Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?

Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?

A. Python Connector

B. Spark Connector

C. Node.js connector

D. Kafka Connector

View Answer

Answer: D

Explanation:

Apache Kafka software uses a publish and subscribe model to write and read streams of records, similar to a message queue or enterprise messaging system. Kafka allows processes to read and write messages asynchronously. A subscriber does not need to be connected directly to a publisher; a publisher can queue a message in Kafka for the subscriber to receive later.

An application publishes messages to a topic, and an application subscribes to a topic to receive those messages. Kafka can process, as well as transmit, messages; however, that is outside the scope of this document. Topics can be divided into partitions to increase scalability.

Kafka Connect is a framework for connecting Kafka with external systems, including databases. A Kafka Connect cluster is a separate cluster from the Kafka cluster. The Kafka Connect cluster sup-ports running and scaling out connectors (components that support reading and/or writing between external systems).

The Kafka connector is designed to run in a Kafka Connect cluster to read data from Kafka topics and write the data into Snowflake tables.

Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns:

RECORD_CONTENT. This contains the Kafka message.

RECORD_METADATA. This contains metadata about the message, for example, the topic from which the message was read.