Comparative Analysis of Delta Lake, Apache Iceberg, and Apache Hudi for Data Lake Solutions

The three most popular open‑source data‑lake solutions on the market are Delta Lake, Apache Iceberg, and Apache Hudi. Delta Lake, backed by Databricks, benefits from Spark’s commercial success; Hudi was created by Uber engineers to meet internal analytics needs, offering fast upserts, deletes, and compaction; Iceberg, while less feature‑rich today, is designed with high abstraction and elegant architecture for a universal data‑lake foundation.

Many users wonder which solution fits their scenario. This article deconstructs core data‑lake requirements, deeply compares the three products, and guides users in selecting the right data‑lake based on their workloads.

Databricks and Delta

Delta Lake aims to solve problems illustrated in the Databricks slide (https://www.slideshare.net/databricks/making-apache-spark-better-with-delta-lake). Before Delta, Databricks customers typically used a classic Lambda architecture: Kafka streams feed Spark Streaming for real‑time results, while batch Spark jobs write full click‑stream data to Parquet files on HDFS or S3 for downstream batch analytics and AI.

Key issues with this approach include:

Inconsistent schema enforcement leading to costly data‑cleaning in downstream jobs.

No ACID guarantees during writes, causing readers to see partial data and making versioning difficult.

High cost of upserts/deletes because Parquet files must be rewritten entirely.

Generation of many small files that overload HDFS.

Databricks identified four essential data‑lake capabilities, shown in the following diagram:

Delta’s design unifies streaming and batch workloads at the storage layer, allowing Kafka‑ingested data to be accessed by any analytics engine for reporting, streaming, or AI.

The core features Delta focuses on are illustrated below:

Uber and Apache Hudi

Uber’s original data‑lake (2014) used Kafka → S3 → EMR for batch analytics and Vertica for operational queries. Problems included messy schemas, high expansion cost, and difficult data back‑fills. After moving to the Hadoop ecosystem, scalability improved but fast upserts remained a pain point.

Uber’s ETL refreshed data every 30 minutes, rewriting full datasets, leading to high latency and resource consumption. They needed a solution that supported fast upserts and incremental streaming consumption.

Hudi provides both Copy‑On‑Write and Merge‑On‑Read formats. Merge‑On‑Read enables fast upserts by writing incremental delta files and periodically compacting them, offering three read views: delta‑only, data‑only, or merged.

The resulting Uber data‑lake requirements align with Hudi’s core strengths, as shown in the diagram:

Netflix and Apache Iceberg

Netflix migrated from Hive to a self‑developed Iceberg because Hive’s metadata (MySQL + HDFS) caused massive partition counts (2.688 M partitions, 2.7 M files per month) and slow queries. Hive’s metadata split, lack of atomic writes, missing file‑level statistics, and tight coupling to HDFS made it unsuitable for S3.

Iceberg was built as a highly abstracted, universal data‑lake format. Although it currently lacks some features compared to Delta and Hudi, its solid foundation promises strong future potential.

Netflix’s core Iceberg requirements are summarized in the following diagram:

Pain‑Point Summary

Aggregating the pain points of the three projects reveals the essential features a good data‑lake should provide (highlighted in red in the diagram):

7‑Dimension Comparison

After understanding each project’s design goals, we compare them across seven dimensions, also including Hive ACID as a reference.

1. ACID and Isolation Level Support

Snapshot Isolation offers the best concurrency among the three.

2. Schema Evolution Support

Hudi supports backward‑compatible add‑optional‑column and drop‑column operations; Iceberg provides a decoupled schema abstraction; Delta and Hive have more limited evolution capabilities.

3. Streaming‑Batch Interface Support

Iceberg and Hive currently lack native streaming consumption.

4. Abstraction Level and Pluggability

Iceberg achieves the highest decoupling of compute, storage, and file format; Delta and Hudi are tightly bound to Spark.

5. Query Performance Optimization

6. Additional Features

Delta offers the simplest one‑line demos; Iceberg provides strong Python support and file‑level encryption; Hudi excels at upserts and compaction.

7. Community Status (as of 2020‑01‑08)

Delta and Hudi have vibrant community outreach, extensive documentation, and active webinars; Iceberg’s community is more focused on GitHub issues and PRs.

Conclusion

We summarize the three products (Delta’s open‑source and commercial editions, Hudi, Iceberg, and Hive‑ACID) in the following diagram:

Using a house‑building metaphor: Delta’s foundation is solid and its floors are high but tightly coupled to Spark; Iceberg’s foundation is robust and extensible but its floors are still under construction; Hudi’s foundation is less solid, requiring more work to integrate other engines, yet its floors already address many user pain points; Hive‑ACID resembles a mansion with many features but also hidden structural issues.