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Data lakes were introduced to offer a flexible, scalable, and cost-effective way to store vast amounts of raw data in various formats. However, while traditional databases ensure strong data reliability through ACID properties, Atomicity, Consistency, Isolation, Durability, data lakes lacked those fundamental guarantees for a long time. This led to serious reliability and data integrity issues, especially in high-scale, multi-writer environments where multiple pipelines or teams read and write simultaneously.
As modern use cases demand more from storage, such as streaming ingestion, real-time analytics, data versioning, and machine learning on production data, developers and data engineers need robust ACID-compliant systems to manage consistency and prevent corruption. This is where Apache Iceberg, a high-performance open table format for huge analytic datasets, comes in. By introducing a metadata-driven model, Apache Iceberg brings full ACID compliance to your data lakes, transforming them into transactional data lakehouses.
This blog explores how Apache Iceberg provides these guarantees, why it matters for developers building reliable, scalable data infrastructure, and how it compares with other approaches.
ACID stands for Atomicity, Consistency, Isolation, and Durability, four foundational properties that ensure reliable transactions in relational databases:
These properties are essential for applications that rely on trustworthy and synchronized data, such as financial systems, real-time analytics, and ML pipelines.
Until recently, data lakes built on raw storage formats like Parquet or ORC, orchestrated by Hive, lacked these guarantees. Developers had to deal with:
In essence, data lakes became fragile, often requiring extensive operational overhead to maintain reliability.
Apache Iceberg is an open-source high-performance table format developed by Netflix and later donated to the Apache Software Foundation. It was designed to solve the shortcomings of Hive-based data lakes by introducing a transactional metadata layer on top of columnar file formats.
Instead of relying on the directory structure and file naming conventions (as in Hive), Apache Iceberg maintains versioned metadata trees that describe the complete state of the dataset at any point in time. This shift in architecture allows Iceberg to deliver reliable ACID transactions, schema evolution, partition evolution, time travel, and engine independence.
Developers gain the benefits of traditional databases with the scale and flexibility of cloud-native object storage.
Apache Iceberg offers:
These features empower developers to build robust pipelines, automate CI/CD-style data workflows, and unlock next-level agility in managing analytical datasets.
Apache Iceberg achieves ACID guarantees by separating the logical state of a dataset from its physical data files. This is done through multiple layers of metadata:
This architecture allows atomic replacement of the pointer to the latest metadata file. If the operation fails before updating the pointer, readers still see the old state.
Iceberg uses an atomic compare-and-swap model. When a write job wants to commit changes:
If the metadata pointer was changed in the meantime (i.e., another job committed), the write fails and retries. This method avoids global locks and ensures consistent views of data without downtime or interference.
Readers always access a specific snapshot, ensuring isolation. Since snapshots are immutable and versioned, readers don’t experience partial changes, even if a concurrent job is writing. This level of snapshot isolation is ideal for analytics and concurrent ETL workloads.
Apache Iceberg can be used with multiple compute engines such as Apache Spark, Trino, Presto, Hive, Flink, and Dremio. Developers can plug in a variety of catalog backends, Hive Metastore, AWS Glue, REST, Nessie, for managing metadata locations.
In Spark SQL:
CREATE CATALOG iceberg_catalog
USING 'org.apache.iceberg.spark.SparkCatalog'
OPTIONS (
'type' = 'hive',
'uri' = 'thrift://localhost:9083',
'warehouse' = 's3://my-data-lake/warehouse'
);
Once set up, developers can perform transactions like inserts, updates, and deletes directly through SQL or DataFrame APIs:
df.write \
.format("iceberg") \
.mode("append") \
.saveAsTable("iceberg_catalog.db.users")
Updates and deletes:
UPDATE iceberg_catalog.db.users
SET email = 'new@example.com'
WHERE id = 123;
DELETE FROM iceberg_catalog.db.users
WHERE last_login < '2023-01-01';
These operations generate new manifests and snapshots without affecting active readers.
With snapshot-based isolation, developers can build reliable batch and streaming pipelines without worrying about race conditions or data corruption. Recovery is simple, roll back to a previous snapshot and try again.
Apache Iceberg supports:
This allows developers to adapt their models without data migration or reprocessing.
Time travel is a native feature. Developers can query data as it existed at a specific point in time or snapshot ID:
SELECT * FROM users.snapshot_id(123456789)
Ideal for:
Netflix uses Iceberg to manage thousands of petabyte-scale tables with strong ACID guarantees, empowering everything from data exploration to ML training.
Airbnb reduced ingestion latency and compute costs significantly by adopting Iceberg, improving data freshness and consistency across the company.
Companies like Apple, Adobe, LinkedIn, Expedia, and Stripe use Apache Iceberg for managing complex, large-scale analytical datasets reliably.
Apache Iceberg bridges the gap between raw data lakes and transactional databases, offering a robust, scalable, and developer-friendly approach to managing big data. With full support for ACID transactions, schema evolution, and time travel, Iceberg transforms how teams approach data engineering and analytics.
For developers building the next generation of data platforms, whether it's a real-time feature store, ML model tracking, or enterprise reporting, Apache Iceberg is an essential building block.
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