Sync analytical data from Amazon Redshift to Microsoft SQL Server and operational data back to your warehouse—automatically. Stop writing complex ETL scripts, managing SSIS packages, or manually exporting query results between your AWS data warehouse and enterprise databases.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Push pre-computed analytics, KPIs, and summary tables from Redshift into SQL Server tables that power internal applications. Redbird handles schema mapping, type conversion, and incremental updates so your transactional systems always reflect the latest warehouse insights.
Automatically move completed orders, audit logs, and historical records from SQL Server to Redshift for cost-effective storage and deep analysis. Keep your OLTP database lean while building a comprehensive analytical data lake in AWS.
Keep product catalogs, customer master data, and lookup tables synchronized from SQL Server into Redshift for accurate joins in analytical queries. Redbird detects schema changes and handles SCD updates automatically.
Run segmentation and behavior analysis in Redshift, then push customer scores, segments, and flags back into SQL Server tables that feed your CRM and marketing automation systems. Bridge the gap between analytics and operational execution.
Detect new sales, inventory movements, or customer records in SQL Server and immediately replicate them to Redshift. Power near real-time dashboards and reporting without impacting production database performance with heavy analytical queries.
When application databases need historical context, automatically query and load relevant time-series data from Redshift into SQL Server staging tables. Redbird orchestrates the extract, handles large result sets, and manages batch inserts efficiently.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Redshift and SQL Server 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 both Amazon Redshift's columnar analytics architecture and SQL Server's transactional data structures, automatically handling the schema translation, type mapping, and optimization needed to move data between cloud warehouses and enterprise databases.
Redbird automatically maps Redshift's DISTKEY and SORTKEY optimizations to SQL Server's clustered and non-clustered index structures. It handles VARCHAR encoding differences, translates Redshift's SUPER type to SQL Server JSON columns, converts TIMESTAMP WITH TIME ZONE to DATETIMEOFFSET, and manages IDENTITY column synchronization. The AI detects when you're moving dimension tables versus fact tables and adjusts batch sizes and commit strategies accordingly.
faster than building custom ETL between Redshift and SQL Server
Redbird can pull from Redshift and SQL Server 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 Redshift or SQL Server.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Redshift into SQL Server, or from SQL Server back into Redshift. 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 query result, table change, or scheduled event in either Redshift or SQL Server.
Trigger when a Redshift analytical query returns results meeting specific criteria or exceeds volume thresholds.
Start workflows when scheduled Redshift queries or materialized view refreshes finish executing.
Detect when aggregation pipelines or dbt models insert new summary records into Redshift tables.
Run dynamic SQL queries in Redshift with variables passed from other workflow steps or SQL Server sources.
Load data from SQL Server or other sources into Redshift using optimized COPY commands from S3 staging.
Generate temporary Redshift tables from query results for multi-step analytical workflows or staged syncs.
Trigger workflows when specific SQL Server stored procedures complete, capturing output parameters and result sets.
Detect new or modified records in SQL Server transactional tables using change tracking or timestamp columns.
Start automations when SQL Server Agent jobs finish, whether successful or failed.
Merge Redshift analytics results into SQL Server tables using MERGE statements with custom match logic.
Call SQL Server business logic procedures with dynamic parameters from Redshift query results or workflow context.
Completely refresh SQL Server reference tables with current data from Redshift analytical models.
Stop building custom scripts to move data between Redshift and SQL Server. Redbird AI handles schema mapping, type conversion, and incremental syncs automatically—so your team can focus on using data, not just moving it.