Redbird AI automates the flow between your S3 data lake and Snowflake warehouse. Stop writing Snowpipe configs, mapping schemas manually, or babysitting batch loads — let AI handle ingestion, transformation detection, and incremental syncs automatically.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When new data lands in S3 buckets, Redbird detects schema changes, creates or updates Snowflake tables, and loads files automatically. Handles CSV, JSON, Parquet, and Avro without manual COPY commands or stage configuration.
Export aggregated datasets, model outputs, or transformed tables from Snowflake to S3 on a schedule or trigger. Perfect for feeding ML pipelines, sharing with external systems, or creating versioned data snapshots in your lake.
Automatically identify aging or infrequently queried tables in Snowflake and export them to S3 for long-term archival. Keeps warehouse costs down while maintaining data accessibility through external tables or rehydration workflows.
Monitor S3 prefixes for new partitions (by date, region, or custom key) and load only new data into Snowflake. Redbird tracks what's been ingested, handles late-arriving data, and prevents duplicate loads without complex state management.
Run AI-powered quality checks on incoming S3 files — detecting schema drift, missing columns, or anomalous values. Surface issues before bad data enters Snowflake, with alerts to Slack or email when validation fails.
Join raw event streams landing in S3 with customer, product, or reference data from Snowflake. Create enriched datasets back in S3 or load directly to warehouse tables, ready for BI tools and dashboards.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and Snowflake with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird understands S3 bucket structures, file formats, and partitioning schemes alongside Snowflake table schemas, stages, and data types — so you can automate the entire lake-to-warehouse pipeline without engineering overhead.
Redbird automatically detects schema from S3 files (CSV headers, JSON structure, Parquet metadata) and maps to Snowflake data types. It understands VARIANT columns for semi-structured data, handles nested JSON flattening, and generates optimal COPY commands with compression and format options. When schemas evolve, Redbird detects changes and suggests or applies ALTER TABLE statements, keeping pipelines resilient without manual intervention.
faster than building custom Snowpipe and Lambda orchestration
Redbird can pull from Amazon S3 and Snowflake simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either Amazon S3 or Snowflake.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Amazon S3 into Snowflake, or from Snowflake back into Amazon S3. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start workflows from any S3 event or Snowflake operation — Redbird connects both systems with full bidirectional control.
Trigger when objects are created in specific S3 buckets or prefixes, with filtering by file type or naming pattern.
Detect when existing S3 objects are overwritten or updated, useful for reprocessing changed source data.
Monitor hierarchical S3 paths (like date-based folders) and trigger when new partitions are detected.
Write files back to S3 buckets from workflow outputs, transformed datasets, or Snowflake query results.
Organize data by moving or copying S3 objects after processing, archival, or validation steps.
Clean up processed files or move them to Glacier storage classes based on workflow completion or age.
Trigger workflows when rows are added or modified in Snowflake tables, enabling downstream actions on fresh data.
Start workflows when scheduled or ad-hoc queries finish running, using results as inputs for reports or exports.
Monitor tables for column additions, type changes, or structure updates to keep downstream systems in sync.
Insert or merge data from S3 or other sources into Snowflake tables with automatic schema mapping and deduplication.
Run custom SQL statements, stored procedures, or dbt models as part of automated workflows.
Programmatically create new tables, add columns, or alter schemas based on incoming data structure changes.
Sync Amazon S3 and Snowflake in minutes. Redbird AI handles schema detection, incremental loading, and pipeline orchestration — so your team can focus on analysis instead of data plumbing.