Introduction

In the fast-paced world of organizational knowledge management, seamless data ingestion is important. At KnowledgeKeeper, our primary mission is to gather, process, and structure organizational knowledge efficiently. However, our journey began with one of the most challenging aspects of this process — document ingestion.

Organizations rely on diverse sources for knowledge, ranging from Notion and Freshdesk to other SaaS platforms, each with its own format and storage mechanism. To unify these data sources, we have developed a robust and scalable data ingestion pipeline that streamlines document processing while maintaining consistency and efficiency.

This blog delves into the challenges we faced and the innovative solutions we have implemented to create a smooth knowledge ingestion system.

Tackling Document Formats

One of the biggest obstacles in knowledge ingestion is handling documents in multiple formats. Every service provider structures data differently:

• Notion exports data in JSON format.

• Freshdesk stores documents as HTML with CSS.

• Other sources use Markdown, PDFs, or raw text.

To standardize these, we devised a uniform approach — converting all ingested documents into HTML with inline CSS. This provided:

• Consistency: A single format for storage and processing.

• Flexibility: Easy manipulation and rendering across multiple platforms.

• Simplified Access: Unified data for search, retrieval, and AI-based knowledge extraction.

This foundational decision set the stage for seamless data handling across the KnowledgeKeeper ecosystem.

Custom Parsers: A Dynamic Solution

Initially, we faced a challenge: the need for custom parsers to handle different data sources. Developing a separate parser for each source would have been inefficient, leading to maintenance overhead and scalability issues.

To address this, we designed a plug-and-play parser tool that dynamically applies the correct parsing logic based on the document’s origin. This tool:

• Automatically detects the document type based on the source.

• Applies pre-configured parsing rules for that specific source.

• Standardizes the output into our universal HTML + inline CSS format.

This approach drastically reduced manual intervention and streamlined document ingestion, making it easier to integrate new data sources into KnowledgeKeeper.

Enhancing Data Sync with Airbyte

With document parsing optimized, the next challenge was synchronizing data efficiently. Initially, we explored direct API synchronization to AWS S3, but this proved cumbersome due to:

• Inconsistent API protocols across different platforms.

• Rate limits and authentication hurdles.

• Difficulties in managing batch operations.

To resolve these issues, we integrated Airbyte, an open-source ELT (Extract, Load, Transform) solution. Airbyte allowed us to:

• Batch sync data from multiple sources, reducing API bottlenecks.

• Ensure data consistency across different ingestion pipelines.

• Simplify connector creation, making it easier to add new sources.

By combining Airbyte with FastAPI, we further enhanced automation, enabling real-time control over sync jobs and ensuring data flowed smoothly into our system.

Orchestrating with Prefect

Once data was ingested and transformed, the next step was managing workflow orchestration. This is where Prefect came into play.

Prefect, our orchestration engine of choice, enabled us to:

• Develop production-ready pipelines with minimal configuration.

• Monitor workflows in real time, reducing the risk of failures.

• Implement error handling and retries, ensuring robust data ingestion.

Using Prefect, we established a structured workflow that seamlessly:

1. Retrieves data from Airbyte.

2. Applies the custom parser for transformation.

3. Stores the processed data back into AWS S3.

4. Notifies downstream services for further processing.

With this setup, our ingestion system became fully automated, resilient, and scalable.

Conclusion

Through strategic innovations and technology, we transformed KnowledgeKeeper’s knowledge ingestion process into a dynamic, scalable, and efficient system. Our approach not only streamlined the way we handle diverse document formats but also enhanced synchronization, orchestration, and standardization.

Key takeaways from our journey:

• Unified Data Format: Converting all documents to HTML with inline CSS ensured seamless storage and retrieval.

• Plug-and-Play Parsers: A dynamic framework eliminated the need for manual parser creation.

• Airbyte Integration: Simplified and optimized data synchronization.

• Prefect Orchestration: Enabled real-time monitoring and reliable workflow execution.

With these advancements, KnowledgeKeeper is now equipped with a powerful knowledge ingestion engine that efficiently manages organizational knowledge at scale. By leveraging a combination of automation, intelligent parsing, and robust orchestration, we have set new benchmarks for efficiency, flexibility, and scalability in document management.