DatriseAI-first ETL

Timely to Amazon S3 Data Lake Integration

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

How Datrise loads Timely into Amazon S3 Data Lake

Datrise syncs Timely's time entries, projects, clients, users, tags, and billable hours 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

Timely: Automatic time tracking for teams and projects.

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

How Timely entities map to Amazon S3 Data Lake

Timely entityAmazon S3 Data Lake objectNotes
time entriestimely_time_entriesid PK · custom fields → nested struct/map fields in Parquet
projectstimely_projectsid PK · linked to timely_time_entries
clientstimely_clientsid PK · linked to timely_time_entries
userstimely_usersid PK · linked to timely_time_entries

FAQ

How does Datrise handle Timely'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 Timely 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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