Redbird AI syncs data between Amazon S3 and Databricks automatically — no more writing Spark notebooks to ingest new S3 files, manually triggering Delta refreshes, or building custom pipelines to move processed data back to storage. Automate the full lakehouse workflow from raw file landing to analysis-ready tables.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When CSV, Parquet, or JSON files land in S3 buckets, Redbird automatically ingests them into the correct Delta Lake tables in Databricks with schema validation and partitioning. Data teams stop manually monitoring buckets and running ingestion notebooks every time upstream systems drop new files.
After transformations complete in Databricks, Redbird writes output tables back to S3 in Parquet format for BI tools, data warehouses, or ML pipelines that consume from storage. Teams eliminate manual export jobs and keep downstream systems synced with the latest processed data.
When files arrive in designated S3 paths or partitions, Redbird kicks off corresponding Databricks workflows — incremental loads, feature engineering pipelines, or ML retraining jobs. Data engineers stop scheduling jobs on fixed intervals and process data immediately when it's available.
Redbird identifies Delta tables that haven't been queried recently in Databricks and automatically exports them to S3 Glacier storage classes, then drops the warm copies. Analytics teams maintain data governance requirements while cutting storage costs on historical data that's rarely accessed.
CloudTrail logs, S3 access logs, and bucket event data flow continuously into Databricks tables for analysis of storage patterns, cost attribution, and data lineage tracking. Platform teams gain visibility into who's accessing what data without building custom log aggregation pipelines.
Redbird monitors ongoing S3-to-Databricks data flows and alerts teams when source files arrive with schema changes, data quality issues, or unexpected formats that could break downstream pipelines. Data engineers catch problems before they cascade through the lakehouse and corrupt analytics tables.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and Databricks 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, object metadata, and file formats alongside Databricks Delta Lake schemas, Unity Catalog namespaces, and cluster configurations — so syncs work correctly without custom code.
Redbird maps S3 prefixes and partitioning schemes to Databricks catalog structures automatically, handling schema evolution in Delta tables as source files change. It recognizes common data lake patterns — raw/bronze/silver/gold hierarchies, date-based partitions, and multi-format ingestion — and configures the right read/write operations for Parquet, JSON, CSV, and Avro files. When tables use features like Z-ordering, liquid clustering, or change data feed, Redbird preserves those optimizations during syncs.
faster than writing custom Spark notebooks for every S3 ingestion pattern
Redbird can pull from Amazon S3 and Databricks 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 Databricks.
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 Databricks, or from Databricks 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 any S3 bucket event or Databricks job status, then take action across both systems.
Triggers when files are uploaded to specified S3 buckets or prefixes, with filtering by file type or size.
Fires when S3 object tags, storage class, or metadata attributes are modified.
Monitors total data volume in S3 paths and triggers when thresholds are exceeded for cost management.
Uploads files or datasets to specific S3 paths with partitioning and format conversion.
Moves or replicates S3 objects across buckets or regions based on workflow logic.
Modifies S3 object metadata, lifecycle policies, or transitions data to Glacier tiers.
Fires when Databricks jobs finish, enabling downstream actions based on pipeline completion status.
Monitors Delta Lake tables for new data commits or schema changes via Delta transaction logs.
Tracks Databricks compute lifecycle events for cost tracking and workflow orchestration.
Executes Databricks notebooks or workflows with parameterized inputs from other systems.
Appends, upserts, or overwrites data in Unity Catalog tables with schema enforcement.
Executes SQL against Delta tables and surfaces results to downstream tools or alerts.
Stop writing glue code between S3 and Databricks. Redbird AI automates lakehouse ingestion, Delta sync workflows, and pipeline orchestration so your team can focus on building features, not maintaining infrastructure.