No Ice for you. When Not to Use Iceberg: A Critical Evaluation

I love Ice, especially Iceberg, which helps simplify data management and enhances query performance without requiring users to manually manage partition metadata. Iceberg also provides transactional consistency between multiple applications where files can be added, removed, or modified atomically, with full read isolation and multiple concurrent writes. Given the recent hype that Databricks and Snowflake both adopted the Iceberg format, many companies in Southeast Asia are eager to jump on the bandwagon. Yes, the format provides a number of benefits, but you should ask your data engineering team to do due diligence before getting into the water. In this article, I write about when you should not get the ice.

Iceberg tables have become a popular choice for managing large-scale data in data lakes thanks to their ability to handle petabyte-scale datasets efficiently and their compatibility with various big data processing engines like Apache Spark, Apache Flink, and Trino. However, like any technology, Iceberg is not a one-size-fits-all solution. There are scenarios where using Iceberg may not be the most optimal choice. This article explores these scenarios, helping data engineers and architects make informed decisions about their data management strategies.

1. Small to Medium-Sized Datasets

Iceberg is designed to handle very large datasets, often exceeding terabytes or even petabytes. For smaller datasets, the complexity and overhead introduced by Iceberg may not be justified. Small to medium-sized datasets can be efficiently managed with simpler and less resource-intensive tools like traditional relational databases or simpler file formats (e.g., Parquet or Avro without the additional metadata layer).

2. Low-Latency Query Requirements

While Iceberg supports ACID transactions and can optimize for read-heavy workloads, it may introduce additional latency due to its sophisticated metadata management and versioning capabilities. In scenarios where low-latency query performance is critical, such as real-time analytics or operational dashboards, alternative solutions like Apache Druid or ClickHouse might be more appropriate due to their design optimizations for low-latency queries.

3. Limited Computational Resources

Iceberg’s advanced features, including its ability to manage schema evolution, partitioning, and data versioning, come at a computational cost. These operations require significant processing power and memory. In environments with limited computational resources, such as edge devices or smaller clusters, the overhead of managing Iceberg tables may outweigh its benefits. In such cases, simpler storage solutions with fewer resource requirements should be considered.

4. Non-Compatibility with Existing Tools

Although Iceberg is gaining traction and support across many big data processing tools, it might not be compatible with every tool in a company’s existing technology stack. If a critical tool or workflow does not support Iceberg, integrating Iceberg could necessitate substantial changes to existing systems and processes. Ensuring compatibility with the full range of analytical and data processing tools in use is essential before adopting Iceberg.

5. Complex Deployment and Maintenance

Deploying and maintaining an Iceberg-based data lake involves setting up and managing various components, including metadata catalogs, compute engines, and storage layers. This complexity can introduce operational challenges, especially for teams with limited experience in managing distributed systems. Organizations without the necessary expertise or resources might face steep learning curves and increased operational overhead.

6. Cost Considerations

The advanced capabilities of Iceberg, such as fine-grained metadata management and support for complex schema evolution, can lead to increased storage and computing costs. For organizations with tight budget constraints, it is important to weigh these costs against the benefits. Simpler and more cost-effective solutions may be more suitable for projects with limited budgets.

7. Specific Use Cases Requiring Different Optimizations

Certain use cases may require specific optimizations that Iceberg does not provide. For example, time-series data may benefit more from specialized databases like InfluxDB or TimescaleDB, which are optimized for such workloads. Similarly, graph data may be better managed with graph databases like Neo4j or Amazon Neptune. It is crucial to align the data management solution with the specific requirements of the use case.

Conclusion

While Iceberg offers significant advantages for managing large-scale data lakes, it is not always the best choice for every scenario. Small to medium-sized datasets, low-latency query requirements, limited computational resources, tool incompatibility, deployment complexity, cost considerations, and specific use cases may all warrant the consideration of alternative solutions. By carefully evaluating the unique needs and constraints of their data environments, organizations can choose the most appropriate and efficient data management strategies.