DatriseAI-first ETL

Typeform Amazon S3 Data Lake

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

How Datrise loads Typeform into Amazon S3 Data Lake

Datrise syncs Typeform's forms, responses, questions, workspaces, and completion metrics 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

Typeform: Conversational forms and response analytics.

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

How Typeform entities map to Amazon S3 Data Lake

Typeform entityAmazon S3 Data Lake objectNotes
formstypeform_formsid PK · custom fields → nested struct/map fields in Parquet
responsestypeform_responsesid PK · linked to typeform_forms
questionstypeform_questionsid PK · linked to typeform_forms
workspacestypeform_workspacesid PK · linked to typeform_forms

FAQ

How does Datrise handle Typeform'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 Typeform 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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