Delta Lake is the open table format behind Databricks and Microsoft Fabric. Lima writes Delta tables with a transaction log on S3, ADLS or GCS, applies changes with MERGE and keeps history through time travel, so the same tables serve Spark, Databricks, Fabric and Trino.
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 |
|---|---|---|
| Delta writes and MERGE | Appends and merges rows with a transaction log. | Landing and current state. |
| Time travel | Reads earlier versions. | Audits and restores. |
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 |
|---|---|
| Delta tables | current state by key |
| Transaction log | every change recorded |
| Pattern | Supported |
|---|---|
| Full load | Yes |
| Incremental | Yes |
| Change capture | MERGE |
| Streaming | micro-batch appends |
| Write-back | — |
Small files and log growth on high-frequency loads.
The prerequisites are what the team that runs Delta Lake 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.
