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This is a Quantitative Study study.

October 1, 2025World Journal of Advanced Research and Reviews

Designing resilient, low-latency data pipelines for streaming big data analytics using Apache Kafka and Spark ecosystems

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UUUju Ugonna Uzoagu

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Implication

This analysis explores low-latency data pipelines for streaming big data using Kafka and Spark, highlighting architectural design for scalability and fault tolerance.

Key Points

The integration of Kafka and Spark enables robust streaming data pipelines, supporting real-time decision-making.
Key architectural principles evaluated include fault tolerance through replication and exactly-once semantics.
Performance under various workloads shows significant improvements in latency and scalability for enterprise analytics.
The findings provide practical guidelines for building resilient infrastructures to meet demands of modern data environments.

Cite This Study

Uju Ugonna Uzoagu (2025) studied this question.

synapsesocial.com/papers/68dd9537fe798ba2fc4996e8 https://doi.org/10.30574/wjarr.2025.27.3.3369

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Implication

This analysis explores low-latency data pipelines for streaming big data using Kafka and Spark, highlighting architectural design for scalability and fault tolerance.

Designing resilient, low-latency data pipelines for streaming big data analytics using Apache Kafka and Spark ecosystems

Discussion

Member takes

Implication

Key Points

Cite This Study

Designing resilient, low-latency data pipelines for streaming big data analytics using Apache Kafka and Spark ecosystems

Discussion

Member takes

Implication

Key Points

Cite This Study

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