DatriseAI-first ETL

Snowflake to Amazon S3 Data Lake Integration

AI-first ETL from Snowflake into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Snowflake into Amazon S3 Data Lake

Datrise syncs Snowflake's databases, schemas, tables, views, query history, and warehouses 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

Snowflake: Cloud data platform with separated storage and compute.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Snowflake entities map to Amazon S3 Data Lake

Snowflake entityAmazon S3 Data Lake objectNotes
databasessnowflake_databasesid PK · custom fields → nested struct/map fields in Parquet
schemassnowflake_schemasid PK · linked to snowflake_databases
tablessnowflake_tablesid PK · linked to snowflake_databases
viewssnowflake_viewsid PK · linked to snowflake_databases

FAQ

How does Datrise handle Snowflake'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 Snowflake 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

Early access

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