One universal ingestion endpoint
Send application events, database rows, batch exports, and backfills through a single HTTP API instead of wiring a connector for each source.
OrcaSheets Data Lake
OrcaSheets Data Lake ingests database rows, app events, and batch exports through one JWT-authenticated HTTP endpoint. It is designed for teams that want those rows available in OrcaSheets for queries, dashboards, and AI Reports without building a separate warehouse stack.
Overview
OrcaSheets Data Lake is the ingestion and storage layer behind the OrcaSheets analytics experience. It lets teams send application events, database rows, and batch exports into one workspace through a JWT-authenticated HTTP endpoint.
The product is aimed at teams that have data but not a full data stack, including Shopify sellers, early-stage SaaS, D2C brands, and teams running analytics from an app database. Once data is ingested, it can be queried in OrcaSheets with plain-English search, dashboards, and AI Reports without first building a separate warehouse pipeline.
Features
Workspace-scoped access
The endpoint uses workspace JWT authentication, and the workspace model keeps client silos isolated at the ingestion layer.
Structured batch payloads
Use a stable `event_type` for each dataset and map incoming row data into `metric_name` and `metric_value` fields before posting JSON batches.
Batch and backfill friendly
Send data in real time or as scheduled batches, including historical backfills and nightly sync scripts.
Query and reporting from the same workspace
Once data lands, it is available in the OrcaSheets workspace for plain-English queries, dashboards, and AI Reports.
Ingestion plus analytics in one place
The product materials describe a workflow that keeps ingestion, storage, and analytics together, so teams do not need to assemble separate event, ETL, warehouse, and BI tools.
Use Cases
Centralize operational analytics ingestion
Push application events or database rows into a single workspace when you want analytics available without assembling separate ETL, warehouse, and BI layers.
Import historical data
Backfill historical orders, users, or revenue data in batches when you need older records available for analysis before setting up ongoing syncs.
Automate recurring batch loads
Send nightly sync jobs or other scheduled exports through the same endpoint so ingestion stays consistent as volume grows.
Load app and business events
Route product, customer, or operations events from internal services and scripts into OrcaSheets for day-to-day reporting.
Move from manual to automated ingestion
Start with a manual upload or small script, then keep the same endpoint when you later automate the pipeline.
Pros and Cons
Pros
- Consolidates ingestion into one endpoint for databases, apps, batch jobs, and backfills.
- Keeps data available in the same OrcaSheets workspace used for queries, dashboards, and AI Reports.
- Supports both real-time and batch workflows, which makes it usable for manual syncs and scheduled automation.
- Uses workspace-scoped authentication, which helps keep client data isolated.
Cons
- The source materials do not document a broad list of built-in source connectors for this page; the core emphasis is the universal ingest API.
- Using the endpoint requires a workspace JWT and a defined payload structure, so teams need a small integration step before data can flow.
FAQ
How do I send data into OrcaSheets Data Lake?
The universal endpoint accepts rows from databases, applications, scheduled jobs, and one-off backfills. You request a workspace JWT, build a JSON payload with an `event_type` and `records`, then POST batches to the endpoint.
How do I authenticate?
The source pages say access starts with a workspace JWT from OrcaSheets. Requests are authenticated with that JWT in the `Authorization: Bearer` header.
Can I send large datasets in batches?
The docs describe batching as the normal approach for larger loads, and the home page notes that hundreds of rows can be sent per call for backfills and scheduled syncs.
What can I do with data after it is ingested?
Data ingested into Data Lake becomes available in your OrcaSheets workspace for queries, dashboards, and AI Reports. The product pages also say analysts can use plain-English questions alongside dashboards.
Who is OrcaSheets Data Lake for?
The source material positions Data Lake for teams with data but no data stack, including Shopify sellers, early-stage SaaS, D2C brands, and similar app-database-driven teams.
Quick Facts
- Category
- Data Lake
- Primary workflow
- JWT-authenticated universal HTTP ingestion
- Primary users
- Teams with data but no full data stack
- Outputs
- Queries, dashboards, and AI Reports in OrcaSheets
- Pricing signal
- Free plan, paid plans, and contact-sales options are listed on the pricing page
- Source domain
- orcasheets.io
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