Overview

Feathery enables you to automatically sync workflow and form data into your Snowflake data lake or data warehouse. Whether you need real-time events, incremental updates, or full historical exports, Feathery provides multiple Snowflake ingestion pathways to fit your data engineering workflow.

Feathery supports four Snowflake-compatible sync modes:

1. Table Sync
2. Streaming Sync
3. Bulk Load Sync
4. Snowpipe Sync

Each method is optimized for different volume, latency, and operational needs.

1. Table Sync (Full or Incremental Table Replication)

Feathery’s Table Sync is the easiest and most common way to keep Snowflake tables up to date with your Feathery workflows.

What it does

• Creates (or updates) a Snowflake table mapped to your Feathery workflow schema.
• Automatically adds new columns when you add new fields in Feathery.
• Supports full loads, incremental loads, and merge-based updates.

How it works

Feathery periodically pushes workflow data into Snowflake via Snowflake’s COPY INTO batching or MERGE logic:

• Full syncs: Export all workflow records and rebuild the Snowflake table.
• Incremental syncs: Only send new or updated rows.
• Schema evolution: Feathery detects field changes and updates Snowflake metadata accordingly.

Best for

• Workflows with moderate to high volumes
• Systems that prefer predictable batch ingestion
• Data teams that want clean, normalized tables for analytics or downstream processing

2. Streaming Sync (Real-Time Event Streaming)

Feathery’s Streaming Sync provides continuous, low-latency ingestion of workflow events into Snowflake using Snowflake’s Snowpipe Streaming API or the Snowflake Kafka connector (depending on configuration).

What it does

• Sends new workflow submissions, updates, or triggers to Snowflake in near real time.
• Uses Snowflake’s streaming ingestion layer for sub-second or low-second latency.
• Ensures each event is durable and exactly-once ingested.

How it works

• Feathery emits rows as they occur.
• Snowflake’s streaming endpoint ingests micro-batches immediately.
• Downstream Snowflake tasks can process updates using Streams + Tasks or MERGE logic.

Best for

• Real-time dashboards
• Trigger-based systems
• Mission-critical workflows that require immediate visibility in Snowflake
• Event-driven or microservices architectures

3. Bulk Load Sync (High-Volume File-Based Loads)

Feathery’s Bulk Load option is designed for large exports or backfills. When you need to load millions of workflow records or historical datasets into Snowflake, Bulk Load provides the most efficient approach.

What it does

• Exports workflow data to cloud storage (S3/GCS/Azure) in large, optimized files (CSV/Parquet).
• Uses Snowflake’s high-performance COPY INTO <table> command.
• Supports partitioned loads for improved performance and parallelism.

How it works

1. Feathery compacts records into data files.
2. Files are staged to a Snowflake-compatible cloud location.
3. Snowflake loads them using its native multi-cluster compute engine.

Best for

• Initial full loads
• Re-syncing large tables
• Migrating historical Feathery workflow data into Snowflake
• Organizations with strict ETL orchestration patterns

4. Snowpipe Sync (Continuous Micro-Batch Ingestion)

Feathery’s Snowpipe Sync leverages Snowflake Snowpipe for automated, continuous ingestion of workflow data with cloud notifications.

What it does

• Automatically delivers workflow records into Snowflake as new files become available.
• Relies on Snowflake-managed serverless compute for ingestion.
• Provides near-real-time sync without the cost of streaming ingestion.

How it works

• Feathery generates micro-batch files and writes them to a Snowflake-connected stage.
• Cloud notifications (S3/GCS/Azure) trigger Snowpipe.
• Snowpipe loads the data continuously into your target table.

Best for

• Near-real-time ingestion where cost is a concern
• Low-maintenance architectures
• Workflows where minute-scale latency is acceptable

What you need

• An active Feathery account and form
• An active Snowflake account

Background

Snowflake is a cloud-based data platform that offers data storage, processing, and analytic solutions. It enables businesses to manage and analyze large volumes of data across various clouds, providing flexibility, scalability, and secure data sharing capabilities.

Benefits

Using Snowflake offers benefits such as flexible and scalable data storage and computing, the ability to run multiple types of data workloads, cost-effective data management with separate compute and storage scaling, and secure data sharing across organizations without moving data. It supports real-time analytics and is compatible with various data integration tools and business intelligence platforms.

Considerations

When evaluating Snowflake, consider its compatibility with existing systems, scalability to handle data growth, cost structure for storage and compute resources, security and compliance features, performance for data processing and analytics, ease of use and learning curve for your team, and support and community resources available.