Redbird AI syncs your Databricks lakehouse with Oracle enterprise databases automatically. Stop manually exporting transformed data, writing custom JDBC scripts, or rebuilding incremental loads every time your pipeline changes.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically sync cleaned, aggregated data from your Databricks lakehouse back to Oracle DB tables that power enterprise applications. Redbird detects schema changes in your delta tables and handles upserts, managing incremental loads without custom Spark-to-JDBC code.
Pull transactional data from Oracle partitions into Databricks bronze tables on schedule or trigger. Redbird understands Oracle partitioning schemes and translates them to optimized delta lake ingestion patterns, preserving performance and data lineage.
When Databricks notebook jobs complete dimension enrichment, push results to Oracle staging tables. Redbird tracks job run IDs, validates row counts match between systems, and alerts on discrepancies before downstream reports consume stale data.
Monitor Oracle DB tables for statistical changes in key columns—distribution shifts, null rate increases, or outlier patterns. When detected, automatically kick off Databricks ML pipeline jobs to retrain models on fresh data, maintaining prediction accuracy without manual monitoring.
Automatically move aged Oracle partitions to Databricks delta tables with compression and column pruning. Redbird handles Oracle export, Parquet conversion, and metadata reconciliation, giving you queryable archive access at lakehouse economics instead of enterprise storage costs.
Aggregate Databricks cluster logs that track Oracle JDBC read performance, join patterns, and partition scan metrics. Build automated reports identifying slow Oracle queries, recommending index candidates, and tracking data volume trends across your integration layer.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Databricks and Oracle DB 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 Databricks delta table schemas, partition strategies, and notebook workflows alongside Oracle DB table structures, constraints, and PL/SQL procedures—bridging lakehouse and RDBMS semantics automatically.
Redbird parses your Databricks data frames, delta merge operations, and Spark SQL transformations while simultaneously understanding Oracle table partitions, foreign keys, and trigger logic. It automatically maps complex types between systems—converting Databricks array and struct columns to Oracle nested table patterns, handling timestamp timezone differences, and translating delta lake change data feed into Oracle flashback-compatible inserts. No more manual schema translation or brittle custom connectors.
faster than building Spark-to-Oracle connectors with custom JDBC code
Redbird can pull from Databricks and Oracle DB 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 Databricks or Oracle DB.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Databricks into Oracle DB, or from Oracle DB back into Databricks. 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 Databricks job completion or Oracle table change—Redbird connects the events that matter across your lakehouse and transactional database.
Trigger workflows when specific notebooks finish, whether successful or failed, with access to run metadata and output tables.
Detect when new date or category partitions land in delta tables, capturing partition keys and row counts for downstream processing.
Fire automation when models are logged to Databricks MLflow registry, including version numbers, metrics, and training parameters.
Push structured data into existing or new delta tables with schema evolution, partition management, and merge key handling.
Run custom SQL against Databricks SQL warehouses, retrieving results or materializing views based on external triggers.
Start specific notebook or pipeline jobs with custom parameters, passing Oracle metadata or row identifiers as job inputs.
Monitor specific Oracle tables and trigger when row counts cross defined limits, signaling batch readiness or data volume anomalies.
Detect partition DDL changes in Oracle tables, capturing partition names and ranges for lakehouse synchronization workflows.
Track when Oracle gathers stats on key tables, indicating data refresh cycles or signaling optimal extract timing windows.
Write rows to Oracle tables with merge key logic, handling primary key conflicts and respecting existing constraints and triggers.
Call existing PL/SQL procedures with parameters from Databricks results, integrating lakehouse outputs into enterprise application logic.
Generate temporary Oracle tables matching Databricks delta schemas for ETL landing zones, with automatic type mapping and indexing.
Connect Databricks and Oracle DB in minutes. Redbird handles schema mapping, incremental sync logic, and lakehouse-to-RDBMS translation so your team can focus on transformations, not integration plumbing.