From complexity to clarity: Key challenges and considerations in migrating to Apache Iceberg
Authors:
Samiksha Saraf, Director of Technology
Ashish Kumar Dahiya, Senior Technical Architect
Gurvinder Arora, Senior Lead Technical Writer
In this second blog of our “Future-proofing Data Architectures with Iceberg” series, we tackle the key challenges of migrating to Apache Iceberg and the architectural considerations for a smooth transition. If you missed our first blog on Iceberg’s architecture and benefits, check it out here.
Apache Iceberg is a game-changer for building efficient, future-proof data lakes. But migrating from traditional platforms like Hadoop, Teradata, or Netezza isn’t without its challenges. Don’t worry—we’ve got you covered. This blog will break down the top migration hurdles and key architectural considerations, from file management and data recovery to concurrency handling, ensuring a smooth transition and a scalable, resilient data ecosystem.
Top migration challenges
Let’s face it: migrations are tricky. But migrating to an Iceberg-based data lake adds a whole new layer of complexity. Here are some common hurdles you might encounter:
1. Complexity
Migrating to Iceberg isn’t just about moving files from point A to point B. You’re looking at reorganizing files, redesigning partition logic, and migrating metadata. If you think you can do this overnight, think again. It requires thorough testing and validation to ensure your performance improves rather than tanking under the weight of these changes.
2. Data inconsistencies
Iceberg’s complex metadata system and schema evolution model can uncover hidden data inconsistencies during migration. Imagine setting off a domino effect where the slightest mismatch creates chaos. To tackle this, you’ll need to be sharp with Spark or Impala settings and closely monitor Iceberg’s snapshot model to manage historical data.
3. Planning
Without a proper game plan and the right tools in place, you won’t be able to tap into Iceberg’s flexibility and efficiency. A phased migration strategy and comprehensive validation process are critical to unlocking its scalability.
4. Query optimization and compatibility
Legacy data warehouses often optimize their queries through indexing or other tricks, which don’t always translate well to Iceberg. You might need to rework or optimize those old queries using techniques like dynamic file pruning and efficient partitioning—things Iceberg does differently from traditional warehouses.
5. Downtime
Migrating live datasets comes with its risks, especially when you’re managing concurrency. Downtime is a real threat if you don’t get this right. Large-scale migrations also demand significant resources, so make sure your team is trained in Iceberg’s features and optimizations.
6. Data lineage and auditing
Legacy systems have built-in mechanisms for tracking data lineage and auditing, but with Iceberg, you’ll need to set these up yourself. Custom tooling or integrations with Apache Atlas are often necessary to maintain the same level of transparency and control.
Key architectural considerations for migration
Now that we’ve identified the obstacles, let’s explore the architectural side. Designing a solid architecture with Apache Iceberg isn’t just about putting pieces together—it’s about making intelligent choices to ensure performance, scalability, and future-proofing. Here are the key considerations:
1. Data management strategies
Apache Iceberg offers robust data management approaches that support various workloads, from transactional updates to high-volume data appends. Choosing the right strategy depends on your workload requirements:
- Merge-on-Read (MoR): This is your go-to if your workload is write-heavy and involves fewer updates or deletes. MoR defers the merging of updates and deletes until the query time, which gives you higher write throughput.
- Copy-on-Write (CoW): If you’re more focused on read optimization, CoW might be your best bet. It rewrites entire data files when rows are updated or deleted, so your queries will only scan updated data—no need for last-minute merges.
2. File storage format and compression
Apache Iceberg supports several file formats like Avro, Parquet, and ORC. Choosing the right one depends on workload type and data structure:
- Avro: Best for write-heavy workloads where records are processed one at a time, like in streaming.
- Parquet/ORC: Ideal for analytic workloads where you’re scanning specific columns across millions of records.
As for compression, Snappy is your friend if you need fast compression and decompression, though it comes at the cost of lower compression ratios. ZSTD is an excellent alternative if you’re looking for better compression, although its compression speed is slower (but decompression is comparable to Snappy).
3. Data recovery
Iceberg’s snapshot-based architecture is a game-changer when it comes to data recovery. Every time your data changes—whether through inserts, updates, or deletes—Iceberg creates a snapshot, giving you a complete representation of your data at that point in time.
- Snapshots and time travel: Iceberg creates a snapshot of the table with every modification, allowing you to revert to previous versions in case of failure, corruption, or accidental deletion.
- Tagging and branching: Want to keep your teams working on different versions of the data without stepping on each other’s toes? Branching lets them work independently, while tags help you preserve critical points in a table’s history, providing flexibility in long-term operations.
4. Concurrency management
Iceberg uses optimistic concurrency control for handling simultaneous write operations, which can sometimes lead to conflicts. It’s like managing a traffic jam in real time—if two cars (or in this case, operations) try to take the same lane, you need to reroute one. Iceberg’s retry mechanisms only handle these conflicts by updating metadata, but you may need to tweak default settings to fit your specific use case.
A few key settings to adjust:
- commit.retry.num-retries: How many times to retry a commit before giving up.
- commit.retry.min-wait-ms: The minimum time to wait before retrying.
- commit.retry.max-wait-ms: The maximum time to wait.
Tuning these settings is critical for managing concurrent workloads efficiently and minimizing failure risks.
Overwhelmed? Let LeapLogic simplify your Apache Iceberg migration
Migrating to Apache Iceberg isn’t just a technical shift; it’s a strategic move to future-proof your data architecture. By understanding and addressing key challenges such as data management, file storage, and concurrency control, you can unlock the full potential of Iceberg’s advanced capabilities. But migration doesn’t have to be overwhelming.
LeapLogic’s automated migration accelerator is designed to simplify this transition, ensuring a seamless migration while minimizing risk and downtime. With specialized expertise and proven tools, we enable you to modernize your data platform efficiently so you can focus on driving innovation and gaining a competitive edge. Stay tuned for our next blog to explore how LeapLogic can accelerate your Iceberg migration journey.
