The Kafka Ecosystem - Kafka Core, Kafka Streams, Kafka Connect, Kafka REST Proxy, and the Schema Registry (2023)

FAQs

What is Kafka Connect and Kafka Streams? ›

Kafka Stream is the Streams API to transform, aggregate, and process records from a stream and produces derivative streams. Kafka Connect is the connector API to create reusable producers and consumers (e.g., stream of changes from DynamoDB). The Kafka REST Proxy is used to producers and consumer over REST (HTTP).

Kafka EcoSystem. Kafka is horizontally scalable and can run on a cluster of brokers deployed across multiple nodes and multiple regions, with each broker capable of handle terabytes of messages without performance impact.

You can use HTTP Basic Authentication or mutual TLS (mTLS) authentication for communication between a client and REST Proxy. You can use SASL or mTLS for communication between REST Proxy and the brokers.

Kafka Streams is an API for writing client applications that transform data in Apache Kafka. You usually do this by publishing the transformed data onto a new topic. The data processing itself happens within your client application, not on a Kafka broker. Kafka Connect is an API for moving data into and out of Kafka.

A schema defines the structure of the data format. The Kafka topic name can be independent of the schema name. Schema Registry defines a scope in which schemas can evolve, and that scope is the subject .

Difference between Kafka Streams and Kafka Consumer

Kafka Streams is an easy data processing and transformation library within Kafka used as a messaging service. Whereas, Kafka Consumer API allows applications to process messages from topics.

Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.

The Kafka Connect JDBC Sink connector exports data from Kafka topics to any relational database with a JDBC driver. The Kafka Connect JMS Source connector is used to move messages from any JMS-compliant broker into Kafka. The Kafka Connect Elasticsearch Service Sink connector moves data from Kafka to Elasticsearch.

Kafka is often used for operational monitoring data. This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.

Kafka uses a binary protocol over TCP. The protocol defines all APIs as request response message pairs. All messages are size delimited and are made up of the following primitive types.

Which SSL security mechanism provides the security for the inflight data in between Kafka client and Kafka broker? ›

Encryption of data in-flight using SSL / TLS: This allows your data to be encrypted between your producers and Kafka and your consumers and Kafka. This is a very common pattern everyone has used when going on the web. That's the “S” of HTTPS (that beautiful green lock you see everywhere on the web).

Producers send messages to the Kafka leader with the other Kafka nodes acting as clients to this leader for replication, as any external Kafka client.

Since Kafka Connect is intended to be run as a service, it also supports a REST API for managing connectors. By default this service runs on port 8083 . When executed in distributed mode, the REST API will be the primary interface to the cluster.

Stream-stream joins combine two event streams into a new stream. The streams are joined based on a common key, so keys are necessary. You define a time window, and records on either side of the join need to arrive within the defined window.

To use Kafka Connect with Schema Registry, you must specify the key. converter or value. converter properties in the connector or in the Connect worker configuration. The converters need an additional configuration for the Schema Registry URL, which is specified by providing the required .

In some rare cases, when a producer sends inappropriate or wrong data with an unsupported data format into the Kafka server, the downstream consumers will break or collapse when trying to read data from that specific topic. To eliminate such complexities, Confluent introduced the Kafka schema registry.

2.2.

Kafka Streams greatly simplifies the stream processing from topics. Built on top of Kafka client libraries, it provides data parallelism, distributed coordination, fault tolerance, and scalability.

Kafka Streams partitions data for processing it. In both cases, this partitioning is what enables data locality, elasticity, scalability, high performance, and fault tolerance. Kafka Streams uses the concepts of stream partitions and stream tasks as logical units of its parallelism model.

What are the different types of Kafka? ›

Kafka supports two types of topics: Regular and compacted. Regular topics can be configured with a retention time or a space bound. If there are records that are older than the specified retention time or if the space bound is exceeded for a partition, Kafka is allowed to delete old data to free storage space.

Apache Kafka can handle scalability in all the four dimensions, i.e. event producers, event processors, event consumers and event connectors. In other words, Kafka scales easily without downtime.

Apache Kafka is a distributed publish-subscribe messaging system that receives data from disparate source systems and makes the data available to target systems in real time. Kafka is written in Scala and Java and is often associated with real-time event stream processing for big data.

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

If we have 3 Kafka brokers spread across 3 datacenters, then a partition with 3 replicas will never have multiple replicas in the same datacenter. With this configuration, datacenter outages are not significantly different from broker outages.

Kafka brokers splits each partition into segments. Each segment is stored in a single data file on the disk attached to the broker.

It enables applications to publish or subscribe to data or event streams. It stores data records accurately and is highly fault-tolerant. It is capable of real-time, high-volume data processing. It is able to take in and process trillions of data records per day, without any performance issues.

Kafka is best used for streaming from A to B without resorting to complex routing, but with maximum throughput. It's also ideal for event sourcing, stream processing, and carrying out modeling changes to a system as a sequence of events. Kafka is also suitable for processing data in multi-stage pipelines.

When to use Kafka vs REST API? ›

The purpose of APIs is to essentially provide a way to communicate between different services, development sides, microservices, etc. The REST API is one of the most popular API architectures out there. But when you need to build an event streaming platform, you use the Kafka API.

Encryption in HTTPS

HTTPS is based on the TLS encryption protocol, which secures communications between two parties. TLS uses asymmetric public key infrastructure for encryption. This means it uses two different keys: The private key.

HTTPS uses an encryption protocol to encrypt communications. The protocol is called Transport Layer Security (TLS), although formerly it was known as Secure Sockets Layer (SSL).

Apache Kafka scales well and may track events but lacks some message simplification and granular security features. It is perhaps an excellent choice for teams that emphasize performance and efficiency. IBM MQ is a powerful conventional message queue system, but Apache Kafka is faster.

Even a lightly used Kafka cluster deployed for production purposes requires three to six brokers and three to five ZooKeeper nodes. The components should be spread across multiple availability zones for redundancy.

Kafka Connect includes two types of connectors: Source connector: Source connectors ingest entire databases and stream table updates to Kafka topics.

How do I start Kafka Connect service? ›

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 supports running and scaling out connectors (components that support reading and/or writing between external systems).

The REST Proxy is an HTTP-based proxy for your Kafka cluster. The API supports many interactions with your cluster, including producing and consuming messages and accessing cluster metadata such as the set of topics and mapping of partitions to brokers.

The Kafka REST Proxy allows applications to connect and communicate with a Kafka cluster over HTTP. The service exposes a set of REST endpoints to which applications can make REST API calls to connect, write and read Kafka messages.

Every topic in Kafka is split into one or more partitions. Kafka partitions data for storing, transporting, and replicating it. Kafka Streams partitions data for processing it. In both cases, this partitioning enables elasticity, scalability, high performance, and fault tolerance.

Kafka Stream is the Streams API to transform, aggregate, and process records from a stream and produces derivative streams. Kafka Connect is the connector API to create reusable producers and consumers (e.g., stream of changes from DynamoDB). The Kafka REST Proxy is used to producers and consumer over REST (HTTP).

Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.

Apache Kafka is a popular event streaming platform used to collect, process, and store streaming event data or data that has no discrete beginning or end. Kafka makes possible a new generation of distributed applications capable of scaling to handle billions of streamed events per minute.

We can use Kafka as a Message Queue or a Messaging System but as a distributed streaming platform Kafka has several other usages for stream processing or storing data. We can use Apache Kafka as: Messaging System: a highly scalable, fault-tolerant and distributed Publish/Subscribe messaging system.

Why is Kafka needed? ›

Why Kafka? Kafka is often used in real-time streaming data architectures to provide real-time analytics. Since Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system, Kafka is used in use cases where JMS, RabbitMQ, and AMQP may not even be considered due to volume and responsiveness.

Java provides three predefined stream objects: in, out, and err, defined in the System class of the java.

Kafka is distributed data infrastructure, which implies that there is some kind of node that can be duplicated across a network such that the collection of all of those nodes functions together as a single Kafka cluster. That node is called a broker.

It enables applications to publish or subscribe to data or event streams. It stores data records accurately and is highly fault-tolerant. It is capable of real-time, high-volume data processing. It is able to take in and process trillions of data records per day, without any performance issues.

Kafka Connect is a free, open-source component of Apache Kafka® that works as a centralized data hub for simple data integration between databases, key-value stores, search indexes, and file systems. The information in this page is specific to Kafka Connect for Confluent Platform.

The Kafka REST Proxy is a RESTful web API that allows your application to send and receive messages using HTTP rather than TCP. It can be used to produce data to and consume data from Kafka or for executing queries on cluster configuration.

The purpose of APIs is to essentially provide a way to communicate between different services, development sides, microservices, etc. The REST API is one of the most popular API architectures out there. But when you need to build an event streaming platform, you use the Kafka API.

As a technology that enables stream processing on a global scale, Kafka has emerged as the de facto standard for streaming architecture.