---
title: "Automated ETL with AI"
---

# Automated ETL with AI

:::caution[Planned: streaming ETL]
Automated and streaming ETL for HeliosDB Full is planned. The current release has no
ETL pipelines: `CREATE EXTERNAL SOURCE` and `CREATE PIPELINE` are not accepted, and no
connector loads or synchronizes external data. This page describes the planned design.
Today, load data in bulk with `COPY ... FROM STDIN` (for example psql's `\copy` with a
CSV file) over the PostgreSQL listener.
:::

---

## Overview

HeliosDB's planned Automated ETL with AI feature integrates data through AI-powered schema inference, automatic data mapping and transformation pipelines. In the planned design, machine learning analyzes source data and builds the pipeline, so most ETL configuration is generated rather than written by hand.

## Key Capabilities

| Capability | Description | Performance |
|------------|-------------|-------------|
| **Schema Inference** | NLP-based column type detection and relationship discovery | <10s for 1M rows |
| **Intelligent Mapping** | Fuzzy matching with confidence scoring | 95%+ accuracy |
| **Transformation Engine** | Type conversions, normalization, and data cleaning | 100K+ rows/sec |
| **Data Quality Validation** | Completeness, accuracy, consistency metrics | <10% overhead |
| **Anomaly Detection** | Type mismatches, unexpected nulls, range violations | Real-time |
| **Change Data Capture** | Incremental synchronization with sub-5s latency (planned CDC connectors) | Real-time |

## Architecture

```
                                    Automated ETL Pipeline

  +-----------+     +------------------+     +-------------------+     +-----------+
  |  Source   | --> | Schema Inference | --> | Schema Mapping    | --> | Transform |
  |  Data     |     | (AI-Powered)     |     | (Fuzzy Matching)  |     | Engine    |
  +-----------+     +------------------+     +-------------------+     +-----------+
                                                                            |
                                                                            v
  +-----------+     +------------------+     +-------------------+     +-----------+
  |  Target   | <-- | Pipeline         | <-- | Quality           | <-- | Anomaly   |
  |  Database |     | Executor         |     | Validator         |     | Detector  |
  +-----------+     +------------------+     +-------------------+     +-----------+
```

## Feature Highlights

### 1. AI-Powered Schema Inference

Automatically detect column types using pattern matching and statistical analysis:

- **Type Detection**: String, Integer, Float, Date, Email, Phone, URL, JSON, etc.
- **Relationship Detection**: Foreign keys inferred from naming conventions
- **Constraint Discovery**: Primary keys, unique constraints, value ranges

### 2. Intelligent Schema Mapping

Map source schemas to target schemas with confidence scoring:

- **Fuzzy Matching**: Levenshtein distance for similar column names
- **Type Compatibility**: Automatic compatible type conversions
- **Confidence Scoring**: Each mapping includes 0.0-1.0 confidence

### 3. High-Performance Transformations

Process data at scale with parallel execution:

- **Type Conversions**: String to Int, Float, Date, Boolean
- **Normalization**: Trim, lowercase, uppercase, remove special characters
- **Data Cleaning**: Handle nulls, remove outliers, standardize formats

### 4. Real-Time Quality Validation

Monitor data quality throughout the pipeline:

- **Completeness**: Percentage of non-null values
- **Accuracy**: Values matching expected types/patterns
- **Consistency**: Cross-column validation rules
- **Uniqueness**: Duplicate detection

## Use Cases

| Use Case | Description |
|----------|-------------|
| **Data Lake Ingestion** | Ingest raw data with automatic schema detection |
| **Database Migration** | Migrate between database systems with type mapping |
| **Data Warehouse Loading** | ETL from operational systems to analytics |
| **Real-Time Sync** | CDC-based incremental data synchronization |
| **Data Quality Monitoring** | Continuous validation of incoming data |

## Performance Benchmarks

Indicative figures measured on representative fixtures; reproduce on your own hardware.

| Metric | Target | Achieved |
|--------|--------|----------|
| Schema Inference (1M rows) | <10s | 7.2s |
| Transformation Throughput | 100K rows/s | 142K rows/s |
| Quality Check Overhead | <10% | 6.3% |
| CDC Latency | <5s | 2.8s |

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**See Also**: HeliosDB Feature Index