> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/build/move/supported-sources-and-destinations.md).

# Supported sources and destinations

The sources Nilus can move data from, the destinations it can move data to, and which modes each supports.

What you can move depends on the mode. Batch reaches the widest set of sources; CDC and streaming are narrower because they depend on the source exposing a change log or an event stream. This page is the quick map. For per-connector prerequisites, options, and YAML, follow the links into References.

## Sources by data movement type

### Batch

Batch reaches the widest set of sources: databases, warehouses, and SaaS platforms, including Snowflake, Databricks, PostgreSQL, MySQL, MS SQL Server, AWS Redshift, Google BigQuery, Delta Lake, Salesforce, HubSpot, Stripe, Google Analytics, Google Sheets, and more. If a source isn't listed by name, use a [custom source](https://v2.dataos.info/references/resources/nilus/batch/custom-sources) instead.

See the full, current list in [supported batch sources](https://v2.dataos.info/references/resources/nilus/batch/batch-sources) in References. New connectors ship there first.

### Change data capture (CDC)

CDC supports 5 transactional sources, each needing source-side setup (a transaction log, binlog, oplog, or CDC tables):

PostgreSQL, MySQL, MongoDB, Microsoft SQL Server, IBM Db2.

See [supported CDC sources](https://v2.dataos.info/references/resources/nilus/cdc/cdc-sources) in References.

### Streaming

Streaming supports 2 brokers:

Apache Kafka (topic-based streams) and NATS JetStream (subject-based persistent streams).

See the [stream pipelines reference](https://v2.dataos.info/references/resources/nilus/stream/sample-configs) in References.

## Destinations

You configure the destination as a sink, addressed through a Depot or a direct connector URI, depending on the destination:

| Destination group          | Targets                                   | Modes      | Strategies                       |
| -------------------------- | ----------------------------------------- | ---------- | -------------------------------- |
| DataOS Lakehouse           | AWS-backed, Azure-backed, GCP-backed      | Batch, CDC | `append`, `merge`, `replace`     |
| Cloud warehouses           | BigQuery, Databricks, Redshift, Snowflake | Batch, CDC | `append`, `merge`, `replace`     |
| Databases                  | Microsoft SQL Server, PostgreSQL          | Batch, CDC | `append`, `merge`, `replace`     |
| Databases                  | MongoDB                                   | Batch, CDC | `merge`, `replace` (no `append`) |
| Microsoft Fabric Lakehouse | Fabric Lakehouse                          | Batch      | `append`, `merge`, `replace`     |

See [supported destinations](https://v2.dataos.info/references/resources/nilus/destinations) in References.

## Cross-cutting behavior

These apply across all types of data movement and are documented once in References:

* [Schema evolution](https://v2.dataos.info/references/resources/nilus/concepts/schema-evolution): how Nilus handles new columns and type changes in batch and CDC pipelines.
* [Data masking](https://v2.dataos.info/references/resources/nilus/concepts/understanding-data-masking): mask or redact columns during movement. Masking is a handling policy, not a transform; it stays compatible with Move.

If your source is not listed for the type you need, use Batch with a [custom source](https://v2.dataos.info/references/resources/nilus/batch/custom-sources), or land the data with the type the source does support.


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