Redbird AI automates the flow between your S3 data lake and Azure SQL databases. Stop manually downloading CSV files, transforming data in spreadsheets, and bulk importing into SQL Server—let AI handle schema mapping, incremental loads, and bidirectional sync across AWS and Azure.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When ETL jobs write processed data to S3 buckets, automatically detect new files and load them into corresponding Azure SQL tables. Redbird maps CSV, Parquet, and JSON schemas to your database structure and handles incremental inserts without duplicate keys.
Move aging transactional records from Azure SQL to S3 on a schedule or when tables exceed size thresholds. Maintain queryable archives in Parquet format while keeping your operational database lean and performant.
When analytics teams drop data extracts into S3, automatically populate Azure SQL staging tables that feed Power BI dashboards. Keep your Microsoft analytics stack current without manual imports or SSIS packages.
Schedule or trigger database exports to S3 for partners, data science teams, or AWS-based applications. Redbird executes your SQL queries, formats output files, and delivers them to designated S3 prefixes with consistent naming.
When new rows appear in Azure SQL, pull matching enrichment data from S3 lookup files—product catalogs, geo mappings, or third-party datasets. Update SQL records with additional fields without maintaining duplicate reference tables.
Track every file upload, deletion, and modification in your S3 buckets by writing event metadata to Azure SQL. Build complete audit trails and data lineage records across your cross-cloud infrastructure.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and Azure SQL 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 object structures and Azure SQL schemas—no need to write parsing logic or mapping configs between flat files and relational tables.
Redbird's AI inspects file formats in your S3 buckets—CSV headers, JSON structures, Parquet schemas—and maps them to Azure SQL tables and columns. It detects data types, handles NULL values, manages primary keys to prevent duplicates, and adapts to schema changes without breaking pipelines. Whether you're loading daily exports or streaming pipeline outputs, Redbird translates object storage into structured database rows.
faster than building custom S3-to-Azure SQL ETL with AWS Lambda, Azure Functions, or SSIS packages
Redbird can pull from Amazon S3 and Azure SQL 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 Azure SQL.
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 Azure SQL, or from Azure SQL 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 event or Azure SQL change—Redbird watches both systems and executes workflows in seconds.
Fires when files land in specific S3 buckets or prefixes, detecting CSV, JSON, Parquet, or other formats.
Triggers when files are removed from S3 or transitioned to Glacier storage classes.
Watches for objects matching naming conventions like date partitions or file type patterns.
Fetch files from S3, parse structured data formats, and extract rows for database loading.
Upload query results or processed data to S3 in CSV, JSON, or Parquet with custom naming.
Move or duplicate S3 objects for archival, staging, or cross-region replication workflows.
Detects inserts in Azure SQL tables, capturing new records for export or cross-system sync.
Fires when specific columns change or rows meet filter criteria in your SQL database.
Runs SQL queries on a schedule and triggers workflows with result sets for export or processing.
Load data from S3 files directly into Azure SQL tables with automatic schema mapping.
Match rows by key and append or update columns with data pulled from S3 sources.
Run custom SQL queries and send result sets to S3 as formatted files for downstream use.
Sync Amazon S3 with Azure SQL in minutes. Redbird handles cross-cloud data movement, schema mapping, and incremental loads—so your team can focus on insights instead of infrastructure.