How ChatSlide Leverages Apache Gravitino for Multi-Modal Data Catalog Management at Scale
With over 150,000 users and 500+ daily active users generating presentations, videos, and multi-modal content, ChatSlide faced a critical challenge: managing an increasingly complex and diverse data catalog. Here's how we solved it with Apache Gravitino.
The Challenge: Multi-Modal Data at Scale
At ChatSlide, we don't just handle text. Our platform processes and generates:
Presentation Slides
PowerPoint, PDF, and web-based formats
AI-Generated Videos
With avatars, voiceovers, and animations
Image Assets
Charts, diagrams, and AI-generated visuals
Audio Content
Voice cloning and text-to-speech outputs
User Documents
Uploaded PDFs, Word docs, and research papers
Metadata & Analytics
Usage patterns, performance metrics, and user data
With users generating thousands of presentations daily, our data infrastructure needed to support:
- Real-time stream processing for live collaboration and instant content generation
- Machine learning pipelines for AI avatar generation, content recommendations, and quality enhancement
- Data lake storage for long-term archival and analytics across structured and unstructured data
Enter Apache Gravitino: A Unified Metadata Lake
Apache Gravitino is a high-performance, geo-distributed, and federated metadata lake that provides a unified metadata abstraction layer across diverse data sources and AI assets.
Unified metadata abstraction across heterogeneous sources
Direct system integration with bidirectional sync
Comprehensive data governance and access control
Multi-region, geo-distributed architecture
Multi-engine support (Trino, Spark, Flink)
Why Gravitino Was Perfect for ChatSlide
The combination of multi-modal data support, real-time processing capabilities, and federated metadata management made Gravitino the ideal solution for our complex data catalog challenges. Its active community and strong engineering team in the Bay Area also meant we could collaborate closely with experts who understand enterprise-scale data challenges.
ChatSlide's Data Architecture with Gravitino
Unified Metadata Lake
Gravitino manages metadata for our Kafka streams, enabling real-time collaboration features and instant content generation with full data lineage tracking.
Our machine learning models for avatar generation and content enhancement are registered as first-class assets in Gravitino, with complete versioning and lineage.
All user-generated content, from presentations to videos, is cataloged in our S3 data lake through Gravitino's unified interface, ensuring consistent governance.
How We Use Gravitino: Real-World Use Cases
When a user searches for presentations on "climate change," Gravitino helps us discover not just text-based slides, but also related videos, audio clips, images, and data visualizations across all our storage systems.
Result: 3x faster search queries across multi-modal content with complete lineage tracking.
Every AI-generated avatar, voice clone, and visual asset is tracked through Gravitino. We know exactly which ML model version created what content, using which training data, and when.
Result: 100% audit compliance and the ability to reproduce any AI output for quality assurance.
With data spread across AWS S3, PostgreSQL, Redis, and Kafka, Gravitino provides a single control plane for access policies, ensuring GDPR and SOC 2 compliance across all systems.
Result: Centralized governance reduced our compliance overhead by 60%.
When multiple users collaborate on a presentation, Gravitino tracks all changes, versions, and contributors in real-time through our Kafka streams, maintaining a complete audit trail.
Result: Zero data conflicts and complete version history for team projects.
Measurable Impact on ChatSlide's Infrastructure
Unified Data Discovery
Single interface for all data assets
Reduced Compliance Overhead
60% less time on audit preparation
Faster Feature Development
New data integrations in days, not weeks
Complete Data Lineage
End-to-end tracking for all assets
Looking Forward: The Future of Data Catalogs
As ChatSlide continues to grow and Apache Gravitino evolves, we're excited about several upcoming capabilities:
Gravitino's upcoming AI asset management features will allow us to treat ML models, training datasets, and AI-generated content as first-class citizens with comprehensive versioning and governance.
As we expand globally, Gravitino's geo-distributed architecture will enable us to maintain unified metadata across AWS, GCP, and Azure deployments.
We're working with the Gravitino team to build rich visualization tools that help our engineers understand complex data flows across our multi-modal content pipelines.
Integration with Kafka and Flink will enable real-time metadata updates, critical for our live collaboration features and instant content generation.
Conclusion
Managing data catalogs at scale is one of the most challenging problems in modern data engineering, especially when dealing with multi-modal content across real-time streams, machine learning pipelines, and data lakes. Apache Gravitino has proven to be an invaluable solution for ChatSlide's complex data infrastructure.
The combination of unified metadata management, comprehensive governance, and active community support has enabled us to scale confidently from 100K to 150K+ users while maintaining the performance and reliability our users expect.
If you're facing similar challenges with multi-modal data catalogs, real-time processing, or machine learning metadata management, I highly recommend exploring Apache Gravitino.
The project has a strong team, active community, and clear roadmap that makes it a safe bet for production deployments. With 2.3K GitHub stars and growing, it's clear the industry recognizes the value of unified metadata management.
Want to see how ChatSlide handles multi-modal content?
Try ChatSlide FreeAbout the Author
Quanlai Li is the founder of ChatSlide, an AI-powered presentation platform serving 150,000+ users globally. With a background in distributed systems and machine learning, Quanlai is passionate about building scalable data infrastructure and democratizing content creation through AI.