BigQuery Without SQL: How the ASI Biont AI Agent Automates Analytics and Predicts Trends

BigQuery Without SQL: How the ASI Biont AI Agent Automates Analytics and Predicts Trends

July 2026. You are a data analyst at a company processing terabytes of events in BigQuery. Every morning you write the same SQL query for a sales report, then spend an hour visualizing it in Looker Studio. Sound familiar? Now imagine you simply tell an AI agent: "Show me revenue trends for the last quarter broken down by category and forecast the trend for next month." In 30 seconds, you have a ready-made analysis, chart, and text recommendations. This isn't science fiction—it's the integration of ASI Biont with BigQuery.

What is BigQuery and Why Connect It to an AI Agent?

BigQuery is a fully managed cloud data warehouse from Google Cloud designed for analyzing large volumes of data (petabytes) using SQL queries. According to Google's official documentation (cloud.google.com/bigquery/docs/introduction), the service automatically scales computing resources and supports standard SQL-92, making it accessible to analysts of all levels.

The problem is that:
- Writing complex SQL queries takes time and expertise.
- Most business users don't know SQL and have to wait for a report from an analyst.
- Forecasting trends based on historical data is a separate task requiring either statistical knowledge or additional tools (e.g., BigQuery ML).

The integration of ASI Biont with BigQuery solves all three problems. The AI agent connects to your project via API, accesses tables, executes queries, builds forecasts, and delivers ready-made insights in natural language.

How Does the AI Agent Connect to BigQuery?

Unlike traditional platforms where you need to configure connectors through control panels (OAuth, service accounts, IAM roles), ASI Biont does everything through dialogue. You simply pass a service account API key (JSON key) in the chat with the AI agent. The AI itself writes the integration code for the BigQuery API.

Example dialogue:

You: "Connect to my BigQuery project. Here's the service account key: [insert JSON]"
ASI Biont: "Connection established. I see 12 tables in the sales_data dataset. What data interests you?"

The AI agent uses the google-cloud-bigquery Python client library, performs authentication, retrieves table metadata (schemas, data types, row counts), and is ready to work. All this—without a single line of code from you.

What Tasks Does the Integration Automate?

1. SQL Query Generation and Execution

Instead of writing:

SELECT DATE_TRUNC(order_date, MONTH) AS month,
       SUM(revenue) AS total_revenue
FROM `project.sales_data.orders`
WHERE order_date >= '2026-01-01'
GROUP BY month
ORDER BY month;

You simply say: "Show monthly revenue since the start of the year." ASI Biont generates the query, executes it, and returns the result as a table or chart.

Use case:
- Problem: A sales manager wants to see conversion trends by region over the last week.
- Solution: The manager writes in chat: "Export conversion by region for the last 7 days." The AI agent queries the events table, aggregates data, and returns a summary.
- Result: Report generation time drops from 2 hours to 1 minute.

2. Trend Forecasting

ASI Biont can use BigQuery ML's built-in capabilities or its own machine learning models for forecasting. For example, based on historical sales data, the AI agent builds a time series model (ARIMA or Prophet) and predicts values for the next period.

Example:

You: "Forecast sales for the next quarter based on the last 2 years of data."
ASI Biont: "Running forecast. Using ARIMA model with 12-month seasonality. Forecast for Q3 2026: expected growth of 8-12% compared to Q3 2025. 95% confidence interval: ±3%."

The AI agent doesn't just output numbers—it gives recommendations: "Given the trend, I recommend increasing inventory for the 'Electronics' category by 15% by August."

3. Automated Reporting and Alerts

You can set the AI agent to regularly check data and send notifications. For example:

"Check every morning at 9:00 if revenue has fallen below 1 million rubles. If it has, send me a message with an analysis of the reasons."

ASI Biont executes the query, compares it to the threshold, and if necessary, generates a text report with hypotheses (e.g.: "The drop is due to weekends—Sunday sales are traditionally low. No anomalies detected.").

4. Exploratory Data Analysis (EDA)

When working with a new dataset, the AI agent can autonomously:
- Identify missing values and outliers.
- Build a correlation matrix.
- Identify key drivers of metrics.

Scenario:

You: "Analyze the user_behavior table. Find which factors most strongly influence user retention."
ASI Biont: "Running analysis. Key factors: number of sessions in the first week (correlation 0.78), support response time (-0.45), use of premium features (0.62). I recommend focusing on increasing sessions in the first week—this gives the biggest boost to retention."

Practical Example: Sales Report with Forecast

Problem: A retail chain with 500 stores generates a monthly sales report in BigQuery. The analyst spends 4 hours writing queries, checking data, and building charts.

Solution: The company connects ASI Biont to BigQuery.

Steps:
1. The analyst passes the service account API key in the chat.
2. The AI agent scans the table schema and offers ready-made report templates.
3. The analyst selects: "Generate a monthly report broken down by store, category, and a forecast for next month."
4. ASI Biont executes 15 queries, merges results, builds charts (via the matplotlib library), and forecasts the trend.

Result:

Conclusion: The integration reduced analytical work time by 94% and completely eliminated manual SQL generation.

Why Is This Beneficial?

1. Time savings: Instead of writing SQL queries, you formulate tasks in natural language. According to a McKinsey study (2023), analysts spend up to 60% of their time on data preparation, not analysis. ASI Biont automates this routine.

2. Accessibility for non-technical users: Marketers, product managers, and executives can independently retrieve data from BigQuery without an analyst's help. This reduces bottlenecks in decision-making.

3. Forecasting without Data Science: Built-in machine learning models allow forecasting without hiring an ML specialist. You just say "forecast"—the AI does everything.

4. Flexibility: ASI Biont connects to any service via API. No need to wait for developers to add BigQuery support—connect right now.

How to Get Started?

1. Go to asibiont.com.
2. Create a new AI agent.
3. In the chat, write: "Connect to my BigQuery. Here's the API key: [service account JSON key]."
4. Start asking questions: "What tables are available?", "Show top 10 products by sales", "Forecast the trend for next month."

The entire connection takes 1 minute. No control panels, "add integration" buttons, or waiting—just a dialogue with AI.

Conclusion

BigQuery is a powerful tool, but its potential often remains untapped due to SQL complexity and routine. Integration with ASI Biont turns it into a "talking analyst": you ask questions in natural language, and the AI writes queries, analyzes data, and gives recommendations. Trend forecasting, automated reporting, exploratory analysis—all become available in minutes.

Try it yourself: connect BigQuery to ASI Biont at asibiont.com and see that analytics can be fast and simple.

Sources: Google Cloud BigQuery Documentation (cloud.google.com/bigquery/docs), McKinsey & Company — "The data-driven enterprise of 2025" (2023).