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Apache Kafka: a reliable instrument for big data specialists

Companies that deal with Big Data constantly require scalable instruments 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.

It is also important for such an ecosystem to deliver live analytics and offer the data for instant readability. In an attempt to create such an ecosystem, Apache started to develop an open source distributed messaging system that would handle streams of data from various sources.

Apache Kafka is a crucial component of Apache Software Foundation, which 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 with respect to its architecture and practical use cases.

Kafka Architecture: basic components

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

As Kafka is a distributed messaging system, it deals a lot with topic partition and distributing it across brokers in real time. Each broker is responsible for some part of the topic, which 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. One should also mention the ability of Kafka to process data streams from the moment they flow into the system. This means that it is possible to make the data available to transmit it into different real-time streaming data pipelines.

What else is Kafka capable of?

Kafka as a message storage

Serving as a reliable system for myriads of log records, Kafka 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, as it has enhanced fault tolerance and quick partitioning that is built into the system. These features make Kafka more preferable than traditional distributed databases.

Kafka for log aggregation

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 is an expert in disaster recovery

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.

Kafka: why it matters

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 Kafka data streaming and storing. Its architecture allows users to decide at which speed to process the data. What is more, if the data storage system experiences a sudden failure or some other issue, a user can still receive the message from the nodes.

Now, numerous companies are actively using Kafka to their advantage to power up big data applications. And Geomotiv is of no exception.

Kafka supports RTB auctions

We use Kafka in our product subsidiary Adoppler to enable 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 that occur within the system 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 and never worry about the storage capacities and scalability of the storage system.

Kafka makes the data instantly readable, allowing us to gather information for immediate reports and monitor each event as it happens.

The system helps to enable numerous RTB scenarios in an error-free mode with maximal productivity and speed. In such a way, big data turns into fast and safe data, allowing us to manage real-time auctions with no losses.

Kafka as an innovative solution to big data problems

These days, Kafka is not just a trendy technology for companies depending on big data to compete. Kafka stream processing is super fast and is ready to transmit the data for reporting and analysis. So, it is no surprise that Kafka is part of tech stack of numerous companies within Fortune 500: it is actively used by LinkedIn, Uber, and many others.



Sergey Lobko-Lobanovsky

Chief Executive Officer

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