Apache Hudi vs. Iceberg: Which Table Format Wins for Your Data Lakehouse?

Your VP of Engineering just forwarded yet another eye-watering Snowflake bill, asking: “Can we move some of this to a data lake?” 

But you know the answer isn’t that simple. Modern data architecture isn’t about choosing between data warehouses and data lakes anymore – it’s about building a lakehouse that gives you the best of both worlds. 

And at the heart of that decision sits a choice that will shape your query performance, storage costs, and operational complexity for fiscal quarters to come: Apache Hudi or Apache Iceberg? 

Both table formats promise to turn your data late from a swamp of Parquet files into a queryable, ACID-compliant data platform – and integrate with Snowflake, Spark, and the broader data ecosystem. So, how do you choose? 

Short answer? You’ll want to take into account: 

• Workload patterns
• Team capabilities
• How you balance streaming updates against query performance

The long answer requires understanding the architectural differences that make each format excel in different scenarios. 

What Are Table Formats and Why Should You Care? 

Before we get into Hudi vs. Iceberg, let’s cover the problem these technologies solve. 

Traditional data lakes store files – usually Parquet, ORC, or Avro – organized in directory structures. This works fine, unless you need to update a record, delete old data for compliance, or keep concurrent readers and writers from corrupting your data. Suddenly your “simple” file-based storage becomes a maintenance nightmare of manual partition management, schema evolution headaches, and inconsistent data. 

Table formats sit in a metadata layer above your raw files, which enables database-like capabilities on object storage. They can: 

• Track which files belong to which table version
• Handle concurrent access
• Manage schema changes
• Enable time travel queries

All while keeping your data in open formats any tool can read. 

For organizations running Snowflake, table formats offer a strategic advantage: you can offload cold data and less frequently accessed tables to cheaper object storage while maintaining query capabilities. When properly optimized, this hybrid approach can reduce costs without sacrificing performance. 

The question here isn’t if you need a table format. If you’re building a lakehouse, you do. The question is which one: Apache Hudi or Iceberg. 

What Is Apache Hudi? 

Apache Hudi (Hadoop Upserts Deletes and Incrementals) was Uber’s product, created in 2016 to ingest streaming data into their data lake while maintaining sub-hour data freshness. The name tells you exactly what it was made for: handle upserts and deletes efficiency in a system originally built for append-only operations. 

Hudi’s Core Architecture

Hudi’s defining characteristic is its dual storage type system. When you create a Hudi table, you choose between two fundamentally different approaches: 

• Copy-on-Write (CoW): These tables store data in columnar formats and create new versions of files whenever data changes, so if you update a single record in a file containing a million rows, Hudi reads the entire file, applies the update, and writes a completely new file. This sounds expensive – and for writes, it is – but reads are blazingly fast because they’re just reading standard Parquet files with no additional processing needed. 
• Merge-on-Read (MoR): These tables take the opposite approach, so updates go into row-based delta logs (similar to database write-ahead logs), and Hudi only merges these deltas into the base columnar files during periodic compaction. Writes are fast because you’re appending to a log, but reads require merging the base files with all subsequent deltas, adding query overhead. 

Your choice of architecture here has massive impact: 

• Your ingestion pipeline performance
• Query latency
• Storage costs
• Operational complexity

Basically, you’re choosing if you want to pay the cost at write time or read time. 

Hudi Pros

Hudi excels in specific scenarios where its architecture delivers clear advantages: 

• Streaming-first architectures: If you’re building real-time analytics platforms that process click streams, IoT sensor data, or change data capture (CDC) from operational databases, Hudi’s incremental processing capabilities are made for those patterns. 
• Incremental query processing: Instead of re-processing entire tables, you can query just the records that changed since your last read. 
• Continuous update handling: When ingesting customer transaction data from multiple sources that send corrections hours after the original transaction, Hudi MoR tables accept updates continuously without disrupting ongoing queries. This compaction happens during low-traffic windows while analysts always see the most current version. 
• Efficient upsert operations: Record-level indexing means updates and deletes can locate specific records efficiently, even in massive tables. This is much more efficient for workloads with many small updates scattered across partitions. 
• Write-optimized workloads: MoR tables make writes fast by appending to delta logs, deferring the expensive merge operation until compaction runs on your schedule. 

Pro tip: For Snowflake users handling incremental query processing, this means pulling only updated rows from staging areas without complex timestamp logic or expensive full table scans. 

Hudi Cons

The flexibility that makes Hudi so powerful also introduces challenges that teams need to manage: 

• Steeper operational learning curve: You need to understand compaction strategies, choose appropriate table types for different workloads, tune write performance parameters, and monitor background processes. Teams without deep data engineering expertise often find this type of overhead a significant burden. 
• Compaction management overhead: In MoR tables, compaction keeps read performance from degrading as delta logs accumulate, so you have to schedule compaction jobs, monitor their progress, allocate appropriate compute resources, and handle failures gracefully. Poor compaction results in either degraded read performance or excessive resource consumption. 
• Limited ecosystem support: While it is improving, Hudi’s ecosystem support still lags behind Iceberg. Some query engines offer read-only support or limited feature coverage. 
• Snowflake integration limitations: The Snowflake-Hudi integration is read-only and less polished than Iceberg support. 

Pro tip: For organizations betting on multi-engine architectures where you might query data with Snowflake, Databricks, Athena, and Tino, Iceberg’s broader compatibility becomes increasingly appealing. 

What Is Apache Iceberg? 

Apache Iceberg took a slightly different path to solve the same problems. It was started by Netflix in 2017 and open-sourced in 2018, made for analytical workloads at massive scale. Where Hudi evolved from streaming use cases, Iceberg’s DNA is pure analytics. 

Iceberg’s Architecture

Iceberg’s architecture centers on three metadata layers that work together to provide fast queries and safe concurrent access: 

• Metadata files
• Manifest list
• Data files

Metadata files track the current table state – the schema version you’re using, which snapshot is current, and where to find data files. The manifest list points to manifest files, which then point to the actual data files. 

This three-level hierarchy might seem over-engineered, but it allows for powerful features like O(1) snapshot creation and efficient metadata pruning. 

This is what happens when you query an Iceberg table: 

• The engine reads the metadata files
• Fetches relevant manifest files
• Uses them to skip entire data files that don’t contain relevant data

Hidden partitioning is one of Iceberg’s most user-friendly features. Instead of requiring users to specify partition columns in their queries (like traditional Hive partitioning), Iceberg automatically routes data to the right partition based on partition transforms you define at table creation. That means users can query the table without thinking about partitions, and Iceberg handles the optimization automatically. 

Pro tip: This matters a lot when integration with Snowflake. You can set up Iceberg external tables in Snowflake, and the query optimizer benefits from partition pruning and metadata without your analysts needing to understand the underlying partitioning schema. 

Iceberg’s Pros

Iceberg has achieved something rare in engineering: near-universal adoption that translates to tangible benefits. 

• Unmatched ecosystem compatibility: Every major data platform supports Iceberg as a first-class citizen. You can create an Iceberg table in Snowflake, query it with Spark for ML feature engineering, analyze it in Athena for cost optimization, and run ad-hoc queries in Trino, all against the same underlying data with consistent results. 
• Superior analytical query performance: The combination of hidden partitioning, aggressive metadata pruning, and columnar data layouts optimized for scans means read-heavy analytical workloads usually run faster than Hudi MoR tables. 
• Automatic partition handling: Hidden partitioning routes data to the right partition based on transforms you define once at table creation. Users query without thinking about partitions, and Iceberg handles optimization automatically. 
• Clean time travel versioning: Every change creates a new lightweight snapshot, so you can query any historical version, rollback mistakes, and audit changes without maintaining separate history tables or complex temporal query logic. 
• Simpler operational model: Cleaner abstractions and fewer knobs to tune mean faster time to production and reduced operational burden for teams without deep lakehouse expertise. 

Iceberg’s Cons

Iceberg’s analytical optimization creates specific challenges in certain workload patterns: 

• Update performance overhead: Since Iceberg doesn’t have native merge-on-read capability, updates require rewriting entire data files. 
• CDC and streaming inefficiency: For CDC workloads or streaming pipelines with frequent updates, write amplification creates increased compute costs and latency. 
• Small file proliferation: Frequent small writes create numerous small files that degrade query performance over time. File rewriting (Iceberg’s version of compaction) is required to consolidate files, adding operational overhead and resource requirements. 
• Newer merge-on-read features: Iceberg v2 introduced position deletes and quality deletes to address update performance, but these features aren’t as mature or battle-tested as Hudi’s merge-on-read implementation. 

Apache Hudi vs. Apache Iceberg

Here’s a direct comparison fo how Hudi and Iceberg compare across the dimensions that matter most for production deployments:

Making the Decision: A Framework for Your Organization

Choosing between Hudi and Iceberg isn’t about picking the “better” technology – it’s about matching technology characteristics to your organization’s specific needs. 

When to Choose Apache Hudi

You should go with Apache Hudi if: 

• Your workloads are streaming-first: If you’re building real-time data platforms with CDC pipelines, IoT ingestion, or event streaming, Hudi’s merge-on-read capability and incremental processing are purpose-built for these patterns. 
• Updates and deletes are frequent and scattered: When you need to update records across partitions continuously – think user profile updates, transaction corrections, or real-time inventory adjustments – Hudi’s record-level indexing and efficient upsert handling excel. 
• You have strong data engineering capabilities: Hudi requires more operational expertise to run well. If you have experienced data engineers who can tune compaction strategies, monitor performance, and handle the additional operational complexity, Hudi’s flexibility becomes an advantage. 
• Your primary compute engine is Spark: Hudi’s deepest integration and best performance come from Spark. If your data platform is Spark-centric with Snowflake as a secondary query layer, Hudi’s limitations in Snowflake matter less.  

When to Choose Apache Iceberg

On the other hand, you should choose Apache Iceberg if: 

• Analytical queries dominate your workload: If most of your queries are ad-heavy analytics, reporting, or data science workloads with occasional batch updates, Iceberg’s optimized read path and clean metadata architecture deliver better performance. 
• Multi-engine access is critical: When you need to query the same data Snowflake, Databricks, Athena, Trino, and other engines with consistent results and good performance everywhere, Iceberg’s ecosystem support is unmatched. 
• Operational simplicity matters: Iceberg is generally easier to operate, with cleaner abstractions and fewer knobs to tune. 
• Snowflake is your primary data platform: If Snowflake is central to your architecture and you’re using Iceberg for cost optimization via tiered storage, the tighter integration and bi-directional support make Iceberg the natural choice. 
• Schema  governance and safety are priorities: In regulated industries or data environments where breaking downstream consumers is unacceptable, Iceberg’s strict schema evolution rules prevent entire classes of problems. 

When to Choose Both

No rules have to say you must choose just one! Many organizations run both Hudi and Iceberg for different use cases: 

• Hudi for real-time operational data with frequent updates
• Iceberg for analytical datasets and historical archives
• Regular ETL processes that promote data from Hudi to Iceberg once it stabilizes

This approach adds architectural complexity but maximizes each format’s strengths. The key is establishing clear boundaries and governance to prevent the “use both” strategy from becoming an unmaintained mess. 

Stop Worrying About Table Formats, Start Optimizing What Matters

Here’s the unfortunate truth: most organizations spend weeks debating Hudi vs. Iceberg while their Snowflake costs spiral from poorly optimized warehouses, oversized clusters, and inefficient queries. 

Yes, choosing the right table format matters. But obsessing over this decision while ignoring the dozens of cost optimization opportunities in your existing data platform is not a good use of your time. 

The engineering managers we work with typically discover their biggest cost savings don’t come from perfect table format execution. Instead, they see gains from: 

• Right-sizing warehouses
• Implementing query result caching
• Optimizing clustering keys
• Eliminating unnecessary data scanning
• Fixing inefficient transformation logic

These are the unglamorous, high-impact improvements that actually move the needle. 

This is where Yuki excels. Whether you choose Hudi, Iceberg, or stick with pure Snowflake tables, our platform continuously optimizes your environment, no dev lift needed. Yuki helps: 

• Automatically identify oversized warehouses
• Recommend clustering improvements 
• Catch inefficient queries before they drain your budget
• Provide actionable insights that directly impact your bottom line 

Ready to stop debating architecture and start saving money? See how Yuki can optimize your Snowflake environment – regardless of which table formats you choose – and start seeing results in days, not months.