Apache Iceberg is an open table format that turns files on object storage into tables with snapshots, schema evolution and hidden partitioning. Lima writes Iceberg tables registered in a Glue, REST or Nessie catalog so that Trino, Spark, Athena, Snowflake and Databricks read the same archive without another copy.
Each method has a job. The first load uses the bulk method; the daily run uses the incremental or change-capture method; write-back uses the interface the system itself uses.
| Method | What it does | When it is used |
|---|---|---|
| Iceberg writes | Appends and merges rows as new snapshots. | Archives and history. |
| Catalog registration | Registers tables in Glue, REST or Nessie catalogs. | Every load. |
The objects are the tables, views or records Lima reaches through the methods above. The patterns say how a copy is made and kept current.
| Object | Plain meaning |
|---|---|
| Tables and snapshots | every load is a snapshot you can query back to |
| Manifests | the file lists Iceberg tracks |
| Pattern | Supported |
|---|---|
| Full load | Yes |
| Incremental | Yes |
| Change capture | merge-on-read or copy-on-write |
| Streaming | micro-batch snapshots |
| Write-back | — |
Compaction cadence against query patterns.
The prerequisites are what the team that runs Apache Iceberg tables grants. The four steps are the same for every connector.
Incoming data = valid records + exception records. A row that fails a check is kept, routed to the person who owns it with a deadline, and replayed once fixed. The load carries on with the valid rows and the report says exactly what was held back. How exceptions work →
Lima connects from outside through the interfaces listed above with credentials your administrators grant. Where a vendor requires its own agent or add-on for a method, the prerequisites say so.
Parallel reads are capped and the cap is agreed with the team that runs the system. Large reads run inside an agreed run window. Change capture keeps the daily volume small.
A report per run: rows in, rows out, control totals and variance per table, the exceptions held back and who owns them, and whether the deadline was met.
Yes. Most estates start with one source, one target and one dataset, then add tables and patterns on the same connection.
A trial connects this system, runs a first load and ends with a reconciliation report. Your administrators see the prerequisites before anything is installed.
