Apache Iceberg vs. Delta Lake: Choosing Your Data Lakehouse Format

Modern data platforms face a fundamental challenge: how to combine the flexibility and cost-effectiveness of data lakes with the transactional reliability of data warehouses. This is where open table formats like Apache Iceberg and Delta Lake step in, transforming raw data stored in object storage into structured, queryable tables with ACID properties. They are the foundation of the burgeoning data lakehouse architecture, promising a unified approach to analytics.

Choosing between these two powerful formats is a critical decision for any data engineering team. This article dives deep into Apache Iceberg and Delta Lake, comparing their core architectures, feature sets, ecosystem compatibility, and operational considerations. By understanding their nuanced differences, you’ll be equipped to select the format that best aligns with your organization’s data strategy and technical stack.

The Foundation: Why Open Table Formats Matter for Data Lakehouses

For years, data teams were forced into a binary choice: the structured, reliable but often rigid world of data warehouses, or the scalable, flexible but often unreliable “data swamp” of data lakes. The lakehouse architecture emerged to resolve this tension, and open table formats are the technology that makes it possible.

Bridging the gap between data lakes and warehouses

Traditional data lakes, built on object stores like Amazon S3 or Google Cloud Storage, manage data as a collection of files. This approach makes it difficult to enforce data quality, handle concurrent operations, or update schemas without complex, error-prone data engineering.

Open table formats add a metadata layer on top of these files, defining what constitutes a “table.” This layer tracks the files belonging to a table at any given time, their schemas, and their partitioning schemes. This abstraction enables database-like features directly on cloud object storage:

• ACID Transactions: Ensures data integrity during concurrent reads and writes.
• Schema Evolution: Allows for safe modification of table structures over time.
• Time Travel: Enables querying historical versions of a table for audits or rollbacks.
• Performance Optimizations: Facilitates file compaction, data skipping, and other optimizations.

Apache Iceberg explained: A metadata-centric approach

Apache Iceberg, originally developed at Netflix and now an Apache Software Foundation project, is an open table format designed for huge analytic tables. Its core philosophy is to treat the table state as a collection of immutable metadata files. Each change to a table creates a new metadata file that points to the complete set of data files for that version. This snapshot-based approach decouples the table’s state from the physical data layout, providing strong guarantees and flexibility.

Delta Lake explained: A transaction log-driven format

Delta Lake was created by Databricks and is now an open-source project under the Linux Foundation. It enhances data lakes by bringing reliability through a transaction log. Every operation (like an insert, update, or delete) is recorded as an ordered, atomic commit in a JSON-based log stored alongside the data files. Query engines read this log to determine the current state of the table, ensuring a consistent and up-to-date view.

Iceberg vs. Delta Lake: A Direct Comparison of Core Capabilities

While both Iceberg and Delta Lake aim to solve similar problems, their design philosophies and implementations lead to important differences.

Comparison Table: Iceberg vs. Delta Lake

Architectural Philosophies and Underlying Mechanics

The fundamental difference between Iceberg and Delta Lake lies in how they track the state of a table.

How Iceberg’s abstract table layer ensures engine compatibility

Iceberg maintains a pointer to the current metadata file for a table. This file contains a manifest list, which in turn points to one or more manifest files. These manifest files list the actual data files (e.g., Parquet files) that make up the table, along with statistics for each file.

When a write operation occurs, Iceberg creates new data files and new manifest files. It then generates a new manifest list and a new top-level metadata file. The final step is an atomic swap of the pointer to this new metadata file. This design means that readers are never exposed to a partially written state. Because the table’s logical structure is entirely contained within its metadata, different query engines can interact with Iceberg tables without needing to understand the physical directory layout, leading to broader compatibility.

Delta Lake’s transaction log: Ensuring consistency and reliability

Delta Lake’s architecture revolves around the _delta_log directory. Every transaction that modifies a table is recorded as a new JSON file in this directory (e.g., 00000.json, 00001.json). Each file contains a set of actions, such as “add file” or “remove file.”

To get the current state of a table, a query engine reads the log sequentially, applying the actions to build an in-memory representation of the table’s file list. To improve performance, Delta Lake periodically creates checkpoint files in Parquet format that consolidate the transaction log up to a certain point. This log-based approach provides a clear, auditable history of every change made to the table.

Beyond table formats: Parquet, ORC, and Avro’s role

It’s crucial to distinguish between table formats and file formats. A file format like Parquet is a way to organize data within a single file, using columnar storage for efficient analytical queries. Table formats like Iceberg and Delta Lake are a higher level of abstraction. They manage a collection of files (which are often in Parquet format) and provide the metadata layer that enables ACID transactions, versioning, and schema evolution across that collection. Iceberg is designed to be file-format-agnostic, while Delta Lake is more tightly coupled with Parquet.

Transactional Guarantees and Data Management Features

Both formats offer a rich set of features for managing data reliably at scale.

Achieving ACID properties in distributed environments

Both Iceberg and Delta Lake use optimistic concurrency control to provide ACID guarantees. When multiple writers attempt to modify a table simultaneously, each one works on a new version. The first to commit succeeds. Subsequent writers detect that the table has changed and must retry their operation based on the new table state, ensuring that no updates are lost. Iceberg accomplishes this via an atomic swap of the metadata pointer in a catalog, while Delta Lake relies on the ability to atomically write to the transaction log.

Seamless schema evolution and schema enforcement

A major pain point in traditional data lakes is managing schema changes. Both Iceberg and Delta Lake solve this elegantly. You can add, remove, rename, or reorder columns without rewriting data. The schema is tracked in the metadata, so queries against older data use the old schema, while new data conforms to the new one. This prevents data corruption and eliminates the need for complex data migration jobs when business requirements change.

Time travel and versioning: Auditing and data recovery

The ability to access historical versions of a table is a transformative feature. In Delta Lake, you can query a specific version number or timestamp to see the state of the table at that point. Iceberg provides similar functionality through its snapshot system. This is invaluable for:

• Auditing: Verifying data at a specific point in time.
• Debugging: Reproducing ML models or analytics reports with the exact data they were built on.
• Recovery: Quickly rolling back a table to a previous state in case of a bad write or accidental deletion.

Change Data Capture (CDC) support and merge operations

Both formats support MERGE operations, which are essential for handling upserts and slowly changing dimensions in ETL/ELT workflows. Delta Lake has a mature Change Data Feed feature that explicitly records row-level changes (inserts, updates, deletes) in the transaction log, making it easier to build streaming applications or incremental data pipelines. Iceberg also supports CDC, with implementations varying slightly across different query engines.

Data compaction and file optimization strategies

Over time, data lakes can accumulate many small files, which hurts query performance. Both Iceberg and Delta Lake provide tools to compact these small files into larger, more efficient ones. Delta Lake uses the OPTIMIZE command, often used in conjunction with Z-ORDER indexing to co-locate related data. Iceberg has similar procedures for rewriting data files, with strategies to optimize for size and sorting.

Ecosystem Integrations, Performance, and Vendor Neutrality

The choice between Iceberg and Delta Lake often comes down to the tools and platforms you use.

Broad engine support: Spark, Flink, Trino, Presto, Dremio, Snowflake, Athena

This is where Iceberg’s vendor-neutral design shines. It has broad, first-class support across a wide range of query engines, including Spark, Flink, Trino, Presto, Dremio, and has growing native support in platforms like Snowflake and AWS Athena. This makes it a strong choice for organizations with a diverse or evolving data stack. You can write data with a Spark job and read it with a Trino query without any compatibility issues. You can even build a data lakehouse with Dremio and Kestra.

Delta Lake’s ecosystem is expanding, but its deepest integrations are with Apache Spark and Databricks. While connectors exist for other engines like Trino and Flink, they sometimes lag behind the core Spark implementation in features and performance.

Cloud platform native integrations (AWS, Azure, GCP)

Both formats work seamlessly on all major cloud object stores. Cloud providers are increasingly offering native support. AWS, for example, supports both formats in services like Athena, EMR, and Glue. Google Cloud offers connectors for services like Dataproc, and Azure integrates Delta Lake deeply into services like Synapse and Microsoft Fabric.

Open-source community strength and governance models

Iceberg is governed by the Apache Software Foundation (ASF), known for its strong emphasis on community over company and vendor neutrality. This model fosters broad adoption and contributions from many different organizations.

Delta Lake is a Linux Foundation project. While it has a growing community of contributors, its development is still heavily influenced by Databricks, which can be seen as either a benefit (strong corporate backing) or a risk (potential for vendor-specific optimizations).

Is Delta Lake only for Databricks?

No, Delta Lake is an open-source project that can be used outside of Databricks. However, Databricks has built a highly optimized and feature-rich platform around Delta Lake. Many of the most advanced features, performance enhancements (like Photon), and governance tools (like Unity Catalog) are part of the Databricks offering. For teams not invested in the Databricks ecosystem, using Delta Lake might mean forgoing some of these benefits or building equivalent tooling themselves.

Performance benchmarks and scalability considerations

Performance comparisons between Iceberg and Delta Lake are complex and often depend on the specific workload, query engine, and configuration. Generally, both formats perform exceptionally well for analytical queries. Delta Lake has historically had a performance edge in Spark-based environments due to its deep integration. Iceberg’s performance is highly competitive and often excels in multi-engine scenarios. The key takeaway is that both are designed for petabyte-scale tables and can deliver excellent performance when properly optimized.

Making Your Choice: When to Opt for Iceberg or Delta Lake

The right choice depends on your organization’s priorities, existing infrastructure, and long-term vision.

When Apache Iceberg is the superior choice

Apache Iceberg is the ideal choice for organizations that:

• Prioritize a multi-engine strategy: If your data needs to be accessible from Spark, Flink, Trino, and other engines, Iceberg’s interoperability is a major advantage.
• Value open governance and vendor neutrality: The ASF governance model ensures that the project’s direction is not tied to a single company’s commercial interests.
• Require partition evolution: Iceberg’s hidden partitioning is a powerful feature for tables whose partitioning scheme might change over time, as it avoids costly data rewrites.
• Are building a truly open data platform: If you want to avoid any form of lock-in and maintain maximum flexibility, Iceberg is the safer long-term bet.

When Delta Lake provides distinct advantages

Delta Lake is a strong contender, particularly for teams that:

• Are heavily invested in the Databricks or Spark ecosystem: The deep integration, performance optimizations, and rich feature set within Databricks are compelling. It’s often the default choice for Databricks alternatives that are still Spark-centric.
• Need mature CDC and MERGE capabilities: Delta Lake’s Change Data Feed is a well-established feature for building incremental pipelines.
• Prefer the simplicity of a transaction log model: For some teams, the chronological log is an intuitive way to reason about table history.
• Are building on platforms like Microsoft Fabric that have chosen Delta Lake as their standard.

Which is better, Apache Iceberg or Delta Lake?

Neither format is objectively “better.” The best choice is contextual.

• Choose Iceberg for openness, multi-engine support, and future-proofing against vendor lock-in.
• Choose Delta Lake for a seamless, optimized experience within the Databricks and Spark ecosystems.

Hybrid approaches and future trends in table formats

The competition between Iceberg, Delta Lake, and the third major format, Apache Hudi, is driving innovation across the ecosystem. Projects like the UniForm initiative (part of Delta Lake) aim to provide interoperability by allowing Delta Lake tables to be read as if they were Iceberg or Hudi tables. This signals a future where the lines may blur, but for now, the architectural differences remain a critical decision point.

Orchestrating Your Data Lakehouse Workflows with Kestra

Regardless of your choice, managing data pipelines in a lakehouse requires robust orchestration. Kestra is a language-agnostic, declarative orchestration platform that can manage complex workflows across both Iceberg and Delta Lake environments.

With Kestra, you can build event-driven pipelines that trigger on new data arrival, run data quality checks, execute Spark or dbt transformations, and update tables in your lakehouse. Its declarative YAML interface makes it easy for teams to define, version, and manage data workflows as code.

For example, you could create a Kestra flow that triggers a Databricks job to update a Delta Lake table, then runs an Athena query against an Iceberg table, and finally sends a notification to Slack—all in one auditable workflow.

id: databricks_delta_to_athena_iceberg
namespace: io.kestra.lakehouse.etl

tasks:
  - id: start_databricks_job
    type: io.kestra.plugin.databricks.job.SubmitRun
    authentication:
      token: "{{ secret('DATABRICKS_TOKEN') }}"
    host: "{{ secret('DATABRICKS_HOST') }}"
    tasks:
      - taskKey: "update_delta_table"
        newCluster:
          sparkVersion: "13.3.x-scala2.12"
          nodeTypeId: "i3.xlarge"
          numWorkers: 2
        notebookTask:
          notebookPath: "/Users/alex@kestra.io/UpdateFactTable"

  - id: query_iceberg_table
    type: io.kestra.plugin.aws.athena.Query
    region: "us-east-1"
    accessKeyId: "{{ secret('AWS_ACCESS_KEY_ID') }}"
    secretKeyId: "{{ secret('AWS_SECRET_ACCESS_KEY') }}"
    database: "analytics"
    query: |
      SELECT
        source_system,
        COUNT(1) as event_count
      FROM iceberg_events
      WHERE event_date = CURRENT_DATE
      GROUP BY 1;
    store: true

  - id: notify_on_slack
    type: io.kestra.plugin.notifications.slack.SlackIncomingWebhook
    url: "{{ secret('SLACK_WEBHOOK') }}"
    payload: |
      {
        "text": "Lakehouse ETL complete. Databricks job `{{ outputs.start_databricks_job.runId }}` finished and Athena query on Iceberg table succeeded."
      }

This example shows how Kestra can serve as a unified control plane for your entire data platform, seamlessly integrating tools like Databricks and Athena to manage workflows across different table formats. You can find more examples for querying Snowflake or Databricks SQL in our Blueprints library. To explore more resources, check out our data engineering guides.