Implementation notes

The PySpark workflow behind the page.

The goal is not to build a heavy dashboard, but to show an end-to-end analytical pipeline: synthetic data, transformations, SQL summaries, ML metrics and a business-readable output.

1 · Data

Historical quarterly records simulate a financial-services portfolio.

2 · Features

Ratios convert statements into risk signals.

3 · Trends

Window functions detect quarter-on-quarter deterioration.

4 · SQL

Sector and rating summaries translate rows into business cuts.

5 · ML

A train/test Spark ML baseline estimates distress probability.

Why each step matters

These choices were made to resemble day-to-day analytics work: start with data quality and context, engineer features, summarise impact, then add modelling only where it helps prioritisation.

For non-technical readers

The page answers three practical questions: who needs attention, why, and how much exposure is involved.

PySpark feature engineering

Window functions compare each entity with its own previous quarter, which is closer to monitoring work than a one-off spreadsheet snapshot.

DataFrame + Window functions

w = Window.partitionBy('company_id').orderBy('year', 'quarter')
df = (df
  .withColumn('net_debt_to_ebitda', net_debt / ebitda)
  .withColumn('current_ratio', current_assets / current_liabilities)
  .withColumn('prev_revenue_m', lag('revenue_m').over(w))
  .withColumn('revenue_growth_pct', (revenue - prev_revenue) / prev_revenue * 100)
  .withColumn('financial_stress_score', leverage + liquidity + coverage + growth)
)

Spark SQL business summaries

SQL is used after feature engineering because it is readable for analysts and practical for stakeholder cuts such as sector concentration.

Exposure at risk by sector

SELECT sector,
       SUM(exposure_m) AS total_exposure_m,
       SUM(CASE WHEN risk_tier = 'High' THEN exposure_m ELSE 0 END) AS high_risk_exposure_m,
       AVG(financial_stress_score) AS avg_stress_score
FROM latest_features
GROUP BY sector
ORDER BY high_risk_exposure_m DESC

Spark ML validation

The model is deliberately simple and interpretable. The important point is the workflow: split data, train, evaluate, then publish metrics alongside the business rules.

Train/test ML baseline

train, test = model_df.randomSplit([0.8, 0.2], seed=42)
pipeline = Pipeline(stages=[VectorAssembler(...), StandardScaler(...), LogisticRegression(...)])
model = pipeline.fit(train)
predictions = model.transform(test)
auc = BinaryClassificationEvaluator(metricName='areaUnderROC').evaluate(predictions)

Open full source Open generated JSON