Mixpeek + Databricks

Overview

Mixpeek and Databricks occupy different layers of the data stack. Mixpeek ingests unstructured multimodal files and extracts structured features, embeddings, and classifications. Databricks provides the lakehouse platform — Delta Lake for storage, Unity Catalog for governance, and integrated ML for training and serving models. Together, they give you a complete path from raw media files to governed, analytics-ready data.

Mixpeek

Ingests unstructured files, extracts features (embeddings, transcripts, classifications, metadata), and powers multimodal retrieval.

Databricks

Stores structured outputs in Delta tables, enforces governance via Unity Catalog, and runs ML training and serving at scale.

Architecture

                        Mixpeek                              Databricks
               +-----------------------+            +------------------------+
               |                       |            |                        |
  Files -----> |  Buckets & Collections|            |   Delta Lake Tables    |
  (images,     |                       |            |                        |
   video,      |  Decompose files into |  export    |  - classifications     |
   audio,      |  features:            | ---------> |  - extracted metadata  |
   PDFs)       |   - embeddings        |            |  - taxonomy labels     |
               |   - transcripts       |            |  - document payloads   |
               |   - classifications   |            |                        |
               |   - metadata          |  enrich    |  Unity Catalog governs |
               |                       | <--------- |  all tables. Databricks|
               |  Retrieval & Search   |            |  ML retrains models.   |
               +-----------------------+            +------------------------+

Use Cases

Write extracted features as Delta tables

After Mixpeek processes your files, export the structured outputs — transcripts, object detections, taxonomy labels, metadata — as Delta tables. This makes them queryable with Spark SQL, joinable with your existing business data, and available to any tool in the Databricks ecosystem.

Use Unity Catalog for governance

Unity Catalog provides fine-grained access control, lineage tracking, and audit logging for all data assets. Once Mixpeek outputs land in Delta tables, Unity Catalog governs who can access them and how they flow through your organization.

Combine Mixpeek retrieval with Databricks ML

Use Mixpeek to power real-time multimodal search and retrieval. Feed the same structured features into Databricks ML for batch training — fine-tune classifiers, build recommendation models, or run large-scale analytics on extracted content.

Quick Start

Export Mixpeek document metadata to a Delta table using the Mixpeek Python SDK and the Databricks SQL Connector.

Install dependencies

pip install mixpeek databricks-sql-connector

List documents from Mixpeek

from mixpeek import Mixpeek

client = Mixpeek(api_key="your-api-key")

# List documents from a collection
documents = client.collections.documents.list(
    collection_id="your-collection-id",
    page_size=100
)

Write to a Delta table via Databricks SQL

from databricks import sql
import json

connection = sql.connect(
    server_hostname="YOUR_WORKSPACE.cloud.databricks.com",
    http_path="/sql/1.0/warehouses/YOUR_WAREHOUSE_ID",
    access_token="YOUR_ACCESS_TOKEN"
)

cursor = connection.cursor()

# Create table if it does not exist
cursor.execute("""
    CREATE TABLE IF NOT EXISTS mixpeek_catalog.default.documents (
        document_id STRING,
        source_url STRING,
        content_type STRING,
        metadata STRING,
        created_at TIMESTAMP
    )
""")

# Insert each document
for doc in documents:
    cursor.execute(
        """
        INSERT INTO mixpeek_catalog.default.documents
            (document_id, source_url, content_type, metadata, created_at)
        VALUES (%s, %s, %s, %s, %s)
        """,
        (
            doc.get("document_id"),
            doc.get("source", {}).get("url"),
            doc.get("content_type"),
            json.dumps(doc.get("metadata", {})),
            doc.get("created_at"),
        )
    )

connection.commit()
cursor.close()
connection.close()

For production workloads, write Mixpeek outputs to cloud storage (S3 or ADLS) and use Databricks Auto Loader to incrementally ingest new files into Delta tables.

When to Use Each

Capability	Mixpeek	Databricks
Ingest unstructured files (video, images, audio, PDFs)	Yes	No
Extract features (embeddings, transcripts, classifications)	Yes	No
Multimodal semantic search	Yes	No
Structured SQL analytics	No	Yes (Spark SQL)
Data governance and lineage	Document-level ACL	Unity Catalog
ML model training and serving	No	Yes (MLflow, Model Serving)
Streaming ingestion	Webhooks + batch triggers	Structured Streaming, Auto Loader

Mixpeek handles everything before the data is structured. Databricks handles everything after. Use both to bridge the gap between raw multimodal files and governed, ML-ready data.

Taxonomies — classify content and export labels
SQL Lookup Stage — query external databases from retriever pipelines
API Call Stage — call external APIs during retrieval
Webhooks — trigger Databricks jobs when Mixpeek processing completes

Get started

Connect your data

Extract features

Build retrievers

Enrich & organize

Integrate & operate

Resources

Mixpeek + Databricks

Overview

Mixpeek

Databricks

Architecture

Use Cases

Write extracted features as Delta tables

Use Unity Catalog for governance

Combine Mixpeek retrieval with Databricks ML

Quick Start

When to Use Each

​Overview

Mixpeek

Databricks

​Architecture

​Use Cases

​Write extracted features as Delta tables

​Use Unity Catalog for governance

​Combine Mixpeek retrieval with Databricks ML

​Quick Start

​When to Use Each

​Related

Overview

Architecture

Use Cases

Write extracted features as Delta tables

Use Unity Catalog for governance

Combine Mixpeek retrieval with Databricks ML

Quick Start

When to Use Each

Related