Azure Synapse enables hotels to forecast demand, optimize pricing, and manage overbooking through a streamlined pipeline. Below is a concise, technical overview:
Data Ingestion, Preparation, Forecasting and Visualization
- Sources: Pull data from PMS (CSV exports), competitor pricing via REST APIs (JSON), and event data from Azure Blob Storage.
- Pipeline: Use Copy Data activity in Synapse Pipelines to ingest; trigger via Tumbling Window for daily updates or Event Hubs for real-time streams.
- Storage: Land data in Azure Data Lake Storage (ADLS) as raw CSV/JSON, partitioned by date.
- Tools: Leverage Mapping Data Flows in Synapse.
- Actions: Clean: Filter nulls, cast dates to YYYY-MM-DD using toDate(). Transform: Derive booking lead time with datediff(), compute 7-day rolling averages via window(). Aggregate: Group by date/location for daily occupancy with sum().
- Output: Write to Parquet in ADLS, optimized for Spark queries.
- Platform: Use Synapse Notebooks with SparkML.
- Forecasting: Algorithms: ARIMA (p=1, d=1, q=1) or Prophet with seasonality priors. Features: Lag-7 bookings, binary holiday flags.
- Pricing: Algorithm: Random Forest Regression (n_trees=100). Features: Demand forecast, competitor rates, occupancy %.
- Metrics: Evaluate with MAE (forecast error), RMSE (prediction stability).
- Code: Train with pyspark.ml pipelines, save models to ADLS.
- Revenue Management: Deployment: Batch score daily forecasts via Synapse Spark, real-time pricing with Azure ML endpoints. Integration: Push rates to PMS via REST API (POST /rates).
- Overbooking: Model: Logistic regression for cancellation probability (threshold=0.7). Logic: Set overbooking cap as predicted_cancellations * 1.2. Update: Sync with PMS via Stored Procedure in SQL Pool.
Synapse Pipelines ingest data (e.g., PMS, APIs), Data Flows transform it (e.g., aggregations, cleaning), and Notebooks build models (e.g., ARIMA, regression). These drive dynamic pricing (real-time rate updates) and overbooking (cancellation-based caps), enhancing revenue and efficiency.
