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Data Compare Agent

Cross-database validation across Oracle, Snowflake, Redshift, Postgres, MySQL, SQL Server & BigQuery. Schema, count, PK distribution & hash comparison strategies.

Database Comparison

Drift Monitor

Database-connected drift detection with KL divergence, PSI, equal-frequency binning. Compare baseline and current distributions across selected columns and time windows.

Data Drift Monitoring

Profile Input

Pick a dataset and sample size for a random profile sample.

Dataset

Sample Size

Column Profiles

Nulls, uniqueness, distributions, and profile notes by column.

Distribution Preview

Quality Flags

Response Payload

Dataset summary, columns, and quality flags as JSON.

⚖ Model Identity

Training Date ?Training DateWhen the model was last trained. Used to assess data staleness and concept drift risk. Example: a model trained in 2019 misses COVID-era shifts.

Domain ?DomainThe industry your model operates in. High-sensitivity domains like lending and finance get a higher concept drift multiplier because market conditions change faster.

☷ Data Source for Drift Analysis (Optional)

Provide a data source to enable data-aware drift analysis. The training date above auto-suggests baseline/detection windows.

Data Source Type

Baseline (Training Data)

Upload Baseline CSV

Drop or click to upload baseline CSV

Detection (Current Data)

Upload Current CSV

Drop or click to upload current CSV

Model ID

Table Name

Columns to Monitor (comma-separated)

Date Column

Baseline Window

From

Detection Window

From

Rolling Frequency

Sample Size

⚙ Model Properties

Target Variable ?Target VariableThe column your model predicts. Example: loan_status (default vs. paid), fraud_flag (fraud vs. legitimate).

Features (comma-separated) ?FeaturesThe input columns used by the model. Checked for data leakage — features that contain future information not available at prediction time. Example: recoveries in a loan default model is leaky because recovery only happens after default.

Class Distribution ?Class DistributionHow the target variable is split between outcomes. Used to detect class imbalance. Example: if 97% of loans are "Fully Paid" and only 3% "Charged Off", the model may just predict the majority class and still look accurate.

Class Name (Majority)

Count / Proportion

Class Name (Minority)

Count / Proportion

Reported Metrics ?Reported MetricsPerformance numbers from model training/evaluation. Cross-referenced with class distribution to flag suspicious results. Example: 99% accuracy with severe imbalance likely means the model just predicts the majority class.

Accuracy ?Accuracy% of all predictions that were correct. Can be misleading with imbalanced classes.

AUC / ROC ?AUC / ROCArea Under the ROC Curve. 1.0 = perfect, 0.5 = random. Values > 0.99 are flagged as suspicious.

F1 Score ?F1 ScoreHarmonic mean of precision and recall. A big gap between accuracy and F1 signals poor minority-class performance.

Recall (Minority) ?Recall (Minority)Of all actual positive cases, what % did the model catch? 50% recall means half go undetected.

$ Financial & Risk

Annual Revenue ($) ?Annual RevenueYearly revenue influenced by the model's predictions. Used to estimate expected annual loss from a stale model. Example: $6M revenue with 15% risk exposure = $900K potential loss.

Refresh / Retrain Cost ($) ?Retrain CostHow much it costs to retrain the model (compute, data engineering, validation). If expected annual loss exceeds this cost, the advisor recommends retraining. Example: $30K retrain cost vs. $135K expected loss → ROI of 4.5x.

Revenue Impact Factor ?Revenue Impact FactorFraction of revenue at risk due to model degradation. Example: 0.15 means 15% of revenue could be impacted by a stale model.

⚙ Advanced Tuning (Optional)

Load ADWIN Sample Scenario

ADWIN Drift Data — Time-Series JSON Array

Correlation Data (JSON) ?Correlation DataJSON object mapping feature pairs to correlation coefficients. Used to detect multicollinearity and feature redundancy.

Custom Weights (JSON) ?Custom WeightsOverride the default check weights used in scoring. JSON object mapping check names to weights (0-1). Example: {"staleness": 0.3, "feature_leakage": 0.2}

☷ Drift Monitor Configuration

Data Source

Step 1: Baseline (Training Data)

Upload Baseline CSV

Drop or click to upload training CSV

Step 2: Detection (Current Data)

Upload Current CSV

Drop or click to upload current CSV

Connection Configuration File (JSON or YAML)

Drop or click to upload .json / .yaml file

View sample connection file formats

JSON

{
  "type": "snowflake",
  "account": "xy12345.us-east-1",
  "user": "analytics_user",
  "password": "********",
  "database": "PROD_DB",
  "schema": "PUBLIC",
  "warehouse": "COMPUTE_WH",
  "role": "ANALYST_ROLE"
}

YAML

type: redshift
host: my-cluster.abc.us-east-1.redshift.amazonaws.com
port: 5439
database: analytics
user: admin
password: "********"
# Optional: iam_role, region

Required fields by database type:
Snowflake: type, account, user, password, database, warehouse
Redshift: type, host, port, database, user, password
PostgreSQL / MySQL: type, host, port, database, user, password
Oracle: type, host, port, service_name, user, password
SQL Server: type, host, port, database, user, password
BigQuery: type, project, dataset, credentials_path

Model ID

Table Name

Date Column

Sample Size (default: 20,000 rows)

Metric Precision

Baseline Window

From

Detection Window

From

Statistical Tests

Columns to Monitor

Available 0

Selected 0

Upload a CSV or browse a table to populate columns

▦ Raw Data Worksheet

Expand

Inspect familiar raw rows from the active dataset and compare them with the drift metrics above.

Dataset

Window Preset

Rows

Load a dataset and click Refresh Worksheet to inspect raw rows.

Rolling Drift Analysis

Expand

Split your data by a date column into time windows and track how drift evolves over time. Rolling analysis uses the current dataset. The first window in the selected rolling range is used as the baseline; each subsequent window is compared against it.

Rolling From

Rolling To

Frequency

Data Compare Agent

Drift Monitor

Comparison Final Result