An overview of Camel
Apache Camel is an open source integration framework that targets the integration between different systems. Camel is a routing engine, more precisely a routing- engine builder. however It allows you to define your own routing rules, decide from which sources to accept messages, and determine how to process and send those messages to other destinations.
However its Routes, Camel uses a set of Factors which are an implementation of Enterprise Integration Patterns (EIPs). EIPs are used to address a fairly expansive list of integration script using different integration strategies similar as File Transfer, Shared Database and asynchronous Messaging.
an overview of a Camel Route:
Apache Camel Use Cases
Application Integration: Camel is used for every planning where you need to transfer data between different protocols and applications. it’s done cause of its large set of Components. so you can use Apache Camel when you want to move data back and forth between any one of the apps using most known protocols. so All components in Camel work more or less in a similar way. So, once you know how to use one component, you will find it easier to use others.You can also write your own custom component. Besides standard File, FTP, JMS components there are components also for complex apps such as Facebook, Twitter, Salesforce and Workday.
There are Many frequent use cases for application integration – such as support for transactions, or transformation – would usually be complicated to plan and write in code. But Apache Camel can simplify this process by providing components that can be added in your Route. it provides patterns and functionality like:
- Routing data based on its content, using Camel’s content-based routing
- Transforming Data
- Handling errors, transactions and rollbacks
- Caching frequently-accessed data
- Security concerns (encryption and authentication)
High-level architecture for many integrations: Once you’ve learned the introductory patterns, you’ll find that it becomes easy to develop common integrations in Camel. you’ll have the capability to create many integrations fairly quickly by reusing patterns like templates. so it can be really good option in larger companies where it helps to pick one approach which is shared and understood by the development team.
Working with data, and in particular with Java objects: Apache Camel is a Java framework, it’s especially working with Java objects. So if you’re working with a file format that can be de-serialised into a Java object (many file formats can be converted into Java objects, like XML, JSON etc…) then it will be handled easily by Apache Camel.
An overview of Apache Kafka
Apache Kafka is pure distributed messaging with a publish-subscribe schema that provides scalability. it can handle enormous load of data.
Its main characteristics are as follows:
- Distributed: Cluster-centric design that supports the distribution of the messages over the cluster members, maintaining the semantics. So you can grow the cluster horizontally without downtime.
- Multi-client Easy integration with different clients from different platforms: Java,.NET, PHP, Ruby, Python, etc.
- Persistent: You can’t afford any data lost. Kafka is designed with efficient data structures that provide constant time performance no matter the data size.
- Real time: The messages produced are immediately seen by consumer threads;
- Very high throughput: As we mentioned, all the technologies in the stack are designed to work in commodity hardware. so Kafka can handle hundreds of read and write operations per second from a large number of clients.
A Kafka cluster has five main components:
Topic– It is a category name to which records are stored and published. all Kafka records organised into topics. Producer applications write data to topics and consumer applications read from topics. The cluster has a partitioned log for each topic. every message in the partition has a unique sequential id called an offset.
Broker: A Kafka cluster has one or more physical servers in which each one may have one or more server processes running. Each server process is called a broker . The topics live in the broker processes.
Producer: Publishes data to topics by choosing the appropriate partition in the topic. For load balancing, the messages allocation to the topic partition can be done in a round-robin mode or by defining a custom function.
Consumer: Applications or processes subscribed to topics and process the feed of published messages.
Zoo-Keeper: Zoo-Keeper is the coordinator between the broker and the consumers. Zoo-Keeper coordinates the distributed processes through a shared hierarchical name space of data registers; these registers are called z-nodes
Apache Kafka Use cases
- Commit logs: Many times systems do not have logs, simply because it’s not possible to handle a large data volume. Kafka can manage large volumes of logs and also
- Log aggregation: log analysis is a key aspect of every support team. Kafka can physically collect the logs and remove cumbersome details such as file location
- Messaging: Systems are often heterogeneous: Kafka is a perfect solution to integrate different messaging systems thanks to its plugins.
- Stream processing: With Kafka, the information can be collected and further enriched. This is also known as stream processing.
- Record user activity: recording user activity can be a complex task where the data volume is huge. Kafka is a perfect use case for real-time process and monitoring of large data sets.
In this tutorial We have covered in detail the key differences between Apache Camel and Apache Kafka. so camel is a integration framework and Kafka is a pub-sub messaging system.