Detailed tool examination
Apache Spark:
An open source platform that supports multiple languages (Python, Java, SQL, Scala and R). It is suitable for large-scale distributed and scalable data processing, providing fast big data query and analysis capabilities.
- Strengths: Offers parallel processing, flexibility, and built-in capabilities for various data tasks, including graphics processing.
- Weaknesses: Long rendering graphics can cause reliability issues and negatively impact performance.
AWS Glue:
A serverless ETL service that simplifies data pipeline monitoring and management. It supports multiple languages and integrates well with other AWS machine learning and analytics tools.
- Strengths: Provides code-free and visual features, making it easy to use for data engineering tasks.
- Weaknesses: Customization and integration with non-AWS tools are limited as a closed source tool.
Apache Kafka:
An open source platform that supports real-time data processing with high speed and low latency. It can ingest, read, write, and process data in on-premises and cloud environments.
- Strengths: Fault tolerant, scalable and reliable for real-time data processing.
- Weaknesses: Steep learning curve and complex operational and configuration requirements.
Microsoft SQL Server Integration Services (SSIS):
A closed source platform for creating ETL, data integration, and transformation pipeline workflows. It supports multiple data sources and destinations and can run locally or integrate with the cloud.
- Strengths: Easy to use with a customizable, user-friendly graphical interface and built-in troubleshooting logs.
- Weaknesses: Initial setup can be cumbersome.
Apache Airflow:
An open source tool for workflow orchestration and management, supporting parallel processing and integration with multiple tools.
- Strengths: Extensible with hooks and operators to connect with external systems, robust to manage complex workflows.
- Weaknesses: Steep learning curve, especially during initial setup.
TensorFlow Extended (TFX):
An open source machine learning pipeline platform that supports end-to-end machine learning workflows. Provides components for data ingestion, validation, and feature extraction.
- Strengths: Scalable, integrates well with other tools such as Apache Airflow and Kubeflow, and provides comprehensive data validation capabilities.
- Weaknesses: Setting up TFX can be challenging for users who are not familiar with the TensorFlow ecosystem.
Conclusion
Selecting 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 could provide a more comprehensive solution to complex data engineering challenges.
Fountain: https://arxiv.org/pdf/2406.08335
Aswin AK is a Consulting Intern at MarkTechPost. He is pursuing his dual degree from the Indian Institute of technology Kharagpur. She is passionate about data science and machine learning, and brings a strong academic background and practical experience solving real-life interdisciplinary challenges.
