DatriseAI-first ETL

Amazon DynamoDB to Mode Integration

AI-first ETL from Amazon DynamoDB into Mode. Governed entities, incremental sync, typed landing tables.

How Datrise loads Amazon DynamoDB into Mode

Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

Amazon DynamoDB: Serverless key-value and document store on AWS.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Amazon DynamoDB entities map to Mode

Amazon DynamoDB entityMode objectNotes
tablessource_dynamodb_tablesid PK · custom fields → flattened columns for SQL and notebooks
itemssource_dynamodb_itemsid PK · linked to source_dynamodb_tables
partition keyssource_dynamodb_partition_keysid PK · linked to source_dynamodb_tables
sort keyssource_dynamodb_sort_keysid PK · linked to source_dynamodb_tables

FAQ

How does Datrise handle Amazon DynamoDB's custom fields in Mode?

Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.

How does the Amazon DynamoDB to Mode sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

Early access

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