Optimizing Cloud Data Ingestion

In today’s data-driven world, organizations face the challenge of managing and processing vast amounts of information. Traditional data ingestion methods often struggle to keep up with the exponential growth of data, leading to inefficiencies and increased costs. To address these challenges, a novel data ingestion design pattern has been proposed, focusing on cloud-based architectures.

The Proposed Design Pattern

The core of this design pattern is a metadata-driven framework that supports both incremental and full refresh ingestion methods. By utilizing a mapping table stored in a SQL database, the framework captures essential parameters for each data source, including:

• Data Source Name: Identifies the origin of the data.
• Table Name: Specifies the target table for data storage.
• Ingestion Method: Indicates whether the data should be ingested incrementally or through a full refresh.
• Primary Key Information: Defines the unique identifier for records.
• Date Column for Incremental Ingestion: Utilized to track changes over time.

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• Credentials for Accessing the Data Source: Ensures secure data retrieval.

This metadata-driven approach allows for dynamic adjustments to ingestion strategies based on the characteristics of each data source, facilitating seamless changes to ingestion types, schema updates, table additions, and the integration of new data sources with minimal intervention from data engineers. (link.springer.com)

Ingestion Techniques

The design pattern incorporates two primary ingestion methods:

1. Incremental Ingestion: This method focuses on transferring only the data changes since the last ingestion cycle, utilizing techniques like Change Data Capture (CDC). By analyzing timestamps or hash functions, it minimizes computational load and ensures efficient resource use. (link.springer.com)

2. Full Ingestion: Involves transferring the entire dataset, beneficial during initial loads or when significant changes are confirmed across tables. While it can create substantial system load, it guarantees data consistency. (link.springer.com)

The hybrid approach, combining both methods, has been shown to significantly reduce data ingestion times, especially with larger datasets. For instance, in experiments conducted on Azure, the hybrid model ingested 1 billion rows in approximately 9.3 minutes, compared to 26 minutes using pure full ingestion. (link.springer.com)

Implementation and Workflow

The ingestion pipeline is orchestrated using tools compatible with various cloud providers, such as Azure Data Factory, AWS Data Pipeline, and Google Cloud Dataflow. Each step retrieves configuration parameters dynamically from the metadata table to optimize the ingestion workflow, enhancing flexibility across diverse data environments. (link.springer.com)

For data storage, the design pattern advocates for a folder-based approach, with each data table isolated in distinct folders within a Data Lake structure, preferably using Delta Lake format for its transactional capabilities. (link.springer.com)

Testing and Validation

The proposed design pattern has been validated through experiments conducted on Azure and Google Cloud platforms. The results demonstrate that the hybrid ingestion method not only reduces data ingestion times but also optimizes resource utilization, making it a strong candidate for scalable data ingestion workflows. (link.springer.com)

Key Findings

• Efficiency Gains: The hybrid ingestion method demonstrated considerable time savings over traditional ingestion methods, particularly as data volume increased. (link.springer.com)

• Flexibility: The metadata-driven structure enhances adaptability to changing data sources and ingestion types, allowing organizations to efficiently manage diverse data landscapes without extensive reconfiguration or development overhead. (link.springer.com)

• Cloud-Agnostic: The design pattern’s principles enable deployment across multiple cloud platforms, protecting against vendor lock-in and facilitating integration with existing systems. (link.springer.com)

• Enhanced Data Management: The use of Delta Lake format ensures that data management practices align with best practices, improving data governance and analytical capabilities within cloud environments. (link.springer.com)

By integrating a metadata-driven approach with hybrid ingestion techniques, this design pattern offers a solution that enhances efficiency, flexibility, and quality of data streams. The results of the experiments validate the effectiveness of the methodology across different cloud platforms and data sources, suggesting broad applicability in various organizational contexts. (link.springer.com)

References

• Rucco, C., Longo, A., & Saad, M. (2025). Enhancing Data Ingestion Efficiency in Cloud-Based Systems: A Design Pattern Approach. Data Science and Engineering. (link.springer.com)

• AWS. (2021). AWS Cloud Data Ingestion Patterns and Practices. (docs.aws.amazon.com)

• AWS. (2021). Heterogeneous Data Ingestion Patterns. (docs.aws.amazon.com)

• AWS. (2021). Best Practices – AWS Prescriptive Guidance. (docs.aws.amazon.com)

• AWS. (2021). Optimize Your Modern Data Architecture for Sustainability: Part 1 – Data Ingestion and Data Lake. (aws.amazon.com)

• Mukherjee, K., Shah, R., Saini, S. K., et al. (2023). Towards Optimizing Storage Costs on the Cloud. (arxiv.org)

• Yu, G. X., Wu, Z., Kossmann, F., et al. (2024). Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD – Extended Version. (arxiv.org)

• Rucco, C., Saad, M., & Longo, A. (2025). Formalizing ETLT and ELTL Design Patterns and Proposing Enhanced Variants: A Systematic Framework for Modern Data Engineering. (arxiv.org)

• Chandrashekar, S. (2025). Best Practices to Optimize Data Ingestion Spend in Snowflake. (medium.com)

• Design and Implementation of a Cloud-Based Event-Driven Architecture for Real-Time Data Processing in Wireless Sensor Networks. (2021). The Journal of Supercomputing. (link.springer.com)

• Best Practices for Cloud Storage. (2021). Google Cloud. (cloud.google.com)

• Data Ingestion and Normalization. (2021). Microsoft Learn. (learn.microsoft.com)