Hudi vs. Delta vs. Iceberg: How to Choose the Right Lakehouse Table Format

Why This Matters

A few years ago, data teams had to make a tough choice: the flexibility of a data lake or the reliability of a data warehouse. Now, the lakehouse architecture bridges that gap, combining cheap object storage with transactional guarantees, schema management, and even time travel. But here’s the catch — none of this works without a table format to organize the chaos of raw files.

If you’ve ever tried to manage updates, deletes, or schema changes in a plain S3 bucket, you know the pain. Table formats like Apache Hudi, Delta Lake, and Apache Iceberg solve this by adding a metadata layer that turns files into structured, queryable tables. They all promise ACID transactions, schema evolution, and scalability, but they’re not interchangeable. The right choice depends on your workload, team, and long-term goals.

In this post, I’ll break down each format’s strengths, weaknesses, and real-world fit — based on what I’ve seen working (and not working) in production.

The Core Problem: Why Table Formats Exist

Object storage is cheap and scalable, but it’s also dumb. Without a table format, you’re stuck with:

• No transactions: Updates and deletes are a nightmare.
• No schema history: Renaming a column? Good luck.
• No time travel: Need to roll back? Too bad.
• Concurrency issues: Multiple writers can corrupt your data.

Table formats fix this by maintaining metadata—essentially a "table of contents" for your data lake. This lets query engines like Spark, Trino, or Flink interact with files as if they were structured tables.

Apache Hudi: Built for Streaming

What It Is

Hudi (short for Hadoop upserts, deletes, and incrementals) was born at Uber to handle real-time data ingestion at scale. If your use case involves millions of events per second — think ride-sharing, IoT, or clickstreams — Hudi is designed for you.

Where It Shines

• Upserts and deletes: Hudi makes it easy to update or delete records, which is critical for GDPR compliance or real-time analytics.
• Incremental processing: Downstream jobs can pull only new or changed data, reducing compute costs.
• Streaming-first: Optimized for low-latency ingestion, unlike batch-focused alternatives.

The Catch

• Complexity: Managing compaction (merging small files) and clustering (organizing data for performance) requires tuning.
• Niche adoption: While growing, Hudi’s community is smaller than Delta’s or Iceberg’s.

Real-World Example

A ride-sharing company I worked with used Hudi to ingest driver location and trip updates in real time. With millions of events per second, Hudi’s upsert capability ensured that downstream analytics always reflected the latest state of each driver — without rewriting entire datasets.

When to pick Hudi: If your workload is streaming-heavy and you need frequent updates or deletes.

Delta Lake: The Generalist

What It Is

Delta Lake, created by Databricks, is the most widely recognized table format. It’s built on Parquet and adds ACID transactions, time travel, and schema enforcement.

Where It Shines

• ACID guarantees: Reliable transactions for both batch and streaming.
• Time travel: Query historical versions of your data (e.g., “What did this table look like last Tuesday?”).
• Ecosystem: Deep integration with Databricks, but also works with open-source Spark, Presto, and more.
• Simplicity: If you’re already using Spark, Delta Lake feels like a natural extension.

The Catch

• Vendor ties: While open-source, Delta Lake is strongly associated with Databricks.
• Community diversity: Outside Databricks, adoption isn’t as broad as Iceberg’s.

Real-World Example

A global retailer I advised used Delta Lake to manage sales data. Time travel let them audit revenue snapshots before and after corrections, while ACID transactions ensured consistency across BI dashboards and ML pipelines.

When to pick Delta Lake: If you want a general-purpose lakehouse with strong transactional guarantees, especially if you’re in the Databricks ecosystem.

Apache Iceberg: The Enterprise Workhorse

What It Is

Iceberg, originally built at Netflix, is designed for petabyte-scale analytics. It emphasizes schema evolution, partition flexibility, and broad engine support.

Where It Shines

• Schema evolution: Rename columns, reorder fields, or add new ones without breaking queries.
• Partition evolution: Change how data is partitioned over time (e.g., switch from daily to hourly).
• Engine agnostic: Works with Spark, Flink, Trino, Presto, Hive, and more.
• Community momentum: Adopted by Netflix, Apple, LinkedIn, and other large enterprises.

The Catch

• Streaming support: Historically weaker than Hudi, though Flink integrations are improving.
• Operational overhead: Metadata management requires careful tuning at scale.

Real-World Example

A financial services firm I consulted with adopted Iceberg for regulatory reporting. Schema evolution lets them adapt to changing compliance requirements without rewriting historical data. Broad engine support meant analysts could use Spark for ETL and Trino for ad-hoc queries—all on the same datasets.

When to pick Iceberg: If you need enterprise-scale analytics with diverse query engines and frequent schema changes.

Feature Comparison

How to Decide

There’s no one-size-fits-all answer. Here’s how I’ve seen teams make the call:

• Pick Hudi if… You’re drowning in streaming data and need upserts/deletes (e.g., real-time personalization, IoT, or GDPR compliance).
• Pick Delta Lake if… You want a reliable, general-purpose lakehouse with strong transactions and time travel — especially if you’re already using Databricks.
• Pick Iceberg if… You’re managing petabyte-scale datasets with diverse query engines and need schema flexibility.

The Reality: Mix and Match

Most mature teams don’t standardize on a single format. For example:

• Use Hudi for real-time ingestion.
• Use Delta Lake for analytics pipelines.
• Use Iceberg for regulatory reporting or cross-engine access.

Interoperability is improving, too. Tools like Trino and Spark now support all three formats, so you’re not locked in forever.

The Future: Convergence or Coexistence?

The “format wars” aren’t about one winner. Instead, we’re seeing:

• Interoperability: Engines supporting multiple formats.
• Standardization: Efforts like the Open Table Format Standardization project aim to reduce friction.
• Hybrid approaches: Teams use the best tool for each job.

My bet? The lines will blur. Hudi will get better at batch, Iceberg will improve streaming, and Delta will keep dominating in Databricks shops. The smartest teams will focus on flexibility — not dogma.

Final Thoughts

Hudi, Delta, and Iceberg are all powerful, but they’re optimized for different problems. The key is to match the format to your workload:

• Hudi for streaming and upserts.
• Delta Lake for general-purpose reliability.
• Iceberg for scale and schema flexibility.

And remember: the best teams don’t ask, “Which format is the best?” They ask, “Which format is best for this data?”

What’s your experience? Have you used one of these formats in production? What worked — or didn’t? Let’s discuss in the comments.