Moving from Delta Lake to Apache Iceberg: An Enterprise Interoperability Perspective

As enterprise data platforms grow increasingly complex, the choice of table format has evolved from a technical implementation detail to a strategic architectural decision. For CTOs and data leaders managing large-scale analytics environments, the ability to support multiple engines, avoid vendor lock-in, and enable seamless cross-platform collaboration has become paramount. This article examines why Apache Iceberg’s open, interoperable architecture is driving many enterprises to migrate from Delta Lake, and provides practical guidance for making this transition.

The Enterprise Interoperability Challenge#

In 2025, the notion of a single analytics platform serving all enterprise needs has largely proven unrealistic. Modern data organisations typically operate a heterogeneous ecosystem: Snowflake for business intelligence and data warehousing, Apache Spark for large-scale ETL, Apache Flink for real-time streaming, Trino or Presto for federated queries, and increasingly specialised engines for machine learning and AI workloads.

The fundamental question is not whether your organisation will use multiple engines—it’s whether your table format enables them to work together efficiently.

Delta Lake, whilst technically excellent within its primary ecosystem, was designed with Databricks and Spark at its core. Apache Iceberg, by contrast, was architected from the outset as a vendor-neutral, multi-engine table format. This architectural difference has profound implications for enterprise data strategy, particularly around:

• Strategic flexibility: The ability to adopt new technologies without replatforming data
• Vendor negotiation leverage: Avoiding lock-in strengthens commercial positioning
• Merger and acquisition integration: Common formats simplify data consolidation
• Cross-functional collaboration: Teams using different tools can share data seamlessly
• Future-proofing: Protection against platform obsolescence or vendor strategy changes

Why Table Format Choice Matters for Multi-Platform Strategy#

Table formats define how data is organised, accessed, and governed at the storage layer. They provide critical capabilities including ACID transactions, schema evolution, time travel, and partition management. However, not all table formats are equally accessible across different compute engines.

Delta Lake’s Ecosystem Position:

• Originated and primarily maintained by Databricks
• Excellent first-class support in Databricks and Apache Spark
• Growing support in other engines, but often through translation layers
• UniForm feature (2024) attempts to bridge compatibility gaps
• Governance features tightly integrated with Databricks Unity Catalog

Apache Iceberg’s Design Philosophy:

• Originally developed at Netflix, now an Apache Foundation project
• Designed explicitly for multi-engine interoperability
• Native support across diverse engines (Snowflake, Spark, Flink, Trino, Presto, Dremio, Athena, EMR)
• Vendor-neutral governance through open metadata specifications
• Active contribution from multiple vendors (no single controlling entity)

For enterprises managing data at scale, Iceberg’s architecture eliminates an entire class of integration problems that arise when trying to share Delta Lake tables across non-Databricks platforms.

Engine Support: The Interoperability Reality#

Let me be direct: as of mid-2025, the breadth and depth of engine support for Apache Iceberg significantly exceeds that of Delta Lake when you move beyond the Spark ecosystem.

Native Engine Support Comparison#

The pattern is clear: If your strategy involves multiple engines—particularly Snowflake, Flink, or federated query engines—Iceberg provides materially better interoperability.

Technical Interoperability Benefits#

Beyond simple engine support, Iceberg’s design delivers specific technical advantages for multi-platform environments.

1. Schema Evolution Without Breaking Consumers#

Iceberg’s approach to schema evolution is additive and backward-compatible by design. When you add columns, rename fields, or modify data types, different engines reading the same table can continue operating without coordination.

-- In Spark: Add new columns to existing Iceberg table
ALTER TABLE customer_events
ADD COLUMNS (
  customer_lifetime_value DECIMAL(10,2),
  risk_score INT
);

-- In Snowflake: Same table is immediately queryable with new schema
-- Older queries without new columns continue working
SELECT customer_id, event_type, event_timestamp
FROM iceberg_catalog.customer_events
WHERE event_date = '2025-07-25';

-- In Flink: Streaming job can reference new columns
-- Schema registry automatically updated
SELECT customer_id, customer_lifetime_value
FROM customer_events
WHERE customer_lifetime_value > 10000;

With Delta Lake, schema changes made in Databricks may require additional steps to reflect in external engines, particularly for operations beyond simple column additions.

2. Hidden Partitioning Across Engines#

Iceberg’s hidden partitioning feature is genuinely transformative for enterprise interoperability. Users query tables without partition awareness, whilst the engine automatically optimises reads.

-- Create Iceberg table with hidden partitioning
CREATE TABLE sales_transactions (
  transaction_id BIGINT,
  customer_id BIGINT,
  transaction_timestamp TIMESTAMP,
  amount DECIMAL(10,2),
  region STRING
)
USING iceberg
PARTITIONED BY (days(transaction_timestamp), region);

-- Users query without partition predicates
-- Iceberg automatically prunes partitions across any engine
SELECT customer_id, SUM(amount) as total_spend
FROM sales_transactions
WHERE transaction_timestamp >= '2025-07-01'
  AND region = 'EMEA'
GROUP BY customer_id;

This works identically in Spark, Snowflake, Flink, and Trino. Users don’t need to know partition schemes, and changing partitioning strategies doesn’t break queries. Delta Lake requires partition columns in predicates for optimal performance, creating cross-engine consistency challenges.

3. Time Travel and Versioning#

Both formats support time travel, but Iceberg’s metadata structure makes version history accessible across all engines without translation.

-- Snowflake: Query Iceberg table as of specific timestamp
SELECT * FROM sales_transactions
FOR SYSTEM_TIME AS OF TIMESTAMP '2025-07-20 14:30:00';

-- Spark: Same table, same capability
SELECT * FROM sales_transactions
VERSION AS OF 1234567890;

-- Trino: Consistent time travel semantics
SELECT * FROM iceberg.sales_transactions
FOR TIMESTAMP AS OF TIMESTAMP '2025-07-20 14:30:00';

4. Metadata Portability and Catalog Integration#

Iceberg’s REST catalog specification enables centralised metadata management across heterogeneous engines. A single catalog service can provide consistent views to Spark, Snowflake, Flink, and query engines simultaneously.

# Configure multiple engines to use shared Iceberg REST catalog
# Spark configuration
spark.sql.catalog.shared_catalog = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.shared_catalog.catalog-impl = org.apache.iceberg.rest.RESTCatalog
spark.sql.catalog.shared_catalog.uri = https://catalog.enterprise.example

# Snowflake external volume (Iceberg catalog integration)
CREATE EXTERNAL VOLUME iceberg_volume
  STORAGE_LOCATIONS = (
    (NAME = 's3_iceberg'
     STORAGE_PROVIDER = 'S3'
     STORAGE_BASE_URL = 's3://enterprise-data-lake/'
     CATALOG = 'ICEBERG_REST'
     CATALOG_URI = 'https://catalog.enterprise.example')
  );

This unified catalog approach is significantly more mature in Iceberg than Delta Lake, where Unity Catalog is Databricks-centric.

Migration Strategies: From Delta to Iceberg#

Migrating production data platforms requires careful planning. Here are proven patterns for transitioning from Delta Lake to Iceberg with minimal disruption.

Strategy 1: Dual-Write Pattern (Zero-Downtime Migration)#

For critical tables requiring continuous availability, implement dual-write to both formats during transition.

# PySpark: Dual-write pattern for migration
from pyspark.sql import SparkSession

def dual_write_migration(df, table_name):
    """
    Write data to both Delta and Iceberg formats
    Allows gradual consumer migration with zero downtime
    """

    # Write to existing Delta table
    df.write \
      .format("delta") \
      .mode("append") \
      .saveAsTable(f"delta_catalog.{table_name}")

    # Simultaneously write to new Iceberg table
    df.write \
      .format("iceberg") \
      .mode("append") \
      .saveAsTable(f"iceberg_catalog.{table_name}")

    # Optional: Validation checkpoint
    delta_count = spark.table(f"delta_catalog.{table_name}").count()
    iceberg_count = spark.table(f"iceberg_catalog.{table_name}").count()

    if delta_count != iceberg_count:
        raise Exception(f"Row count mismatch: Delta={delta_count}, Iceberg={iceberg_count}")

# Implementation in streaming pipeline
streaming_df = spark.readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "kafka:9092") \
  .load()

processed_df = streaming_df.selectExpr("CAST(value AS STRING)")

# Dual write during migration period
query = processed_df.writeStream \
  .foreachBatch(lambda batch_df, batch_id: dual_write_migration(batch_df, "customer_events")) \
  .start()

Migration Timeline:

1. Week 1-2: Implement dual-write for new data
2. Week 3-4: Historical backfill to Iceberg (parallel batch jobs)
3. Week 5-6: Validation across both formats
4. Week 7-8: Migrate consumers to Iceberg (gradual rollout)
5. Week 9: Deprecate Delta tables after validation period

Strategy 2: Batch Conversion with Iceberg Metadata#

For less critical tables, perform one-time conversion using Iceberg’s metadata-only migration capabilities.

# One-time Delta to Iceberg conversion
# Leverages Iceberg's ability to adopt existing Parquet files

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder \
  .appName("DeltaToIcebergMigration") \
  .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
  .getOrCreate()

def migrate_delta_table_to_iceberg(delta_table_path, iceberg_table_name):
    """
    Convert Delta table to Iceberg format
    Reads Delta, writes to Iceberg with schema and partitioning preserved
    """

    # Read Delta table
    delta_df = spark.read.format("delta").load(delta_table_path)

    # Capture Delta partitioning scheme
    delta_partition_cols = delta_df.schema.fieldNames()  # Adjust based on actual partitions

    # Write to Iceberg with equivalent partitioning
    delta_df.write \
      .format("iceberg") \
      .partitionBy("event_date", "region")  # Adjust to match Delta partitioning \
      .mode("overwrite") \
      .saveAsTable(iceberg_table_name)

    print(f"Migrated {delta_table_path} to {iceberg_table_name}")

    # Validation
    delta_count = spark.read.format("delta").load(delta_table_path).count()
    iceberg_count = spark.table(iceberg_table_name).count()

    assert delta_count == iceberg_count, f"Migration validation failed"

# Execute migration
migrate_delta_table_to_iceberg(
    delta_table_path="s3://data-lake/delta/sales_transactions",
    iceberg_table_name="iceberg_catalog.sales_transactions"
)

Strategy 3: Snowflake-Centric Migration#

For organisations with Snowflake as strategic platform, leverage Snowflake’s native Iceberg support.

-- Create Iceberg table in Snowflake from Delta Lake data
-- Step 1: Create external stage pointing to Delta Lake data
CREATE OR REPLACE STAGE delta_migration_stage
  URL = 's3://data-lake/delta/customer_data/'
  CREDENTIALS = (AWS_KEY_ID='...' AWS_SECRET_KEY='...');

-- Step 2: Create Iceberg table and load data
CREATE OR REPLACE ICEBERG TABLE customer_data_iceberg (
  customer_id NUMBER,
  customer_name STRING,
  email STRING,
  registration_date DATE,
  lifetime_value DECIMAL(10,2)
)
CATALOG = 'SNOWFLAKE'
EXTERNAL_VOLUME = 'iceberg_external_volume'
BASE_LOCATION = 'customer_data/';

-- Step 3: Load data from Delta stage (via Parquet)
-- Note: May require intermediate staging depending on Delta version
COPY INTO customer_data_iceberg
FROM @delta_migration_stage
FILE_FORMAT = (TYPE = PARQUET)
MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE;

-- Step 4: Enable time travel and validate
ALTER ICEBERG TABLE customer_data_iceberg
SET DATA_RETENTION_TIME_IN_DAYS = 90;

-- Validation query
SELECT COUNT(*), MIN(registration_date), MAX(registration_date)
FROM customer_data_iceberg;

Enterprise Architecture Advantages#

Beyond technical features, Iceberg enables architectural patterns that align with modern enterprise data strategy.

Data Mesh and Domain Ownership#

Iceberg’s vendor neutrality makes it ideal for data mesh architectures where different domains may choose different compute platforms.

Scenario: Financial services company with multiple domains:

• Risk Analytics Domain: Uses Spark for complex model training
• Customer Analytics Domain: Uses Snowflake for BI and reporting
• Real-Time Fraud Domain: Uses Flink for streaming detection
• Data Science Platform: Uses Trino for federated queries across domains

With Iceberg, each domain can optimise for its preferred engine whilst sharing data through a common format. Delta Lake would require standardising on Databricks or accepting interoperability friction.

Multi-Cloud and Hybrid Cloud Strategy#

Iceberg’s open specification and broad engine support simplify multi-cloud and hybrid deployments.

# Example: Multi-cloud Iceberg architecture
# Data stored in cloud-agnostic format

AWS_Environment:
  Storage: S3
  Compute:
    - EMR (Spark)
    - Athena (Iceberg native)
  Catalog: AWS Glue with Iceberg support

Azure_Environment:
  Storage: ADLS Gen2
  Compute:
    - Synapse Spark
    - Snowflake (Iceberg Tables)
  Catalog: Azure Purview with Iceberg metadata

On_Premises:
  Storage: HDFS / MinIO
  Compute:
    - On-prem Spark cluster
    - Trino for federated queries
  Catalog: Self-hosted Iceberg REST catalog

Iceberg tables can be accessed consistently across all environments. Delta Lake’s tighter Databricks integration creates friction in multi-cloud scenarios.

Total Cost of Ownership Implications#

Vendor neutrality has direct cost implications:

Licensing Flexibility: Not dependent on Databricks licensing for full feature access across platforms.

Compute Optimisation: Choose most cost-effective engine for each workload:

• Batch ETL: Spot instances on EMR or self-managed Spark
• Interactive queries: Snowflake or Athena (pay-per-query)
• Streaming: Managed Flink or self-hosted for cost control

Storage Efficiency: Iceberg’s metadata structure and snapshot management can reduce storage costs through efficient file pruning and compaction.

Reduced Integration Costs: Eliminating translation layers and dual-format maintenance reduces engineering overhead.

Real-World Enterprise Scenarios#

Scenario 1: Multi-Platform Analytics Environment#

Context: Global retailer with 50+ data engineering teams, £2B+ annual revenue.

Architecture:

• Snowflake: Primary platform for business intelligence (5,000+ users)
• Spark on Kubernetes: Large-scale ETL and ML training
• Flink: Real-time inventory and pricing optimisation
• Trino: Ad-hoc federated queries across data sources

Challenge with Delta Lake:

• Snowflake could only read Delta via external tables (limited functionality)
• Flink integration required custom connectors with lag
• Governance metadata split between Unity Catalog and Snowflake

Solution with Iceberg:

• Single source of truth for transactional data
• Snowflake Iceberg Tables for native read/write
• Flink streaming writes to same tables Snowflake queries
• Unified governance through shared Iceberg catalog
• 40% reduction in data duplication (no longer maintaining separate copies)

Scenario 2: Merger & Acquisition Integration#

Context: Private equity firm acquiring multiple companies, requiring rapid data consolidation.

Challenge:

• Acquired companies use diverse platforms (Databricks, Snowflake, on-prem Hadoop)
• Need unified analytics within 90 days of acquisition
• Cannot force platform standardisation immediately

Iceberg Advantage:

• Establish Iceberg as common format for core datasets
• Each acquired entity keeps preferred compute platform
• Centralised Iceberg catalog provides unified metadata
• Gradual platform rationalisation without data migration pressure
• Preserved investment in existing platform expertise

Timeline:

• Day 1-30: Deploy Iceberg catalog, migrate core datasets
• Day 31-60: Connect existing platforms to Iceberg tables
• Day 61-90: Unified reporting layer operational
• Post-90: Platform consolidation as business units integrate

Scenario 3: Cross-Team Collaboration Without Duplication#

Context: Financial services firm with separated analytics and data science teams.

Previous State (Delta Lake):

• Analytics team: Databricks with Delta Lake
• Data Science team: Snowflake for feature engineering
• Marketing team: AWS Athena for campaign analysis
• Result: Same data stored in three formats, consistency issues, 3-5 day data latency

Iceberg Solution:

• Single Iceberg table for customer transaction data
• Analytics team: Queries via Spark on Databricks
• Data Science team: Queries via Snowflake
• Marketing team: Queries via Athena
• Result: Single source of truth, real-time consistency, 70% storage reduction

Strategic Recommendations for CTOs and Data Leaders#

When to Choose Iceberg Over Delta Lake#

Choose Apache Iceberg if you:

• Operate or plan to operate a multi-engine analytics environment
• Use Snowflake as a strategic platform (Iceberg is Snowflake’s table format direction)
• Require streaming support with Flink or similar real-time engines
• Value vendor neutrality and want negotiation leverage
• Anticipate M&A activity requiring rapid data integration
• Operate across multiple clouds or hybrid environments
• Need federated query capabilities across diverse data sources

Delta Lake may be appropriate if you:

• Are deeply standardised on Databricks across all analytics workloads
• Have strong commercial relationship with Databricks with favourable pricing
• Require features specific to Databricks Unity Catalog
• Have limited multi-engine requirements in foreseeable future

However, note that as of mid-2025, even Databricks is adding Iceberg support, acknowledging market demand for interoperability.

Migration Planning Considerations#

Assessment Phase (4-6 weeks):

1. Inventory current Delta Lake tables by criticality and size
2. Map downstream consumers and their platforms
3. Identify tables accessed by multiple engines (prioritise for migration)
4. Evaluate governance and lineage dependencies
5. Conduct cost-benefit analysis (storage, compute, engineering time)

Pilot Phase (6-8 weeks):

1. Select 2-3 non-critical tables for proof-of-concept
2. Implement dual-write pattern
3. Migrate subset of consumers to Iceberg
4. Measure performance across target engines
5. Validate governance and cataloguing workflows
6. Document lessons learned

Production Migration (12-24 weeks, depending on scale):

1. Prioritise by business criticality and cross-platform usage
2. Use dual-write for high-availability tables
3. Batch conversion for historical or less-critical tables
4. Phased consumer migration with rollback capability
5. Continuous validation and monitoring
6. Gradual deprecation of Delta tables

Long-Term Platform Strategy#

2025-2027 Outlook: The industry is clearly moving towards open, interoperable table formats. Apache Iceberg has achieved critical mass with support from AWS, Google, Snowflake, and emerging engines. Delta Lake’s UniForm initiative acknowledges this trend but adds complexity rather than solving the fundamental interoperability gap.

Strategic Positioning:

• Establish Iceberg as standard for new analytical datasets
• Maintain optionality by avoiding deep dependencies on proprietary features
• Invest in metadata management through Iceberg REST catalog or compatible solutions
• Align with Snowflake strategy if Snowflake is part of your platform mix
• Plan for streaming growth with Flink-compatible infrastructure

Key Performance Indicators to Track:

• Percentage of tables accessible across multiple engines without translation
• Time to onboard new analytics platforms (reduction with Iceberg)
• Data duplication ratio (target <10% with Iceberg)
• Cross-platform query performance consistency
• Mean time to integrate acquired company data (M&A scenarios)

Conclusion#

The migration from Delta Lake to Apache Iceberg is not merely a technical upgrade—it’s a strategic investment in enterprise interoperability, vendor neutrality, and architectural flexibility. For organisations operating multi-engine analytics environments, particularly those with Snowflake, Flink, or federated query requirements, Iceberg delivers materially better interoperability than Delta Lake.

The fundamental question is simple: Do you want your table format to expand or constrain your future platform choices?

Iceberg’s vendor-neutral design, broad engine support, and mature metadata architecture make it the pragmatic choice for enterprises seeking to future-proof their data platforms. The migration requires planning and execution discipline, but the resulting flexibility—commercial, technical, and organisational—justifies the investment.

Key Takeaways#

• Interoperability is a strategic imperative: Multi-engine environments are the enterprise reality, not an edge case
• Iceberg provides materially better cross-platform support: Native integration with Snowflake, Flink, Trino, and emerging engines
• Vendor neutrality preserves optionality: Avoid lock-in whilst maintaining commercial leverage
• Migration is achievable with minimal disruption: Dual-write and phased migration patterns enable zero-downtime transitions
• Long-term TCO favours open formats: Reduced integration complexity, storage efficiency, and compute optimisation
• Industry momentum favours Iceberg: Broad vendor support and Apache governance reduce platform risk

Additional Resources#

• Apache Iceberg Documentation ↗ - Official specification and integration guides
• Snowflake Iceberg Tables ↗ - Snowflake’s native Iceberg implementation
• Netflix Iceberg: The Origin Story ↗ - Architectural decisions and design philosophy
• The Lakehouse Storage Layer ↗ - Databricks perspective on table formats
• Iceberg REST Catalog Specification ↗ - Metadata management integration

For enterprise data leaders, the choice is increasingly clear: Apache Iceberg represents the future of interoperable, vendor-neutral table formats. The question is not whether to adopt Iceberg, but when and how to execute the migration strategy that aligns with your organisation’s risk tolerance and platform roadmap.