Big data is undergoing a revolution. Generative AI is churning out a massive array of content – text, images, audio, and video.

One of the biggest challenges is managing and understanding this data deluge. Traditional data pipelines built for structured information are simply inadequate. This is where the concept of ETL (Extract, Transform, Load) comes back into focus, but with a multimodal twist.

The Rise of Multimodal Data

Some of the leaders in generative AI across modality:

  • Text:  Generative models like ChatGPT, BARD, and Anthropic are producing human-quality text formats, from  articles and scripts to poems and code.
  • Image: DALL-E and Stable Diffusion are creating stunning and realistic images from just a text description.
  • Audio: Platforms like ElevenLabs are generating realistic audio,  from  human speech to sound effects and music.
  • Video: Sora and HeyGen are pushing the boundaries of video creation with AI-powered tools for generating video content.

The Challenge: Extracting Meaning from the Multimodal Mix

This explosion of data creates a challenge: how do we extract meaning and insights from such a diverse and unstructured sea of information? Traditional data pipelines, designed for rows and columns, simply can't handle the richness of multimodal data.

The Solution: Multimodal ETL

Multimodal ETL is the answer to managing the multimodal data deluge. It involves a three-stage process:


Data is pulled from various sources like text documents, images, videos, and audio.

Tools like Mixpeek, a multimodal development platform, allow you to go beyond simple data extraction:

  • Extract objects from videos (e.g., identify a car in a video clip)
  • Extract text from images (e.g., read captions or text overlays)
  • Generate rich metadata (e.g., create tags or summaries for your data)

The transformed data is then loaded into your data storage solution, keeping your information constantly updated.

The Power of Embeddings

A key aspect of multimodal ETL is embedding. Imagine converting all your data – text, images, audio, video – into a common language that AI models can understand. This is what embedding does. Mixpeek excels at this, allowing you to generate embeddings for any data modality.

By creating embeddings, you can unlock the true potential of your multimodal data. AI models can then analyze and search across all data types, regardless of their original format.

Benefits of Multimodal ETL

The benefits of implementing a multimodal ETL pipeline are numerous:

  • Unlocking Insights: By extracting, transforming, and embedding data, you can gain deeper insights from your information.
  • Fueling Generative AI:  Multimodal ETL provides the structured and diverse data that generative AI models need to train effectively.
  • Building Powerful Search:  Imagine searching not just for text documents, but for videos containing specific objects or audio with a particular mood. Multimodal ETL pipelines pave the way for sophisticated search functionalities across all data modalities.

The Future of Data is Multimodal

The rise of generative AI and the explosion of multimodal data are fundamentally changing the way we interact with information. Multimodal ETL is the key to unlocking the potential of this data revolution. By building robust pipelines for extracting, transforming, embedding, and generating insights, we can harness the power of multimodal data to drive innovation across all industries.

About the author
Ethan Steininger

Ethan Steininger

Former GTM Lead of MongoDB's NLP platform, Atlas Search. Occasionally off the grid in his self-converted camper van.

Multimodal Makers | Mixpeek

Multimodal Pipelines for AI

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