Predictive Query Language (PQL) Explained

2025-10-02 · Source: James Briggs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

Kumo introduces a Predictive Query Language (PQL), a SQL-like syntax designed to define prediction parameters for various business outcomes. For instance, PQL can be used to forecast customer value over a specified period, such as the next 30 days. This involves defining a "target" using a `predict` statement, which specifies the metric to be predicted, like the sum of transaction prices within a future timeframe. Additionally, PQL requires defining an "entity," indicating for whom or what the prediction is being made, enabling granular predictions for individual customers.

Key takeaway

For data scientists and business analysts seeking to implement predictive analytics, understanding Kumo's PQL can streamline the definition of forecasting tasks. Your team should explore PQL for its ability to clearly articulate prediction targets and entities, potentially simplifying the development of models for customer value or other time-series predictions.

Key insights

PQL offers a SQL-like syntax to define predictive targets and entities for business forecasting.

Principles

• Define a clear prediction target.
• Specify the entity for each prediction.

Method

Use PQL's `predict` statement to define the target metric and timeframe, then specify the `for each` entity to break down predictions.

In practice

• Forecast customer lifetime value.
• Predict transaction sums for specific entities.

Topics

• Predictive Query Language
• Customer Value Prediction
• SQL-like Syntax
• Predictive Modeling
• Data Querying

Best for: Data Scientist, AI Data Scientist, Machine Learning Engineer

Related on AIssential

• Data Science as a Service | Kumo AI Full Walkthrough — Kumo AI simplifies complex data science tasks by abstracting GNNs and data pipelines into a service.
• Why Your Data Pipeline Is Slow: A Practical Guide to Indexes — Indexes align logical query intent with physical data access, preventing PostgreSQL from doing unnecessary work at scale.
• From Natural Language to PromQL: A Catalog-Driven Framework with Dynamic Temporal Resolution for Cloud-Native Observability — A catalog-driven framework translates natural language into PromQL, simplifying cloud-native observability with dynamic temporal resolution.
• Chatting with your Data: Conversational Analytics in BigQuery — Enterprise-grade conversational analytics requires robust governance to ensure accuracy and cost control over large datasets.
• [Webinar] TabH2O #2 Webinar Recording — Tabular Foundation Models simplify predictive analytics for structured business data by learning patterns across diverse tables.
• Practical SQL Tricks Every Data Scientist Should Know — Advanced SQL patterns using window functions and self-joins streamline complex data analysis tasks, improving efficiency and scalability.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by James Briggs.