Companies that deal with Big Data constantly require scalable instruments. The ones that can work on multiple cases simultaneously. In this respect, a distributed data ecosystem that has a high level of data consistency turns out to be a reliable tool for accurate data processing and analysis.
In an attempt to create such an ecosystem, Apache developed an open source distributed messaging system. The system that would handle streams of data from various sources.
Apache Kafka is a crucial component of Apache Software Foundation. It aims to deliver scalable, fast-performing, and reliable solutions for Big Data. Let’s see why Kafka has become the focus of attention for numerous companies.
The major components of Kafka power rather simple architecture that has turbo speed when we compare it to other distributed data storages. Kafka has publishers, consumers, and topics. Brokers and clusters enhance Kafka fault tolerance. Let’s see what stands behind these basic terms and discuss their functionality.
Topic – the category that receives a stream of messages.
Consumer – the process that subscribes to the given topic.
Broker – a server that replicates and stores topic log partitions.
Producer – any process that publishes messages in a corresponding topic.
Cluster – a group of brokers that contains the published records.
This distributed messaging system deals a lot with topic partition and distributing it across brokers in real time. Each broker is responsible for some part of the topic. The topic contributes to the safety of the data flows within the clusters. This gives a responsibility for producers and consumers to use and publish the same topic in multiple channels simultaneously.
The above mentioned processes happen rather fast. In addition, Kafka processes data streams from the moment they flow into the system. So, it is possible to make the data available to transmit it into different real-time streaming data pipelines.
Kafka serves as a reliable system for myriads of log records. Its message storage turns into a turbocharged file system with solid replication capabilities. Message brokers have vast functionality but can’t influence data volumes; they are defined by the consumers.
In this respect, Kafka is extremely useful for large-scale processing write operations. All because it's enhanced fault tolerance and quick partitioning that is built into the system. These features make Kafka more preferable than traditional distributed databases.
Kafka’s architecture makes it easy to distribute log partitions across nodes with horizontal scalability. This is extremely crucial when the streams of data come in millions at a time.
Kafka can save the data if one component of the cluster fails. This happens because the log of each topic partition is copied across a number of machines within the cluster.
Numerous companies have to deal with Big Data volumes that require stable and productive data storage systems for their processing. Evidently, certain risks occur when such a system fails to deliver the desired results. This may affect the productivity and safety of the data that comes through the pipelines in a steady manner.
Kafka boasts a combination of features that make it an ideal environment for data streaming and storing. Its architecture allows users to decide at which speed to process the data. In addition, if the data storage system experiences a sudden failure, a user can still receive the message from the nodes.
Now, numerous companies are actively using Kafka to power up Big Data applications. And Geomotiv is of no exception.
We use Kafka in our product subsidiary Adoppler. It enables real-time stream processing of events upon each bid request.
With the help of Kafka we log in an RTB auction and source the events on a 24/7 basis. When datasets start going through data pipelines, Kafka generates and distributes the information through nodes and scales horizontally.
With each bid request, RTB nodes instantly receive the information about the price, DSPs and SSPs, ad parameters, etc. and the system shows its horizontal scalability. This allows us to process millions of data quickly and easily. We don't need to worry about the storage capacities and scalability of the storage system.
Kafka makes the data instantly readable. It allows us to gather information for immediate reports and monitor each event.
The system helps to enable numerous RTB scenarios in an error-free mode with maximal productivity. In such a way, Big Data turns into fast and safe data. This helps us to manage real-time auctions with no losses.
These days, Kafka is not just a trendy technology. Kafka stream processing is super fast. It is ready to transmit the data for reporting and analysis. So, it's no surprise that Kafka is used by numerous companies within Fortune 500. LinkedIn, Uber, and many others have it as a part of their tech stack.
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