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# How to optimize real-time data ingestion in Snowflake and Iceberg

**[Redpanda](https://daily.dev/sources/redpanda)** · 11 min read · 0 upvotes · 0 comments

## Summary

Real-time data ingestion into Snowflake and Iceberg often incurs hidden costs from infrastructure complexity, small file proliferation, and inefficient processing pipelines. Traditional Kafka deployments require multiple components (brokers, ZooKeeper, connectors, schema registries), while continuous streaming creates thousands of small files that degrade query performance and increase storage costs. Optimization strategies include source-side filtering with CDC, edge aggregation, format optimization using Avro/Parquet with compression, time-based partitioning, maintaining 100-250 MB file sizes, and comprehensive monitoring of pipeline metrics and costs. Redpanda offers a simplified alternative by consolidating multiple components into a single binary with direct Iceberg integration, automatic file optimization, and built-in schema management.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.redpanda.com/blog/optimize-real-time-ingestion-snowflake-iceberg>

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---

Tags: [#kafka](https://daily.dev/tags/kafka), [#data-engineering](https://daily.dev/tags/data-engineering), [#snowflake](https://daily.dev/tags/snowflake), [#apache-iceberg](https://daily.dev/tags/apache-iceberg)

[View this post on daily.dev](https://daily.dev/posts/how-to-optimize-real-time-data-ingestion-in-snowflake-and-iceberg-2qswrw3zu)

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