Redbird AI automates the sync between your S3 data lake and BigQuery warehouse. Stop manually loading files, writing brittle Python scripts, or maintaining complex ETL jobs just to get S3 data queryable in BigQuery.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When new data files land in S3 buckets, Redbird automatically ingests them into the correct BigQuery tables with proper schema mapping. No more scheduled jobs or manual CSV uploads—your warehouse stays current with your data lake.
Export BigQuery query results, aggregated tables, or ML predictions back to S3 for consumption by non-GCP tools. Redbird handles partitioning, file formatting, and incremental updates so your S3-based pipelines get fresh analytical outputs.
Redbird reads raw application logs, server events, or clickstream data from S3, enriches them with lookups or transformations, then loads clean records into BigQuery. Turn unstructured S3 files into structured warehouse tables automatically.
Move older partitions or infrequently queried BigQuery data to S3 storage classes for long-term archival. Redbird exports tables on schedule, maintains data lineage, and reduces BigQuery storage costs while keeping data accessible.
Detect when source data in S3 changes—new files, deletions, or schema updates—and automatically refresh corresponding BigQuery tables or materialized views. Keep your warehouse synchronized with upstream data lake changes without polling or cron jobs.
Run scheduled BigQuery analytics queries and deliver results as formatted CSV, Parquet, or JSON files to specific S3 paths. Redbird handles query execution, result formatting, and S3 uploads so stakeholders get automated data deliveries.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and BigQuery 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 AI understands S3 object structures and BigQuery schemas, automatically mapping file formats, partitions, and data types across both platforms.
Redbird parses S3 file formats (CSV, JSON, Parquet, Avro) and intelligently maps them to BigQuery table schemas. It handles nested structures, detects schema changes, and manages partitioning strategies automatically. Whether you're syncing event logs, database exports, or analytics outputs, Redbird understands how S3 objects should translate into BigQuery columns and generates the right DDL and load patterns.
Faster than building custom S3-to-BigQuery loaders
Redbird can pull from Amazon S3 and BigQuery 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 BigQuery.
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 BigQuery, or from BigQuery 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 automations from S3 object events or BigQuery job completions, then take action in either system.
Trigger when new objects are created in specific S3 buckets or prefixes.
Detect when S3 objects are updated or removed from watched locations.
Monitor S3 bucket metrics and trigger when storage usage crosses defined limits.
Write data, reports, or exports to specified S3 paths with custom naming.
Relocate files between buckets or prefixes based on workflow logic.
Remove objects or transition them to Glacier storage classes automatically.
Trigger workflows when scheduled or ad-hoc BigQuery queries finish running.
Detect when BigQuery tables receive new data or schema modifications.
Monitor BigQuery dataset metrics and trigger on storage or row count changes.
Insert, append, or replace BigQuery table data from workflow results.
Execute parameterized queries and use results in downstream automation steps.
Modify BigQuery table definitions, add columns, or create new tables programmatically.
Sync Amazon S3 with BigQuery in minutes. Stop writing data pipeline code and let Redbird handle the automation between your data lake and warehouse.