Apache Iceberg: The Data Lake Breakthrough That’s Reshaping the Big Data Landscape

By the end of this read, you’ll understand why Apache Iceberg is not just another open table format — it’s the seismic shift that’s redefining the role of Delta Lake in the modern data ecosystem and transform how the world thinks about data lakes.

Table of Contents

From Data Lakes to Data Icebergs: A New Era Begins

Over the last decade, data lakes have evolved from simple storage backwaters into modern analytical powerhouses. Yet, as volume, velocity, and variety of data grew exponentially, legacy architectures — even open formats like Apache Hive — began to crack.

Enter open table formats like Apache Hudi, Delta Lake, and Apache Iceberg. While all three aim to solve the same foundational problem — enabling ACID transactions, schema evolution, and performant reads/writes on data lakes — only Iceberg is designed for true long-term scale, interoperability, and evolution.

The Foundation: What Are Open Table Formats?

Traditional data lakes (on S3, ADLS, or HDFS) lacked:

❌ ACID compliance
❌ Schema evolution
❌ Efficient deletes, upserts, and time travel
❌ Concurrency control

To solve this, open table formats emerged, sitting on top of files like Parquet/ORC, but managing metadata, snapshots, and consistency in a structured and transactional way.

The three major players:

Apache Hudi (Uber)
Delta Lake (Databricks)
Apache Iceberg (Netflix)

But while they all promise similar capabilities, their design philosophies are radically different.

Comparing the Titans: Delta vs Hudi vs Iceberg

Feature / Capability	Delta Lake	Apache Hudi	Apache Iceberg
ACID Transactions	✅	✅	✅
Time Travel	✅ (via logs)	✅ (limited)	✅ (snapshot-based)
Schema Evolution	Partial	Limited	✅ Full
Hidden Partitioning	❌	❌	✅
Engine Compatibility	Spark-centric	Spark, Flink	Spark, Flink, Trino, Presto, Hive, etc
Multi-engine Writes	❌	❌	✅
Snapshot Isolation	Partial	Partial	✅
Streaming + Batch Support	✅	✅	✅
Spec Evolution	Vendor-controlled	Community-driven	✅ Open governance

Deep-Dive Comparison: Iceberg vs Delta Lake

To better understand Iceberg’s competitive edge, let’s explore the five most critical areas in depth — from day-to-day CRUD operations to long-term cost and snapshot management.

Category	Delta Lake	Apache Iceberg
CRUD Operations	Supports MERGE, UPDATE, DELETE (Spark only); tightly coupled with Delta APIs	Supports MERGE, UPDATE, DELETE across engines (Spark, Flink, Trino); enables REST-based APIs for better integration
Schema Evolution	Add columns only; limited rename/drop; no nested schema reordering	Full support for add, drop, rename, reorder, nested schema evolution without rewriting data
Partition Management	Static, directory-based; partition strategy must be predefined; changing requires table rewrite	Dynamic and flexible; supports partition evolution without rewrites using metadata-based transforms
Cost Efficiency	Compute-heavy log parsing for reads; compaction needed; metadata grows quickly	Low compute overhead via manifest pruning; compact snapshots; no reliance on JSON logs; faster reads
Snapshot Management	JSON log files stored in cloud; slow rollback and audit; prone to log bloating JSON logs; requires checkpointing for performance	Lightweight metadata-based snapshots; true time travel; atomic rollback; efficient auditability Full snapshot isolation; lightweight manifest-based history

Why Delta & Hudi Are Now Hitting Their Limits

Delta Lake – Powerhouse, but Proprietary DNA

Developed by Databricks, Delta Lake gained huge traction due to tight Spark integration, time travel, and ACID support.
However, it has a Spark-first mindset. While read connectors exist for other engines like Presto and Trino, write support is still limited outside Spark.
Its JSON-based transaction log becomes a bottleneck at scale.
Schema evolution is limited, and doesn’t allow dropping or renaming columns easily.

Apache Hudi – Great for Ingest, Not for Analytics

Designed at Uber, Hudi shines in incremental ingestion.
But it struggles with query latency, metadata maturity, and engine interoperability.
Its complexity increases with analytical workloads at scale.

Breaking Down Cost: Compute, Storage, and Maintenance

Cost is a pivotal factor when choosing a data lake architecture — and Apache Iceberg shines with real savings across all angles:

Compute Cost

Delta Lake depends on reading and merging large transaction logs (JSON), which leads to high compute costs during read operations, compaction, and vacuum.
Iceberg leverages manifest files and metadata layers to prune irrelevant data before reading, saving massive compute cycles.

Storage Cost

Delta stores transaction logs as individual JSON files, often leading to bloated storage.
Iceberg’s manifest lists and metadata snapshots are compressed and clean, minimizing S3/ADLS usage.

Maintenance Cost

Iceberg tables are easier to maintain due to schema flexibility, auto-expiring snapshots, and partition evolution.
Delta often requires manual intervention for log clean-up, schema conflicts, and partition rewrites.

Iceberg delivers a lower TCO (Total Cost of Ownership) over time.

Enter Apache Iceberg: A Game-Changer at Scale

Iceberg is not just an alternative — it is a generational leap.

Built at Netflix to handle petabyte-scale data lakes, Iceberg is designed for openness, scale, and cross-engine consistency.

Key Advantages of Apache Iceberg

Engine-Agnostic Architecture
Works seamlessly with Spark, Flink, Trino, Presto, Hive, Dremio, and more.
Hidden Partitioning
Query without needing to know partition schemes.
Immutable Snapshots & Versioned Metadata
Enables time travel, rollback, and audit — without parsing logs.
Full ACID Compliance
High-scale reads/writes with snapshot isolation.
Advanced Schema Evolution
Supports column add, drop, rename, reorder — even for nested fields.
Open Governance
Neutral spec under Apache Foundation ensures community-driven evolution.

Partition Evolution in Apache Iceberg: The Hidden Superpower

Partitioning is the backbone of scalable query performance in data lakes. But in legacy systems, it’s brittle and static.

The Problem with Traditional Partitioning

Legacy systems (Hive, Delta, Hudi) use directory-based partitioning:

/sales/year=2023/month=05/day=01/

Problems:

❌ Changing partition strategy = full table rewrite
❌ Query performance drops if partitions are poorly chosen
❌ Requires user awareness of partition layout

Iceberg’s Solution: Logical Partitioning

Iceberg uses transform-based logical partitioning:

{
  "partition": {
    "year": "2023",
    "month": "05"
  }
}

No dependency on physical paths. Metadata and manifests track partitions for query pruning.

What is Partition Evolution?

Partition Evolution = Ability to change partitioning strategy without rewriting the table.

You can:

✅ Add new partition fields
✅ Drop old fields
✅ Modify transform (e.g., month → day)

Example:

-- Add a new partition field
ALTER TABLE sales ADD PARTITION FIELD day(ts);

-- Drop old one
ALTER TABLE sales DROP PARTITION FIELD month(ts);

Old data retains old spec. New data follows new spec. Queries span both seamlessly.

Real-World Benefits

Traditional Systems	Apache Iceberg
Static partition strategy	Dynamic partition evolution
Rewrites required for changes	No rewrites required
Partition layout = storage path	Partition layout = metadata

Supported Partition Transforms

Transform	Description
`identity(col)`	Use raw value
`year(ts)`	Partition by year
`month(ts)`	Partition by month
`day(ts)`	Partition by day
`bucket(col, N)`	Hash-bucketing
`truncate(col,N)`	Truncate value

Snowflake’s Strategic Bet on Apache Iceberg

I remember working on bringing datasets into Snowflake using Snowpipe and facing many challenges due to its append-only nature. It made handling late-arriving data, updates, and reprocessing pipelines cumbersome and operationally expensive. Snowpipe often led to increased compute costs due to duplicate processing and required workarounds for idempotency. At the time, I explored Apache Iceberg as a potential solution — it promised better support for mutable operations and time travel. However, the support within Snowflake was still in early stages and lacked robust tooling. I stayed with Snowpipe out of necessity — but watching how far Iceberg has evolved today, it clearly represents a major shift in how we handle modern data ingestion and transformation.

Snowflake has made continuous investments to support and enhance Apache Iceberg, reinforcing its commitment to open data lakehouse architectures and challenging proprietary formats like Delta Lake.

Schema Inference with `INFER_SCHEMA`

In its May 2025 (9.13) release, Snowflake added support for the INFER_SCHEMA function tailored for Apache Iceberg. By setting KIND = ICEBERG, users can automatically extract schema definitions from semi-structured files stored in external stages. This significantly simplifies the creation of Iceberg tables using:

CREATE ICEBERG TABLE ... USING TEMPLATE;

This enhancement streamlines onboarding and management of new Iceberg datasets.

Cross-Cloud and Cross-Region Support

Snowflake now supports cross-cloud and cross-region reads and writes for Iceberg tables that use Snowflake as their catalog. This allows global data sharing and federated architectures without needing to move data physically.

Structured Data Type Support

Snowflake has fully aligned with Iceberg’s schema model by supporting structured types such as:

list
struct
map

This ensures that complex and nested data types in external Iceberg tables are natively understood and queried inside Snowflake.

Query Performance Optimizations

As of early 2025, Snowflake has optimized resource usage and metadata management for Iceberg tables. Improvements include:

Faster pruning and partition filtering
Smarter metadata caching
Reduced memory footprint in high-concurrency environments

These changes make querying large Iceberg tables as performant as native Snowflake tables — with the added flexibility of open format.

Implications for the Data Landscape

With these enhancements, Snowflake has made Iceberg a first-class citizen in its ecosystem. It supports:

Better interoperability
Cloud-native efficiency
Seamless user experience for BI and data engineering teams

This places significant pressure on Databricks and Delta Lake, as enterprises now have a true open alternative without sacrificing performance or usability.

The Biggest Challenge to Databricks Is Iceberg

Delta Lake has been Databricks’ moat. But:

Customers want multi-engine support
Teams demand neutral governance
Enterprises seek cloud-agnostic platforms

Snowflake, AWS, GCP, Cloudera, and others have embraced Iceberg. Even Databricks had to acknowledge this by announcing Iceberg support — a nod to the shifting tide.

The Future Is Open, Interoperable, and Iceberg-Centric

Apache Iceberg is doing to data lakes what:

Parquet did to storage formats
Kubernetes did to orchestration

It abstracts complexity, enables scale, and invites innovation.

TL;DR: Why Apache Iceberg Wins

✅ Full ACID, time travel, schema + partition evolution
✅ Works with Spark, Flink, Trino, Presto, Hive, Snowflake, Dremio
✅ Allows multi-writer concurrency
✅ Supports evolving metadata and partition strategies
✅ Vendor-neutral, cloud-native, and built for petabyte scale

Final Word

Apache Iceberg isn’t just a table format. It’s a philosophy shift.
A world where storgae and compute are truly decoupled, analytics are flexible, and formats are open by design.

If you’re not already using Iceberg — you’re building on yesterday’s architecture.