Your Own Open Iceberg Lakehouse
Every PlaidCloud tenant includes a high-performance lakehouse built on open standards. It powers your workflows, dashboards, and AI apps, with no separate warehouse to license or integrate.
Open standards, no lock-in
Apache Iceberg tables you actually own
Your data lives in open Iceberg tables, a format supported across the modern data ecosystem. There is no proprietary storage silo: tables stay portable, readable by other engines, and fully yours.
- Open Apache Iceberg table format
- No proprietary warehouse silo or export tax
- Connect other tools to the same tables when you need to
Time-travel, snapshots, and compare
Every table keeps its history
Tables keep snapshots automatically, so you can view any table as of an earlier point in time, compare two versions side by side, and revert in place when a load or model run goes wrong. Month-end mistakes stop being restore projects.
- View any table as of a prior snapshot
- Compare and merge table versions
- Revert in place, no backup restore required
One store under everything
Workflows, dashboards, and apps read the same tables
Because the lakehouse is built in, your allocation models, dashboards, and AI-built apps all read the same governed tables. Numbers match everywhere, lineage is traceable end to end, and there is no synchronization job between a prep tool and a warehouse.
- High-performance parallel engine on enterprise volumes
- One set of governed tables behind every report and app
- Roles and permissions applied at the data layer
Frequently Asked Questions
What is a data lakehouse?
A lakehouse combines warehouse-style tables and performance with open, file-based storage. PlaidCloud ships its own lakehouse built on Apache Iceberg, so your workflows, dashboards, and apps all run on one governed store without a separate warehouse license.
Why does the open Iceberg format matter?
Iceberg is an open standard, so your data is stored in a format many engines can read, not a proprietary vendor silo. That means no lock-in: your tables remain portable and accessible even outside PlaidCloud.
What is table time-travel?
Every PlaidCloud table keeps snapshots, so you can view a table exactly as it was at an earlier point, compare versions, and revert in place if a load or model run goes wrong.
Do we still need Snowflake or another warehouse?
No. The lakehouse and its high-performance engine are part of the platform. If you already have a warehouse, PlaidCloud connects to it as a source or a target, but nothing in PlaidCloud requires one.
See your data in a lakehouse.
Bring a sample extract to a 30-minute session and see time-travel, compare, and dashboards on your own tables.