How to Use Google BigQuery & Gemini AI for Data Analysis

Unlock powerful insights from your data with the seamless integration of Google BigQuery and Gemini AI. This guide walks you through how to leverage Gemini's advanced AI capabilities—right within BigQuery—without the need for coding expertise or machine learning (ML) infrastructure.

What is Gemini AI?

Gemini AI is Google’s cutting-edge family of large language models (LLMs) designed to perform diverse tasks such as text summarization, feature extraction, sentiment analysis, and more. Select models in the Gemini family also support multimodal inputs, enabling capabilities like object recognition and image captioning, making them perfect for analyzing both structured and unstructured data.

Why Use Gemini with BigQuery?

Traditionally, applying advanced AI to large datasets required intricate coding and infrastructure. BigQuery’s integration with Gemini AI, through Vertex AI, simplifies this process, enabling users to perform AI-driven analysis using SQL while benefiting from BigQuery’s scalability.

5 Steps to Get Started

1. Set Up Your Environment

• Enable Vertex AI and BigQuery APIs for integration.

• Create a cloud resource connection.

• Assign the Vertex AI user role to your service account.

2. Prepare Your Data

• For multimodal tasks (e.g., Gemini Pro Vision), upload visual data (like images or videos) to Google Cloud Storage and reference them as a BigQuery object table.

• Use the CREATE EXTERNAL TABLE SQL command to link this table to your Cloud Storage bucket. Each row in the table will correspond to an object with metadata.

3. Create Gemini Models in BigQuery ML

• Define remote models pointing to the appropriate Gemini endpoints:

  • Gemini Pro (text-based tasks): For extracting insights from structured or textual data.

  • Gemini Pro Vision (multimodal tasks): For analyzing visual or multimedia content.

4. Analyze Your Data

• For Multimodal Tasks (Gemini Pro Vision):

  • Use the ml.generate_text function, specifying the Gemini Pro Vision model and the object table as inputs.

  • Create prompts that instruct the model to extract desired details (e.g., identifying movie titles and release years from posters).

• For Textual Tasks (Gemini Pro):

  • Use ml.generate_text to analyze structured text data. Include prompts tailored to generate summaries, insights, or categorizations.

  • Save the results to a BigQuery table for further analysis.

5. Leverage the Results

• Combine AI-generated insights with other structured data in BigQuery for advanced analysis.

• Visualize or operationalize the results using tools like Looker or integrate them into your workflows.

Example Use Case

Imagine analyzing movie poster images stored in Cloud Storage. Using Gemini Pro Vision, you can extract titles and release years from these images, store them as structured data in BigQuery, and then use Gemini Pro to generate descriptive summaries for each movie. This pipeline transforms unstructured visual data into actionable insights, showcasing the versatility of AI-powered analysis.

Key Benefits of BigQuery + Gemini AI

• Scalability: Effortlessly process large datasets using BigQuery’s robust infrastructure.

• Accessibility: No ML expertise or advanced coding required—run everything with SQL.

• Data Fusion: Seamlessly integrate AI-driven insights with your structured datasets.

• Efficiency: Perform all analysis directly within your data warehouse environment.

Start Your AI-Powered Data Journey

Ready to unlock the full potential of your data? Explore Gemini AI with BigQuery ML today. Let’s talk