Automate the flow between your data lake and warehouse. Stop manually running COPY commands, writing glue scripts, and babysitting ETL jobs. Redbird keeps your S3 data flowing into Redshift tables—transformed, validated, and ready for analytics.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When new CSV, Parquet, or JSON files land in S3 buckets, automatically parse, validate schema, and COPY into the right Redshift tables. Handle partitioned data, detect schema drift, and maintain load history without manual intervention.
Run scheduled or event-triggered queries in Redshift and export results to S3 in the format you need. Perfect for feeding ML pipelines, sharing datasets with partners, or archiving aggregated reports for long-term storage.
Capture application logs, CloudTrail events, or server logs from S3, parse nested JSON or compressed formats, and load into normalized Redshift schemas. Redbird handles deduplication, timestamp parsing, and incremental updates automatically.
Check file integrity, validate schemas against Redshift table definitions, and enrich records with lookups before loading. Catch data quality issues early and prevent bad data from polluting your warehouse.
Automatically identify aging data based on queries or timestamps, unload to S3 in compressed Parquet format, and maintain external tables for occasional access. Reduce Redshift storage costs while keeping historical data accessible.
When reference data files update in S3—product catalogs, customer lists, geography mappings—automatically refresh corresponding Redshift dimension tables. Maintain consistency between your lake and warehouse without merge script maintenance.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and Redshift 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 Redshift schemas—so it can route data intelligently between storage and warehouse without brittle scripts.
Redbird reads your S3 folder hierarchies, detects partitioning schemes, and maps them to Redshift table structures. It understands column types, distribution keys, sort keys, and encoding—suggesting optimal schemas when creating new tables. When files arrive, it validates against target schemas, handles type casting, and manages COPY operations with the right parameters for compression, delimiters, and NULL handling.
faster than building custom ETL scripts for every S3-to-Redshift pipeline
Redbird can pull from Amazon S3 and Redshift 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 Redshift.
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 Redshift, or from Redshift 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 from any S3 event or Redshift state change and automate what happens next across your data stack.
Fires when objects are created in specified S3 buckets or prefixes, with filtering by file type or naming pattern.
Detects when existing S3 objects are overwritten or updated, triggering reprocessing workflows.
Periodically check S3 paths for new or changed files based on last-modified timestamps.
Write transformed data, query results, or generated reports to specified S3 locations with custom formatting.
Relocate files between buckets or prefixes after processing, or transition to Glacier storage classes.
Extract data from CSV, JSON, Parquet, or compressed files for transformation or validation.
Fires when scheduled or ad-hoc Redshift queries finish, with access to result metadata and row counts.
Monitors specific Redshift tables and triggers when new data is loaded or record counts cross thresholds.
Run queries on a schedule to detect conditions like missing data, anomalies, or freshness issues.
Execute COPY commands from S3 with proper formatting, compression, and error handling configurations.
Execute SQL against Redshift and use results in downstream workflow steps or conditional logic.
Export Redshift query results directly to S3 in Parquet, CSV, or delimited format with partitioning.
Stop maintaining brittle ETL scripts between S3 and Redshift. Redbird automates the data flows your analytics depend on—so your team can focus on insights instead of pipelines.