Redpanda Deep Dive: Kafka-Compatible C++ Broker, Tiered Storage & Iceberg Topics

2026-07-26 · Source: Data Engineering on Medium · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Advanced, extended

Summary

Redpanda is a Kafka-wire-compatible streaming broker, re-engineered in C++ on the Seastar async runtime, eliminating ZooKeeper and JVM dependencies. This architecture achieves sub-5 ms p99 latency, significantly outperforming Kafka clusters at similar throughputs. Key features include native Tiered Storage, which transparently moves cold data segments to object storage like S3, reducing costs from approximately \$0.10+/GB-month for EBS to \$0.023/GB-month. Additionally, Iceberg Topics, generally available in 2024, allow Redpanda topics to double as governed Iceberg tables, streamlining the streaming-to-lakehouse data flow by eliminating separate ETL pipelines. The article also compares Redpanda against Apache Kafka, WarpStream, and AutoMQ, detailing their trade-offs in latency, cost, and ecosystem integration, and provides senior-level interview guidance on these architectural distinctions and migration strategies.

Key takeaway

For data engineers or architects evaluating streaming platforms, Redpanda offers a compelling alternative to Apache Kafka, especially for latency-sensitive or cost-optimized workloads. You should consider Redpanda's C++ Seastar architecture for sub-5 ms p99 latency and its Tiered Storage for significant cost reductions on long-term data retention. Utilize Iceberg Topics to collapse complex streaming-to-lakehouse pipelines, simplifying your data architecture and reducing operational overhead. This shift can dramatically improve performance and TCO for your real-time data infrastructure.

Key insights

Redpanda's C++ Seastar architecture, tiered storage, and Iceberg Topics offer superior performance and cost efficiency for streaming data.

Principles

• Thread-per-core design eliminates JVM GC pauses.
• Tiered storage dramatically reduces long-term retention costs.
• Iceberg Topics collapse streaming-to-lakehouse pipelines.

Method

Enable Tiered Storage by setting "cloud_storage_enabled: true" and defining local/total retention policies per topic. Configure Iceberg Topics with "iceberg_enabled: true" and a catalog endpoint, then apply "value_schema_id_prefix" mode per topic.

In practice

• Use "rpk topic mirror" for wire-compatible Kafka migrations.
• Size local disk for 24-48 hours of hot data with Tiered Storage.
• Employ "io_uring" and thread-per-core for sub-5 ms p99 latency.

Topics

• Redpanda
• Kafka Compatibility
• Tiered Storage
• Iceberg Topics
• Lakehouse Architecture
• Low-Latency Streaming

Best for: Data Engineer, MLOps Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.