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Kafka

A distributed streaming platform that enables publishing, subscribing, storing, and processing data streams in a scalable and fault-tolerant manner.

Kafka-based solutions support fast and reliable communication between systems, making it a core component of modern data ecosystems, microservices, IoT platforms, and real-time analytics solutions.

Apache Kafka solutions include:

Real-time Data Streaming

  • arrowCapture and process continuous data flows from various sources such as apps, databases, or sensors.
  • arrowIdeal for building event-driven architectures.

Message Broker and Event Bus

  • arrowActs as a high-throughput, low-latency message broker between distributed systems.
  • arrowDecouples services and improves system scalability.

Data Integration Across Systems

  • arrowSynchronizes data between databases, cloud services, and applications in real time.
  • arrowSupports CDC (Change Data Capture) and stream-based ETL pipelines.

Log Aggregation and Monitoring

  • arrowCollects logs and metrics from various sources for central analysis and observability.
  • arrowWorks seamlessly with tools like Elasticsearch, Prometheus, and Grafana.

Stream Processing and Analytics

  • arrowEnables transformation and enrichment of data in motion with tools like Kafka Streams or ksqlDB.
  • arrowSupports real-time insights and automation.

Scalability and Fault Tolerance

  • arrowDesigned to handle massive volumes of data with horizontal scaling and built-in replication.
  • arrowEnsures data durability and system reliability.

Kafka development services: Real-Time Data Streaming, Scalable Architecture, and Enterprise-Grade Integration

High throughput and low latency

High throughput and low latency

Capable of processing millions of events per second in real time.

Fault tolerance

Fault tolerance

Built-in replication and recovery mechanisms ensure data reliability.

Horizontal scalability

Horizontal scalability

Easy to scale by adding new brokers as data volume grows.

Data durability

Data durability

Supports long-term data storage and reprocessing.

Multiple consumers support

Multiple consumers support

Enables parallel processing by independent services or teams.

Flexible integration

Flexible integration

Works seamlessly with various databases, tools, and processing frameworks (e.g., Spark, Flink, Camel).

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Kafka consulting in the project

Kafka implementation example
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Real-time data streaming

Kafka enables immediate processing and delivery of financial data, ensuring that information about contractors' reliability is always up-to-date.

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Scalability and fault tolerance

Kafka's distributed architecture allows QuickPay to handle increasing volumes of data efficiently while maintaining high availability and resilience.

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Seamless integration with various systems

Kafka facilitates the integration of multiple factoring systems and services, streamlining data flow and reducing complexity in the QuickPay application.

Kafka in practice | Case Studies

QuickPay

QuickPay

About the project

QuickPay is an app which purpose is to check data in various factoring systems and give information about contractor’s reliability. The solution functions on the Trans.eu exchange – one of the biggest freight exchanges in Europe.

Background
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