Answering ‘What Will Happen?’

Once an organization masters understanding the past (Descriptive Analytics) and the reasons behind it (Diagnostic Analytics), the next strategic imperative is anticipating the future.

Predictive analytics uses historical data, statistical modeling, and machine learning (ML) algorithms to identify patterns and determine the likelihood of future outcomes. It answers the question: “What is likely to happen next?”

Examples of predictive analytics include:

• Churn Prediction: Forecasting which specific customers have the highest probability of canceling their subscriptions next month based on their recent usage patterns and support ticket history.
• Demand Forecasting: Predicting inventory needs for specific retail locations over the holiday season based on historical sales, weather patterns, and economic indicators.
• Fraud Detection: Scoring credit card transactions in real-time to determine the probability that a transaction is fraudulent before approving it.

The Predictive Analytics Workflow

Building a predictive capability requires close collaboration between data engineers (who build the pipeline) and data scientists (who build the models).

1. Feature Engineering: The most critical step. Raw data in the lakehouse is transformed into “features” - measurable properties that help the ML model learn. For churn prediction, features might include days_since_last_login, total_lifetime_value, and count_of_support_tickets_last_30_days.

2. Model Training: Data scientists extract a historical dataset containing these features alongside the known outcomes (the “labels” - e.g., did the customer actually churn?). They train algorithms (XGBoost, Random Forests, Neural Networks) to learn the complex mathematical relationships between the features and the outcome.

3. Model Deployment (Inference): The trained model is deployed into production. As new data flows into the lakehouse, the data engineering pipeline calculates the current features for all active customers, passes them to the model, and the model outputs a “churn probability score” for each customer. These scores are written back into an Iceberg table (e.g., dim_customer_risk_scores) for consumption.

Predictive Analytics in the Modern Data Stack

The data lakehouse architecture fundamentally accelerates predictive analytics. Historically, data scientists had to extract massive CSV extracts from the data warehouse to train models on their local laptops or isolated compute instances, creating compliance risks and data staleness.

In the open lakehouse: Direct Access: Data scientists connect their distributed training frameworks (like Ray or Spark MLlib) directly to the Iceberg tables on object storage. They train models on petabytes of data without moving or exporting it.

Feature Stores: Organizations implement feature stores directly on the lakehouse to manage, version, and share the engineered features. The pipeline computes days_since_last_login once, stores it in an Iceberg feature table, and it is reused by the Churn model, the Upsell model, and the Marketing Segmentation model.

Batch Inference: The prediction scores generated by the models are written directly back into the lakehouse as standard Iceberg tables. These prediction tables are then exposed through the Dremio Semantic Layer, allowing business users to view predictive scores (e.g., a “High Risk” badge next to a customer name) directly within their standard BI dashboards.

Learn More

To dive deeper into these architectures and master the modern data ecosystem, check out the comprehensive books by Alex Merced available in our Books section.