Apache Kafka® is a distributed streaming platform. What exactly does that mean? A streaming platform has three key capabilities: Publish and subscribe to streams of records, similar to a message queue or enterprise messaging system. Store streams of records in a fault-tolerant durable way.
what is distributed streaming platform?
Apache Kafka® is a distributed streaming platform. A streaming platform has three key capabilities: Publish and subscribe to streams of records, similar to a message queue or enterprise messaging system. Store streams of records in a fault-tolerant durable way.
how is Kafka distributed?
Since Kafka is distributed in nature, a Kafka cluster typically consists of multiple brokers. To balance load, a topic is divided into multiple partitions and each broker stores one or more of those partitions. Multiple producers and consumers can publish and retrieve messages at the same time.
what is distributed stream processing?
There is a class of emerging applications in which, a large amounts of data generated in external environments are pushed to servers for real time processing. A new class of applications called distributed stream processing systems (DSPS) has emerged to facilitate such large scale real time data analytics.
What is distributed messaging system?
Distributed messaging is based on the concept of reliable message queuing. Messages are queued asynchronously between client applications and messaging systems. A distributed messaging system provides the benefits of reliability, scalability, and persistence.
What are streaming platforms?
What are streaming platforms? By definition, a streaming platform is an on-demand online entertainment source for TV shows, movies and other streaming media. For example, think of things like Hulu, Netflix, Amazon Prime Video, Vimeo, and Sundance Now. You may also read,
Is Kafka a database?
Let’s explore a contentious question: is Kafka a database? In some ways, yes: it writes everything to disk, and it replicates data across several machines to ensure durability. In other ways, no: it has no data model, no indexes, no way of querying data except by subscribing to the messages in a topic. Check the answer of
Is Zookeeper required for Kafka?
Yes, Zookeeper is must by design for Kafka. Because Zookeeper has the responsibility a kind of managing Kafka cluster. It has list of all Kafka brokers with it. It notifies Kafka, if any broker goes down, or partition goes down or new broker is up or partition is up.
Is Kafka a framework?
Apache Kafka: A Framework for Handling Real-Time Data Feeds. Apache Kafka is a distributed streaming platform. It is incredibly fast, which is why thousands of companies like Twitter, LinkedIn, Oracle, Mozilla and Netflix use it in production environments. It is horizontally scalable and fault tolerant. Read:
Can Kafka store data?
The answer is no, there’s nothing crazy about storing data in Kafka: it works well for this because it was designed to do it. Data in Kafka is persisted to disk, checksummed, and replicated for fault tolerance. Accumulating more stored data doesn’t make it slower.
What is ZooKeeper server?
ZooKeeper is an open source Apache project that provides a centralized service for providing configuration information, naming, synchronization and group services over large clusters in distributed systems. The goal is to make these systems easier to manage with improved, more reliable propagation of changes.
Is Kafka asynchronous?
By default, topics in Kafka are retention based: messages are retained for some configurable amount of time. It’s worth noting that this is an asynchronous process, so a compacted topic may contain some superseded messages, which are waiting to be compacted away. Compacted topics let us make a couple of optimisations.
What is the difference between Apache Kafka and confluent Kafka?
Apache Kafka includes Java client. If you use a different language, Confluent Platform may include a client you can use. Confluent adds HDFS, JDBC and Elastic Search connectors. REST Proxy – adds a REST API to Apache Kafka, so you can use it in any language or even from your browser.
What is stream processing framework?
Today stream processing is the primary framework used to implement all these use cases. Stream processing engines are runtime libraries which help developers write code to process streaming data, without dealing with lower level streaming mechanics.
What is real time stream processing?
It is used to query continuous data stream and detect conditions, quickly, within a small time period from the time of receiving the data. It is also called by many names: real-time analytics, streaming analytics, Complex Event Processing, real-time streaming analytics, and event processing.
