This guide walks through the complete DeltaMax workflow — from generating synthetic credit bureau data, running data quality checks (anomaly detection, T-test, PSI, mismatches), to uploading the outputs to Google Cloud Storage and loading them into BigQuery for reporting and visualization in Looker Studio.

It provides the commands and structure required to run each step end-to-end and adapt it to your project setup.

This guide walks through the complete DeltaMax workflow and is organized into three main sections:

Data Generation – creating synthetic credit bureau datasets for the previous and current month.
Data Quality Checks – running anomaly detection, T-tests, PSI analysis, data type mismatch checks etc.
BigQuery Integration – uploading outputs to Google Cloud Storage, loading them into BigQuery, and preparing them for reporting and visualization in Looker Studio.

Step-1 : Search for DeltaMax on Google Cloud Marketplace

https://console.cloud.google.com/marketplace/product/katalyststreet-public/sq-33833?hl=en&project=api-project-266032973323

1) You will need to create a project on Google Cloud

2) The project should be associated with your corporate billing account.

Step-2 : Deploy the DeltaMax Virtual Machine in your Organization

Getting the DeltaMax VM provisioned and configured within your Organization Settings. Check the settings for

VPC
SubNet
Zones
Firewalls

Step-3 : Ensure that VM is updated, if required install python3-full latest version, venv

Data Generation

Step-4 : DeltaMax_synthetic_data_generator.py

Generates synthetic datasets for the previous month (April) and the current month (May), where the May dataset intentionally includes injected anomalies that will be used for training and validation.

Step-5 : Install jupyter nbcovert to convert python note books to python text files for running on CLI environment and pandas for running python moudles

Data Quality Check

Step-6 : M1 – Data Loading & Initial Checks

This step loads the generated datasets, standardizes the businessID, and verifies that both monthly files share common columns. It confirms the data is ready for the next DeltaMax modules. It confirms that the data is successfully loaded and ready to be passed to the next stages of the DeltaMax pipeline.

Step-7 : M2 – Column Type Analysis & Filtering

This step identifies numeric, boolean (bit), and string columns and prepares the correct columns for anomaly analysis.

Step-8 : M3 – Anomaly Detection (IQR + Isolation Forest)

Detects statistical and ML-based anomalies in the dataset.

Step-9 : M4 – Variance Analysis
Generates descriptive statistics to understand data distribution and month-to-month variation.

Step-10 : M5 - Inter-File Comparison(T-Tests)

Compares common numerical columns between months using T-tests to detect statistically significant mean changes.

Step-11 : M6 - Inter-File Comparison(PSI)

Calculates Population Stability Index (PSI) to measure distribution shifts between months.

Step-12: M7 - Decimal Formatting Mismatches

Checks for inconsistencies in decimal precision across common numerical columns.

Step-13: Optional Run M8_Bit field changes (Skipping this step)

Step-14: M9 - String Length Mismatches

Detects inconsistencies in string lengths between previous and current datasets and saves the results to a log file.

Step-15: M10 - Data Type Mismatch Detection

Identifies data type inconsistencies between previous and current datasets and logs the results.

Step-16: M11 - Data Preprocessing and Imputation

preprocesses data and fill missing numeric values using imputation.

Step-17: M12 - Business Unique Analysis

Runs the Business Uniqueness module to compare previous vs current month data and identify businesses appearing in only one dataset.

Step-18: M13 - Create A or B Merged file with col_A and col_B

Merges Dataset A and Dataset B into a single combined file using common keys and saves the merged dataset.

Google Cloud Storage & BigQuery Integration

Step-19: Create a gcloud storage and load the csv files

Note: You can modify the destination table names as needed based on your project naming conventions before loading the data into BigQuery.

Create bucket

Upload generated CSV files to GCS

Step-20: Verify GCP configuration

Step 21 — Create BigQuery Dataset

Step 22 — Load CSV files into BigQuery tables

Step 23 — Run SQL to create anomaly analysis tables

Update project ID inside SQL files before running.

Note: The outputs from all data quality methods (Anomaly Detection, T-Test, PSI, and other checks) are automatically saved in the log files generated during the Data Quality Checking step. These log outputs can be used as source files for building visualizations in Looker Studio and creating dashboards accordingly.

Step-24 : Load the Big Query Tables to Looker and create reports and Visualizations as needed

Step-24: Reach out to Katalyst Street Professional Services for Custom Visualizations and Reports