Arbor_​Rental

Lookup Tables: Source

Derived from Revenue_Common.Xlm

Rental Data Preparation and Enrichment

This workflow section brings together rental data from multiple sources, filters and classifies it, and then enriches it with additional information. First, the main rental dataset is split into two groups: one filtered for rows with specific distance criteria, and another classified using business rules. These two groups are then combined into a single, unified table. In parallel, mapping and lookup tables are read in, summarized, and enhanced with new columns and unique row identifiers. Additional string processing is applied to further refine the data. Lookup tables for default drivers are also filtered and enriched with calculated fields, preparing all datasets for downstream analysis or reporting.

Transform and Stack Suffix Data for Analysis(Process-Billing_Stage)

This section takes a wide table of suffix-related data and reshapes it into a long format for easier analysis. The process involves filtering for relevant rows, reordering columns, and then "unpivoting" the data—turning multiple columns into rows so each suffix entry is its own record. After unpivoting, columns are renamed for clarity. This transformation is repeated in parallel for several related datasets. Each resulting table is then filtered, grouped, and sorted to remove unnecessary columns and organize the data. Finally, string operations add or adjust suffix information, and duplicate rows are removed, ensuring a clean, analysis-ready dataset where each row represents a single suffix instance with all relevant details.

Stage B → H applies business rules and enrichment to the normalized suffix-level data generated in Stage A. Multiple parallel streams perform joins with reference and assignment tables, apply rule-based classifications, and standardize structures. These streams are then consolidated, validated, and de-duplicated to derive final assignment outcomes. The stage produces a single, business-ready dataset suitable for reporting, reconciliation, and downstream analytics.

ATTRIBUTE TO ABM<: This stage enriches the processed data by combining it with scenario and dimension reference inputs using cross joins, creating all required scenario-level combinations. Multiple VAA components (e.g., CO, BPS, HPS) are appended into a single consolidated dataset. Final calculations and adjustments are applied through formula logic, and the output is structured and written as the final VAA dataset for reporting and downstream consumption.

ATTRIBUTE TO ACCOUNT

1. Transformation at this stages works input for next stage

2. ETL are simple at this stage to make Trobuleshoting easy