Epiphany

ETL Pipelines

Describe the data work. Epiphany deploys the pipeline — then the same output feeds Data Lab and ML Lab. ETL is the start, not the product.

<3min
To running pipeline
8
Native connectors
4
AI agents in parallel

How It Works

01

Describe the work

Sources, transforms, and optionally the modeling goal: &quot;S3 orders to Snowflake nightly, then train a churn baseline&quot;

02

AI Builds Pipeline

Watch as AI generates optimized code, transformations, and quality checks automatically

03

Deploy & Monitor

One-click deployment with real-time monitoring, alerting, and automatic optimization

Key Benefits

Plain English to a scheduled pipeline on Epiphany Cloud
Same output opens in Data Lab for profile, quality, and anomalies
One click from the pipeline into ML Lab
Native connectors: Postgres, MySQL, S3, GCS, Snowflake, BigQuery, Redshift, REST
Run history, retries, and failure alerts
Promote lab findings and models back onto the job

Production-Ready Features

Fast Deployment

From natural language description to running pipeline on Epiphany Cloud in under 3 minutes

Built-in Quality

Automatic data validation, schema drift detection, and anomaly alerts

24/7 Monitoring

Real-time pipeline health monitoring with automatic issue detection and alerting

Zero Maintenance

Self-healing pipelines with automatic retries and infrastructure management

Real-World Examples

Cloud Data Migration

Ingest CSV files from S3, transform data types, and load to Snowflake with automated schema validation

E-commerce Analytics

Pull daily sales data from Shopify, aggregate metrics, and push to Redshift for business intelligence

Scheduled Data Sync

Sync data from REST APIs on a cron, deduplicate records, and update your warehouse on a schedule

CRM Data Sync

Sync customer data, deduplicate records, and update your data warehouse on a schedule

8 Native Connectors

Connector-aware code — each one knows its own retry policies, pagination, and credentials

PostgreSQL
MySQL
Amazon S3
Google Cloud Storage
Snowflake
BigQuery
Redshift
REST API

Questions about this use case

A plain-English description of sources, transforms, and destination — for example: “Ingest customer CSVs from S3, deduplicate on email, load to Snowflake nightly.” You can add a modeling goal in the same sentence if you want ML Lab later.

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