Share data with Kafka Streams — KT.team

Make it easier to work on integrations with Kafka

Make it easier to work on integrations with Apache Kafka

Apache Kafka message broker is a distributed streaming platform that can process millions of events daily. Kafka guarantees easy integration into the project infrastructure, the reliability and scalability of the system.

Kafka Connect

Kafka Connect is an Apache Kafka framework that provides scalability and flexibility to move data between Kafka and other repositories. This framework allows the broker to act as an ESB service bus.

The Kafka Connect framework allows the Kafka broker to act as a service tire — KT.team

Apache Kafka features

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Streaming data processing

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App activity tracking

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Logging and log monitoring

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Message storage

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The benefits of Kafka Apache

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Scalability

Apache Kafka allows you to process data of any size. You can start working with one broker to try out Kafka's features and then increase the number of brokers to fully exploit the system. It is also possible to increase the volume while the current number of brokers is running — this will not affect the system as a whole in any way.

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System reliability

One of Kafka's advantages is its reliability. For example, if one of the Kafka brokers “falls” for some reason, it will switch the entire data flow to other brokers and automatically distribute the load between them, and the system will continue to operate normally.

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Productivity

Due to its high throughput, Apache Kafka is able to process more than a million events per second. This makes Kafka the most popular message broker when working with big data.

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Included in the calculation
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  • Preparation of a map of systems and data flows (SOA scheme);
  • We will work out exchanges on key entities;
  • Creating connectors for exchanging data for each stream on 3 stands (test, preprod, prod);
  • Setting up to three dashboards per connector within a ready-made monitoring circuit;
  • Documentation on copying integration, reusing, and maintaining;
  • Demonstration of the implemented functionality.
Additional works
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  • Preparing the infrastructure for connectors to operate;
  • Setting up the monitoring and logging circuit;
  • Creating connectors (storage - receiver) for exchanging data on each high-load stream (>100 messages per minute) on 3 stands (test, preprod, prod);
  • More than 15 attributes for each stream;
  • The cost of computing resources (subscription only);
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