An open lakehouse every engine and every team can use.
Your tables are Apache Iceberg files, an open format. Every engine reads the same copy, and nothing is locked in a vendor format. On Databasin Private they also sit in object storage you control.
That matters to the people downstream too. The number in the board deck, the Data App the sales team opens and the notebook the analyst runs all read one table, so they can’t drift apart.
What “open” buys you.
New tables are written as Apache Iceberg. We chose Iceberg because we didn’t want you to need our permission to read your own data.
- Schema changes without rewritesA column arrives, a type widens, and the table keeps working.
- Time travelFor a retention period you set, so “what did this look like on Tuesday?” is a query rather than a restore.
- Every engine reads itTrino, Databasin Sail and anything else that speaks Iceberg, with no translation layer in between.
- Delta Lake still worksExisting Delta tables keep working, and Databasin Sail reads and writes them. New tables default to Iceberg.
Is this a data lake or a warehouse?
The words get used loosely, so here’s the short version. A data lake is cheap storage holding files in open formats. It takes anything you give it, which is also its problem: no schema guarantees, no transactions, and querying it well is somebody’s full-time job.
A warehouse gives you the guarantees but usually wants the data in its own format, on its own storage, at its own price.
A lakehouse keeps the cheap open storage and adds the warehouse guarantees: ACID transactions, schema enforcement and time travel. Apache Iceberg is the part that makes those guarantees possible over plain files.
- StorageRun by us when hosted; your own storage account on Databasin Private.
- FormatApache Iceberg, readable by anything that speaks it. Delta Lake supported.
- GuaranteesACID transactions, schema evolution, time travel.
- If you leaveThey’re open Iceberg files, readable by any engine.
One copy of the data, and one engine for SQL and Spark.
Databasin Sail runs your dashboards, notebooks and PySpark jobs on these tables. If your team already writes ANSI SQL, Trino reads the very same tables. Neither one keeps its own copy.
Databasin Sail
Our own Spark-compatible engine, written in Rust on Apache DataFusion. Runs PySpark and Spark SQL through Spark Connect, reads and writes Iceberg and Delta, answers interactive queries and BI tools, and scales to zero when nobody is using it.
How Sail works →Apache Trino, if you prefer it
Interactive ANSI SQL, including federated queries across catalogs. Available for analysts who already live in Trino’s dialect, on the same tables and permissions.
How the two compare →Already run Databricks? Sail can read your Unity Catalog tables without Databricks compute (preview), or Databasin can query your workspace directly, billed by Databricks. DuckDB and Apache Doris are available for specific jobs; the docs cover when they help.
Hosted, or inside your own Azure.
Databasin Cloud is fully managed and HIPAA-ready from the first day. Most teams start there because it takes about five minutes.
Databasin Private installs the same product inside your own Azure tenant, on your network, under your policies, with your keys. Your data stays in your subscription.
A lakehouse is only worth it once your systems are in it.
Databasin ships 75+ connectors. For about 30 native sources it builds the pipelines, maps the schemas and creates the business-ready views for you.
Point it at your data and see.
Chris Lundeberg, one of our co-founders, runs the technical demos himself. Bring your hardest data problem; he’d rather see that than a slide.
$50 credit · No card