Server processing — the table is read on the backend
Connect to a Delta Lake table path to begin exploration.
What is a Delta Lake explorer?
Delta Lake adds ACID transactions and versioned history on top of Parquet files, and is the default table format for Databricks and many Spark pipelines. This tool connects to a Delta table path or URI, reads its transaction log on the server, and shows you the current version, file count, row count, and a paginated data preview — no Spark cluster required.
Frequently Asked Questions
- What is Delta Lake?
- Delta Lake is an open-source storage layer built on top of Parquet that adds ACID transactions, versioned history (time travel), and schema enforcement — widely used with Apache Spark, Databricks, and other big data platforms.
- What can this tool show me?
- Point it at a local path or remote URI for a Delta table and it reads the transaction log on the server to show the current version, file count, approximate row count, and a paginated preview of the data.
- Does it support time travel across versions?
- The explorer shows the latest table version and its metadata. Version history browsing depends on what the underlying transaction log exposes for the connected table.
- Is my table data uploaded anywhere permanent?
- The table is read on the backend only to compute metadata and generate a preview. It is not copied or stored permanently.