Streamlined Data Processing: Unveiling Limitations and Exploring Broader Horizons

3 min readFeb 4, 2024


In today’s data-centric landscape, managing and processing large datasets is crucial. Distributed data processing platforms like Kinesis Data Streams and Apache Kafka are vital for real-time data ingestion, processing, and analysis. Choosing between them involves careful consideration of factors such as data retention, real-time processing, message ordering, processing semantics, scalability, and integration capabilities.

This article aims to offer a range of options, including mix-and-match possibilities, enabling you to make an informed decision tailored to your specific data processing needs.

Kinesis Data Streams

Data Retention: Within teams, Kinesis Data Streams has proven instrumental in real-time data processing strategies. While it offers a maximum retention period of 7 days, our experience with Apache Kafka highlighted the need for longer retention periods.


  • Amazon Kinesis Data Firehose and Apache Hudi/Amazon Athena: This remains a valid option for improved data retention, offering long-term archival capabilities.
  • Amazon S3 Glacier: Consider this cost-effective solution for storing inactive data for extended periods.

Real-Time Processing: While Kinesis Data Streams provides near-real-time processing, its latency prompted us to leverage our Apache Kafka experiences for lower latency needs.


  • Apache Flink: This open-source stream processing framework remains a powerful choice for achieving real-time stream processing with significantly lower latency.
  • Amazon Kinesis Data Analytics: Explore this serverless option for near-real-time stream processing with built-in capabilities for exactly-once delivery.

Message Ordering: Kinesis Data Streams lacks guaranteed message ordering within a shard. For message consistency, we compared it to Apache Kafka’s strong ordering guarantees.


  • Apache Pulsar: This streaming platform provides robust ordering guarantees and consistent message delivery. However, introducing another service incurs complexity.
  • Amazon SQS FIFO queues: Consider this managed service for specific use cases requiring message ordering at scale, potentially reducing complexity.

Exactly-Once Processing: Kinesis Data Streams offers at least-once processing semantics, whereas Apache Kafka boasts exactly-once processing capabilities.


  • Apache Beam: Implementing idempotent processing techniques within consumer applications using Beam is a viable option, but custom hosting can be complex.
  • Amazon Kinesis Data Analytics: This managed service offers the benefit of exactly-once semantics with easier implementation and integration into AWS environments.

Compaction: Kinesis Data Streams does not have built-in compaction functionality.


  • Data Firehose + Hudi/Athena: This option offers long-term storage and compaction capabilities, but introduces complexity and potential performance trade-offs.
  • Amazon Managed Streaming for Apache Kafka (MSK) provides managed Kafka with its native compaction capabilities, including upsert-like behavior for messages with the same key.
  • External tools: Apache Flink or Spark Streaming can be used with Kinesis for custom compaction logic, but this requires additional development and management overhead.

Scalability & Durability: Kinesis Data Streams dynamically scales based on data throughput. However, we draw from our Apache Kafka experience for more granular control over scalability.


  • Amazon Managed Streaming for Apache Kafka (Amazon MSK): This managed service leverages the proven scalability and durability of Apache Kafka within the AWS ecosystem.

Ecosystem and Integration: Kinesis Data Streams excels in integration with various AWS services. However, for broader integration options, we explored alternative tools.


  • Apache NiFi (using Amazon EMR): This open-source dataflow management tool facilitates seamless data flow across different technologies, enhancing integration capabilities.

Apache Kafka

Manual Management: Previously, manual management of Apache Kafka clusters presented challenges. We investigated tools like the Confluent Platform to simplify this process.

Operational Overhead: Adopting managed Kafka services like Confluent Cloud or AWS Managed Streaming for Apache Kafka (MSK) significantly reduced operational overhead, allowing our teams to focus on core tasks.

Resource Intensiveness: Our hands-on experience with Apache Kafka, coupled with Kubernetes-based orchestration utilizing Strimzi, and drawing insights from cloud-native solutions such as Red Hat OpenShift, has guided our approach to optimizing resource utilization and refining allocation strategies.

Real-Time Processing: For applications requiring extremely low latency, we previously integrated Apache Kafka with Apache Pulsar. This provided enhanced support for low-latency messaging and real-time event processing.

In navigating the strengths and challenges of both Kinesis Data Streams and Apache Kafka, these summaries draw from collective experiences across teams and various sources, including extensive internet research.

It’s important to note that not all solutions have been universally implemented, highlighting the evolving landscape of distributed data processing. Each platform’s unique characteristics contribute to a broader perspective on building resilient and scalable data processing architectures.




A versatile software engineer, he has a range of interests beyond coding. Earlier posts are here :