Microsoft SQL Server to Amazon S3 Data Lake Integration
AI-first ETL from Microsoft SQL Server into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Microsoft SQL Server into Amazon S3 Data Lake
Datrise syncs Microsoft SQL Server's tables, rows, schemas, indexes, primary keys, and change tracking into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.
Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.
Endpoints
Microsoft SQL Server: Microsoft SQL Server transactional databases.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Microsoft SQL Server entities map to Amazon S3 Data Lake
| Microsoft SQL Server entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| tables | mssql_sql_server_tables | id PK · custom fields → nested struct/map fields in Parquet |
| rows | mssql_sql_server_rows | id PK · linked to mssql_sql_server_tables |
| schemas | mssql_sql_server_schemas | id PK · linked to mssql_sql_server_tables |
| indexes | mssql_sql_server_indexes | id PK · linked to mssql_sql_server_tables |
FAQ
How does Datrise handle Microsoft SQL Server's custom fields in Amazon S3 Data Lake?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.
How does the Microsoft SQL Server to Amazon S3 Data Lake sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.
Related pipelines
More destinations for Microsoft SQL Server
- Microsoft SQL Server → Azure Data Lake Storage
- Microsoft SQL Server → Azure Synapse
- Microsoft SQL Server → Spreadsheets
- Microsoft SQL Server → Airtable
- Microsoft SQL Server → CSV Files
- Microsoft SQL Server → MongoDB
- Microsoft SQL Server → Supabase
- Microsoft SQL Server → Neon
- Microsoft SQL Server → PlanetScale
- Microsoft SQL Server → Amazon DynamoDB
- Microsoft SQL Server → Looker
- Microsoft SQL Server → Looker Studio
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Early access
Connect Microsoft SQL Server to Amazon S3 Data Lake the easy way
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