In the age of digital transformation, companies are relying on real-time information for analytics to guide their business strategy. In a study by CIO Insight, 91% of respondents said streaming data analysis can have a positive impact on their company’s bottom line, and 56% said it is “critical” or “very important” to add real-time context to streaming analytics apps.
Due to multiple systems in a company, consolidating all of that information can be a challenge. Apache Kafka is a robust platform that can ingest data from many systems in real-time to provide leaders with access to information as it occurs.
Apache Kafka is a messaging-based event streaming platform. It is designed to ingest and process data in real-time. Some of the largest companies are using this robust platform to guide their business strategy.
Kafka is a tool for resolving and empowering companies in the sense of making possible the modernization of their data strategies with event streaming architecture.
Kafka provides three core functions:
The platform is flexible enough for a wide variety of use cases. This flexibility is important where real-time data updates are critical to business functions.
A few of the common use cases are:
Real Time: As an event-based platform, the system is constantly ingesting data. This ensures the message queue stays filled with the most up-to-date information.
Low Latency: Kafka decouples messaging between the publisher and subscriber. This means the subscriber can process the information without waiting on the producer. The result is faster processing times.
High Concurrency: Due to the system’s low latency, it can handle large volumes of data with high performance.
Fault-Tolerant: The system uses replication as a method of fault tolerance. With replication, the same information is spread across multiple servers. This helps ensure the process continues even if a broker fails.
Message Guarantees: Kafka guarantees message delivery via a variety of methods which are exactly-once delivery, at-least delivery, and at-most-once delivery.
The Kafka architecture is the set of components that is responsible for orchestrating messaging in the system. Each has a set task to perform.
The core components are events and messages. Events trigger the process for sending and receiving messages in the workflow.
Let's discuss each item further for a more in-depth explanation.
Events are the cornerstone of real-time streaming. Events represent a trigger that occurs on an event source. An event source refers to where the information comes from, such as client applications, databases, or cloud services.
Messages represent a unit of data in Kafka. It is simply an array of bytes and has a specific format. These messages are sent in batches, to optimize performance across the network.
A publisher sends data through the pipeline while the subscriber receives data. Messaging systems often have a central point that coordinates the process.
A topic is the equivalent of a queue that receives messages. A simple analogy is that each topic represents a row in a table in a database or folder in a file system.
The topic is what decouples publishers from subscribers. Each can work independently of one another to process information.
A publisher pushes messages to topics and subscribers receive messages from topics. Each topic can have multiple producers and subscribers.
A topic is divided into smaller units called a partition. The partition is a log file where records are appended to the end of the file as they are entered.
A Kafka broker is a single server. The broker receives messages from producers and stores them on a disk. It also coordinates with subscribers to fetch information and return it to the subscriber.
Brokers are designed to work in groups known as clusters. One broker in the cluster will serve as the lead or cluster controller.
The leader handles administrative tasks such as assigning brokers to partitions and monitoring for broker failures.
Kafka provides redundancy by replicating partitions across multiple brokers. If one fails, another can serve subscribers with replicas of the failed broker’s partitions.
Limiting partitions to one broker ties performance to the capabilities of that single broker. The ability to spread partitions is what provides horizontal scaling.
With horizontal scaling, Kafka can provide greater performance than that of a single broker.
In Kafka, streams are an ordered sequence of messages that maps to a topic. The ordering helps ensure the data is complete, consistent, and arrives in the same format the records were created.
Data in the streams are immutable. Streams can be replayed at any time for error detection or analysis.
As the system grows in size and complexity, coordinating and keeping track of each item in the system can be challenging.
ZooKeeper solves this problem. It is a service that is responsible for maintaining configuration information and for managing distributed service synchronization.
It works by each node updating its status in ZooKeeper. The tool then handles the synchronization of tasks across the cluster.
Kafka is used by thousands of companies including over 60% of the Fortune 100. Given its popularity and widespread usage, the technology is likely here to stay.
This is good news for developers. Why? Because Kafka has proven to be a robust messaging platform that is capable of handling even the most complex use cases.
When it comes to real-time information processing, Kafka offers a robust platform that has low latency, high throughput, and can support multiple use cases.
If you are looking for a partner to help you implement Kafka for information processing, Adservio can help.
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