Orchestration with JavaScript and Python

The **Resource Surveillance & Integration Engine**, known as surveilr, offers extensive built-in capabilities for ingestion, ETL, ELT, and other integration tasks. Many of these tasks can be orchestrated declaratively within **SQL** using the SQLite ecosystem alongside the extension functions and virtual tables provided by surveilr.

Orchestration with Declarative SQL in surveilr

By leveraging the versatility of SQLite, surveilr can handle a range of transformations, integrations, and data workflows, all from within the database itself. This SQL-first approach provides:

• Portability: SQL scripts work across any surveilr installation
• Reproducibility: Declarative definitions ensure consistent results
• Auditability: Every transformation is logged and traceable

The Need for Advanced Orchestration

While SQL-based orchestration covers many use cases, some scenarios require the flexibility of general-purpose programming languages:

• Complex Business Logic: Multi-step decision trees and conditional workflows
• External API Integration: REST calls, webhooks, and third-party services
• Custom Transformations: Machine learning preprocessing, image processing
• Parallel Processing: Fan-out operations across large datasets

TypeScript and JavaScript for Orchestration

TypeScript/JavaScript integration enables:

import { RSSD } from 'surveilr';

const db = new RSSD('compliance.db');

// Query and transform data
const controls = await db.query(`
  SELECT * FROM compliance_controls 
  WHERE status = 'pending'
`);

// Apply business logic
for (const control of controls) {
  const assessment = await evaluateControl(control);
  await db.execute(`
    UPDATE compliance_controls 
    SET status = ?, assessment = ?
    WHERE id = ?
  `, [assessment.status, JSON.stringify(assessment), control.id]);
}

Python for Advanced Integration Workflows

Python's rich ecosystem enables sophisticated data processing:

import surveilr
import pandas as pd

# Connect to RSSD
db = surveilr.connect('analytics.db')

# Load data into pandas for analysis
df = pd.read_sql('SELECT * FROM metrics', db.conn)

# Apply machine learning
from sklearn.ensemble import IsolationForest
model = IsolationForest()
df['anomaly'] = model.fit_predict(df[['value']])

# Write results back
df[df['anomaly'] == -1].to_sql('anomalies', db.conn, if_exists='replace')

Multi-Language Integration for Compliance and Evidence Gathering

Organizations can leverage the best tool for each task:

• SQL: Core data transformations and queries
• Python: Data science and machine learning workflows
• TypeScript: API integrations and web services
• Rust/C++: High-performance processing for large datasets

Conclusion

surveilr's polyglot architecture ensures that teams aren't limited by language constraints. Whether you prefer SQL's declarative elegance, Python's scientific computing capabilities, or TypeScript's async workflows, surveilr integrates seamlessly with your existing toolchain.