NiFi & Kafka plays a crucial role in several new-age data streaming architectures. Apache Nifi was created for the automation of data flowage among the software systems. It supports scalable, robust & streamlined data routing graphs along with system mediation logic. On the other hand,
Apache Kafka is utilized to build ‘live’ data flow pipelines & stream apps. It is fault-tolerant, horizontally scalable & fast. The Apache Nifi Kafka connect makes it easy for apps to dive into a relentless data stream.
Why is Kafka & Nifi integration preferred?
By integrating Kafka with Nifi, it is possible to avoid writing lengthy codes. It becomes easy to administer & comprehend the entire NiFi data flow pipelines on a single screen. When it comes to Kafka, Apache Nifi can function both as a consumer as well as a producer. Both the ways are suitably utilized based on the scenarios and requirements.
APACHE NIFI AS A PRODUCER
To leverage the highest efficiency of Apache Nifi, you must utilize it as a Kafka producer. This will generate data from any source as an input, which is then forwarded to the Kafka Broker. Basically, Nifi is the replacement for the producer, which delivers data packets to fitting Kafka topics. The main advantage of this approach is that producer codes are not needed to enable data to Kafka. It is achieved through dragging & dropping a set of Nifi processors(PublishKafta) as the data flow pipelines are visually monitored and managed.
NIFI AS CONSUMER
In this scenario, Apache NiFi replaces the Kafka consumer. It handles the entire logic. For example, it can retrieve data from Kafka and move it further. The consumer code is avoided here simply by dragging & dropping the Nifi processor(ConsumeKafka). It is possible to deliver data to HDFS from Kafka with no coding by utilizing the ConsumeKafka processor.
In some cases, a bi-directional NiFi data flow pipeline can be put to use for complicated scenarios.
Some factors may have an effect on NiFi’s publication and consumption efficiency. Let’s have a look!
Both the ConsumeKafka & PublishKafka comes with a property- Message Demarcator – Separator- Delimiter.
In the publishing process, the demarcator means that incoming files’ flow would have several messages within the content with their respective demarcations. In this scenario, the flow file contents will be streamed by PublishKafka to split it into demarcation-based messages, and each message will be published separately. If the property is blank, the contents of the flux(or flow) file will be sent as a single message by PublishKafka.
In the consuming process, the demarcator symbolizes that ConsumeKafka should create a single flow file. The content must contain all messages that are received from Kafka in a single poll. The demarcator is utilized to separate the messages. If this property is blank, a flow file is produced by ConsumeKafka for every message that is received.
As Kafka is more appropriate for smaller messages & NiFi is suitable for the larger ones, the batching capabilities drive efficiency by combining the best of both worlds! It is faster to publish one flow file that contains a million messages & stream the same to Kafka than sending a million files with separate messages.
It is the same during the consuming process. Efficiency is improved when several consumed messages are written into a single flow file rather than writing several flow files containing a single message.
As the public clouds generate more data, the demand for hybrid NiFi & Kafka deployments is rising manifold. You might have an on-premises NiFi cluster that runs a data flow pipeline to integrate with the cloud sources & cloud targets. The Kafka & Nifi Integration facilitates the merging of data streams that originate in the cloud as well as various environments. NiFi’s Site-to-Site protocol can be leveraged to transfer data between the public cloud & on-premises at a higher efficiency.
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