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Update markdown for docusaurus v3.
Signed-off-by: Gerd Zellweger <mail@gerdzellweger.com>
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‎docs/concepts.md‎

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Feldera is the pioneering implementation of a new theory that unifies
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databases, streaming computation, and incremental view maintenance,
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written by the inventors of that theory. See our
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[publications](/docs/papers) for all the details.
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[publications](/papers) for all the details.
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Feldera code is available on [Github][Feldera] using an MIT
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open-source license. It consists of a Rust runtime and a SQL compiler.

‎docs/connectors/index.mdx‎

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A connector specification consists of three parts:
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* [Generic attributes](#generic-attributes) common to all connectors, such as backpressure thresholds.
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* Transport specification (`transport`) for either [input](/docs/connectors/sources/) or [output](/docs/connectors/sinks/)
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* Transport specification (`transport`) for either [input](/connectors/sources/) or [output](/connectors/sinks/)
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defines the data transport to be used by the connector.
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Example transports include [Kafka](/docs/connectors/sources/kafka),
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[URL](/docs/connectors/sources/http-get), [Delta Lake](/docs/connectors/sources/delta), etc.
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* [Data format specification](/docs/formats) (`format`), which defines the data format for the connector.
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Example data formats include [CSV](/docs/formats/csv), [JSON](/docs/formats/json), [Parquet](/docs/formats/parquet), or
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Example transports include [Kafka](/connectors/sources/kafka),
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[URL](/connectors/sources/http-get), [Delta Lake](/connectors/sources/delta), etc.
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* [Data format specification](/formats) (`format`), which defines the data format for the connector.
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Example data formats include [CSV](/formats/csv), [JSON](/formats/json), [Parquet](/formats/parquet), or
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Avro).
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:::note
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Some transports, e.g., [Delta Lake](/docs/connectors/sinks/delta) and
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[datagen](/docs/connectors/sources/datagen), use fixed predefined data formats and do not require the
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Some transports, e.g., [Delta Lake](/connectors/sinks/delta) and
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[datagen](/connectors/sources/datagen), use fixed predefined data formats and do not require the
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format section in the connector specification.
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:::
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This architecture allows the user to combine different transports and data formats.
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These basics apply to all connectors **except** the HTTP [input](/docs/connectors/sources/http) and
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[output](/docs/connectors/sinks/http) connectors which are not managed by the user, as they directly feed/fetch data
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These basics apply to all connectors **except** the HTTP [input](/connectors/sources/http) and
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[output](/connectors/sinks/http) connectors which are not managed by the user, as they directly feed/fetch data
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into/from a pipeline via dedicated pipeline endpoints and therefore do not need to be configured in the `WITH` clauses
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of tables and views.
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By default a Feldera pipeline sends a batch of changes to the output transport
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for each batch of input updates it processes. This can result in a stream of
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small updates, which is normal and even preferable for output transports like
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[Kafka](/docs/connectors/sinks/kafka); however it can cause performance problems
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for other connectors, such as the [Delta Lake connector](/docs/connectors/sinks/delta)
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[Kafka](/connectors/sinks/kafka); however it can cause performance problems
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for other connectors, such as the [Delta Lake connector](/connectors/sinks/delta)
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by creating a large number of small files.
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The output buffer mechanism is designed to solve this problem by decoupling the
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:::
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See [Delta Lake output connector documentation](/docs/connectors/sinks/delta#curl)
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See [Delta Lake output connector documentation](/connectors/sinks/delta)
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for an example of configuring the output buffer.
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## Additional resources
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For more information, see:
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* [Tutorial on using input and output connectors](/docs/tutorials/basics/part3)
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* [Tutorial on using HTTP-based Input and Output](/docs/tutorials/basics/part2)
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* [Supported source transports](/docs/connectors/sources)
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* [Supported sinks transports](/docs/connectors/sinks)
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* [Tutorial on using input and output connectors](/tutorials/basics/part3)
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* [Tutorial on using HTTP-based Input and Output](/tutorials/basics/part2)
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* [Supported source transports](/connectors/sources)
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* [Supported sinks transports](/connectors/sinks)

‎docs/connectors/sinks/confluent-jdbc.md‎

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:::note
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This page describes configuration options specific to integration with the Confluent JDBC sink connector.
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See [top-level connector documentation](/docs/connectors/) for general information
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See [top-level connector documentation](/connectors/) for general information
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about configuring input and output connectors.
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:::
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guaranteed to have unique values. Failure to choose a unique key may lead to data loss.
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:::
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* For more details on Avro support in Feldera, please refer to the [Avro Format Documentation](/docs/formats/avro).
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* For more information on configuring Kafka transport, visit the [Kafka Sink Connector Documentation](/docs/connectors/sinks/kafka).
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* For more details on Avro support in Feldera, please refer to the [Avro Format Documentation](/formats/avro).
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* For more information on configuring Kafka transport, visit the [Kafka Sink Connector Documentation](/connectors/sinks/kafka).

‎docs/connectors/sinks/delta.md‎

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for up to a user-defined period of time or until accumulating a user-defined number
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of updates and writing them to the Delta Table as a small number of large files.
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See [output buffer](/docs/connectors#configuring-the-output-buffer) for details on configuring the output buffer mechanism.
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See [output buffer](/connectors#configuring-the-output-buffer) for details on configuring the output buffer mechanism.
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## Limitations
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‎docs/connectors/sinks/http.md‎

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For more information, see:
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* [Tutorial section](/docs/tutorials/basics/part2) on HTTP-based input and output.
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* [Tutorial section](/tutorials/basics/part2) on HTTP-based input and output.
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* [REST API documentation](https://www.feldera.com/api/subscribe-to-a-stream-of-updates-from-a-sql-view-or-table)
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* [REST API documentation](/api/subscribe-to-a-stream-of-updates-from-a-sql-view-or-table)
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for the `/egress` endpoint.

‎docs/connectors/sinks/kafka.md‎

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For more information, see:
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* [Kafka input connector](/docs/connectors/sources/kafka#how-to-write-connector-config)
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* [Kafka input connector](/connectors/sources/kafka#how-to-write-connector-config)
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for examples on how to write the configuration to connect to Kafka brokers (e.g., how to
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specify authentication and encryption).
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* [Tutorial section](/docs/tutorials/basics/part3#step-2-create-kafkaredpanda-connectors) which involves
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* [Tutorial section](/tutorials/basics/part3#step-2-configure-kafkaredpanda-connectors) which involves
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creating a Kafka output connector.
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* Data formats such as [JSON](https://www.feldera.com/docs/formats/json) and
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[CSV](https://www.feldera.com/docs/formats/csv)
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* Data formats such as [JSON](/formats/json) and
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[CSV](/formats/csv)
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* Overview of Kafka configuration options:
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[librdkafka options](https://github.com/confluentinc/librdkafka/blob/master/CONFIGURATION.md)

‎docs/connectors/sources/datagen.md‎

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prototyping and benchmarking purposes.
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For a tutorial on how to use the Datagen connector, see the
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[Random Data Generation](/docs/tutorials/basics/part4) tutorial.
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[Random Data Generation](/tutorials/basics/part4) tutorial.
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## Datagen input connector configuration
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‎docs/connectors/sources/debezium.md‎

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:::note
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See [top-level connector documentation](/connectors/) for general information
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Debezium is a widely-used **Change Data Capture** (CDC) technology that streams real-time changes from databases such
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as PostgreSQL, MySQL, and Oracle to Kafka topics. Feldera can consume these change streams as inputs. We support
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Debezium streams encoded in both [JSON](/docs/formats/json) and [Avro](/docs/formats/avro) formats. Synchronizing
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Debezium streams encoded in both [JSON](/formats/json) and [Avro](/formats/avro) formats. Synchronizing
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either [`VARIANT`](/docs/sql/json) or `VARCHAR` type. The former allows efficient manipulation
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either [`VARIANT`](/sql/json) or `VARCHAR` type. The former allows efficient manipulation
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of JSON values, similar to the `JSONB` type. The latter is preferable when working with JSON
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## Additional resources
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* For more details on JSON support in Feldera, please refer to the [JSON Format Documentation](/docs/formats/json).
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* For more details on Avro support in Feldera, please refer to the [Avro Format Documentation](/docs/formats/avro).
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* For more information on configuring Kafka transport, visit the [Kafka Source Connector Documentation](/docs/connectors/sources/kafka).
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* For more details on JSON support in Feldera, please refer to the [JSON Format Documentation](/formats/json).
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* For more details on Avro support in Feldera, please refer to the [Avro Format Documentation](/formats/avro).
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* For more information on configuring Kafka transport, visit the [Kafka Source Connector Documentation](/connectors/sources/kafka).
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‎docs/connectors/sources/delta.md‎

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specified, and `mode` is one of `snapshot` or `snapshot_and_follow`,
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the snapshot of the table is ingested in the timestamp order. This setting is required
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[`LATENESS`](https://www.feldera.com/docs/sql/streaming#lateness-expressions) attribute
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[`LATENESS`](/sql/streaming#lateness-expressions) attribute
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in Feldera SQL. It impacts the performance of the connector, since data must be sorted
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before pushing it to the pipeline; therefore it is not recommended to use this
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‎docs/connectors/sources/http-get.md‎

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and is in [newline-delimited JSON (NDJSON) format](/formats/json#encoding-multiple-changes)
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* [Tutorial section](/tutorials/basics/part3#step-1-configure-https-get-connectors) which involves
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* Data formats such as [JSON](/formats/json),
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[CSV](/formats/csv), and [Parquet](/formats/parquet)

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