Sources

Overview

Sources describe external systems you want Materialize to read data from, and provide details about how to decode and interpret that data. A simplistic way to think of this is that sources represent streams and their schemas; this isn’t entirely accurate, but provides an illustrative mental model.

In terms of SQL, sources are similar to a combination of tables and clients.

• Like tables, sources are structured components that users can read from.
• Like clients, sources are responsible for reading data. External sources provide all of the underlying data to process.

By looking at what comprises a source, we can develop a sense for how this combination works.

Source components

Sources consist of the following components:

Component	Use	Example
Connector	Provides actual bytes of data to Materialize	Kafka
Format	Structures of the external source’s bytes, i.e. its schema	Avro
Envelope	Expresses how Materialize should handle the incoming data + any additional formatting information	Upsert

Connectors

Materialize bundles native connectors for the following external systems:

• Kafka
• Redpanda
• PostgreSQL
• MySQL
• SQL Server
• Webhooks

For details on the syntax, supported formats and features of each connector, check out the dedicated CREATE SOURCE documentation pages.

Formats

Materialize can decode incoming bytes of data from several formats:

• Avro
• Protobuf
• Regex
• CSV
• Plain text
• Raw bytes
• JSON

Envelopes

What Materialize actually does with the data it receives depends on the “envelope” your data provides:

Envelope	Action
Append-only	Inserts all received data; does not support updates or deletes.
Debezium	Treats data as wrapped in a “diff envelope” that indicates whether the record is an insertion, deletion, or update. The Debezium envelope is only supported by sources published to Kafka by [Debezium]. For more information, see CREATE SOURCE: Kafka—Using Debezium.
Upsert	Treats data as having a key and a value. New records with non-null value that have the same key as a preexisting record in the dataflow will replace the preexisting record. New records with null value that have the same key as preexisting record will cause the preexisting record to be deleted. For more information, see CREATE SOURCE: —Handling upserts.

Related pages

• CREATE SOURCE