Demystifying Data Ingestion: Unveiling Essential Framework Components in a Data Lake

A COMPREHENSIVE EXPLORATION OF THE KEY BUILDING BLOCKS IN DATA INGESTION FRAMEWORKS

✎ Co-author — Gyani Learn

❑ Overview

Building an effective Lakehouse begins with building a strong foundation with ingestion layer. Ingestion refers to the process of collecting, importing, and processing raw data from various sources into the data lake. Data ingestion is fundamental to the success of a data lake as it enables the consolidation, exploration, and processing of diverse and raw data. It lays the foundation for downstream analytics, machine learning, and reporting activities, providing organizations with the flexibility and agility needed to derive meaningful insights from their data. In this blog, we will walk through the key components of an Ingestion framework for a data lake.

Data Lakehouse in action

Ingestion can be divided in 2 types -

• One time Ingestion — One time ingestion/load refers to initial ingestion of historical data to data lake.
• Incremental ingestion — Post one time ingestion, incremental ingestion.

Please Note that we have only considered batch ingestion. Real time ingestion is not in scope of this blog.

❑ Basic components of Ingestion framework

Below graphic represents the basic building blocks (Components) of ingestion framework.

Basic components of ingestion framework

Below table details out different components and purpose of each component.

❑ List of key design considerations

Key design considerations

Designing a framework involves considering various fundamental principles that contribute to its effectiveness, maintainability, and adaptability. Here’s a list of key fundamental principles & design considerations that we used while designing the Ingestion framework -

1. Simplicity:

• Principle — Keep the design as simple as possible.
• Our Rationale — We want this to be super simple. This reduces the likelihood of errors, eases understanding, and facilitates maintenance.

During one of our discussions with friend Dharmendra Keshari he said something that really struck us “Most of the time the frameworks are so complicated that only the creator understands it and is seldom used. This really doesn’t serve its core purpose”. Simplicity became our Mantra!!!

2. Modularity:

• Principle — Breaking down the system into smaller, independent, interchangeable modules makes it easier to understand, develop, and maintain.
• Our Rationale — We have divided the ingestion framework into distinct independent modules or components, each responsible for a specific task or functionality (e.g., data ingestion, notification, auditing etc.). This enhances maintainability, as changes to one module don’t affect others. Promotes code reuse and ease of understanding.

3. High Cohesion:

• Principle — Modules within the framework should be designed to have a high degree of cohesion, meaning that each module should perform a specific, well-defined set of tasks related to data ingestion.
• Our Rationale — We have tried to keep related functionality within the same module ensuring that each module focuses on a single responsibility, such as handling a specific function e.g. Notification module’s only task is to send success & failure notification.

4. Low Coupling:

• Principle — Minimizing coupling between modules reduces dependencies, making it easier to modify or replace one module without affecting others.
• Our Rationale — We have designed modules to be independent, with minimal reliance on the internal workings of other modules. e.g. Auditing can used independently without depending on other modules like notification, reporting etc.

5. Scalability:

• Principle — Design for scalability to accommodate growing data volumes.
• Our Rationale — We have considered distributed processing frameworks for parallel processing wherever possible.

6. Incremental Loading:

• Principle — Support incremental loading to efficiently handle updates and additions.
• Our Rationale — We have considered mechanisms for tracking changes (timestamp, delta files) allowing reprocessing for reloading manually in case of failures.

7. Monitoring and Logging:

• Principle — Set up comprehensive auditing, monitoring and logging for tracking data movement and system health & implement notification alerts for unusual activities or errors.
• Our Rationale — We have ensured that framework has monitoring & logging enabled including the notifications alerts for success or failure cases. We have established a robust metadata management system for cataloging and tracking data lineage.

8. Highly Performant:

• Principle — Tune the ingestion process for optimal performance by performing parallel ingestion wherever applicable.

9. Documentation:

• Principle — Maintain comprehensive documentation which assists in understanding the framework’s purpose, architecture, usage and facilitates collaboration and troubleshooting.
• Our Rationale — We have ensured comprehensive documentation including guidelines for troubleshooting and maintenance.

I hope this blog helped you in understanding how to design an effective ingestion framework for a data lake house. There is still more to come…Happy Learning!!!

Please Note — All opinions expressed here are my personal views and not of my employer.

Thought of the moment-

“Honest work is much better than a mansion.” — Leo Tolstoy