Apache Iceberg vs Delta Lake: Key Features, Differences, and Use Cases Compared

The need for flexible and more cost-efficient data management solutions is constantly increasing. This demand has fueled a significant investment in data lakehouses—a modern data architecture that combines the scalability of data lakes and the reliability of data warehouses.

With features like unified storage and real-time processing and analytics support, data lakes are quickly becoming essential. The global data lakehouse market was valued at approximately USD 8.9 billion in 2023 and is expected to grow around 22.9% CAGR, reaching 66.4 billion by 2033. This growth shows the increasing demand for scalable and flexible data management solutions.

Apache Iceberg and Delta Lake are two prominent data lakehouse solutions. These data lakehouses provide advanced features and distinct advantages yet differ in their approaches and use cases. This article will compare the core aspects of Apache Iceberg vs Delta Lake, explaining their features and specific applications.

What are Data Lakehouse Table Formats?

In a data lakehouse architecture, table formats refer to the data layout and structure that defines how data is stored, managed, and accessed. These protocols help you organize and maintain data in tabular format, facilitating easy analysis.

The table formats provide several optimizations, including indexing, versioning, schema evolution, clustering, compacting, Z-ordering, and more. These capabilities enable data lakehouses to enhance query performance and support complex analytical workloads. 

For example:

• Indexing and clustering speed up data retrieval.
  
• Compacting and partitioning reduce storage costs and enhance data organization. 

By supporting data management practices and optimizing query performance, table formats enable your organization to get more value from its data.

Apache Iceberg Overview

Apache Iceberg is an open-source table format optimized for extensive analytics datasets. Combined with Iceberg, you can analyze data at scale using compute engines like Spark, Flink, PrestoDB, or Trino.

The Apache Iceberg table is made of three different layers:

• The data layer is responsible for storing the actual data files. It is backed by a distributed file system like HDFS or cloud object storage like S3, making it highly scalable and cheap.
  
• The metadata layer consists of metadata files for the Iceberg table. It has a tree-like structure that helps you track data files and metadata related to them. The metadata layer comprises three file types: manifest files, manifest lists, and metadata files.
  
• The catalog layer consists of a catalog service, which connects the metadata layer with the data layer, completing the stack.

Key Features of Apache Iceberg 

• Schema Evolution: Apache Iceberg supports in-place table evolution, enabling you to modify table structures, including nested structures, just like SQL. It supports table schema modifications, such as adding or removing columns, renaming, updating, and reordering columns without disrupting the existing data.
  
• Partitioning: Iceberg handles the task of producing partitions for rows in a table. It simplifies data access by automatically creating and managing partition values. Iceberg helps optimize queries by skipping irrelevant partitions and allows flexible, evolving partition layouts without disrupting existing queries.
  
• Optimistic Concurrency: Apache Iceberg allows multiple users to write to the tables simultaneously through optimistic concurrency. Each writer operates independently, assuming no other writers are writing, and creates a new table metadata. When a writer tries to commit, Iceberg uses an atomic swap to replace the old metadata file with the new one. If any other writer has already committed changes, the swap will fail, prompting the writer to update the data to the latest state and try again.
  
• Scan Planning: It is locating the file in the table required for a query. Iceberg uses metadata to filter out irrelevant data. This process is implemented using a single node, which reduces latency by avoiding a fully distributed scan just to plan a query. As a result, queries run faster, and any client applications can efficiently read data directly from Iceberg tables.

Learn the best practices and techniques for loading data into Apache Iceberg tables: Loading Data into Apache Iceberg.

Delta Lake Overview 

Delta Lake is a table format designed to be deployed on top of an existing data lake, improving its reliability, scalability, and performance. This optimizes the data lakes for large-scale analytics and real-time data applications. Delta Lake works ideally with compute engines like Apache Spark and integrates easily into big data workflows.

The Delta Lake architecture consists of three essential layers:

• The Delta Table is a transactional table used for large-scale data processing. This table stores the data in a columnar format. It supports schema enforcement and evolution, enabling efficient querying and helping maintain consistency.
  
• The second component is Delta Log. It is a transaction log consisting of records of every operation performed on the Delta Table. The Delta Log supports data versioning and rollbacks, enabling you to restore previous table states.
  
• The third component is the storage layer, where the data is stored. It supports object storage, providing scalability for the data stored within the table.

Key Features of Delta Lake

• ACID Transactions: Delta Lake supports ACID transactions, which help you manage big data workloads. It ensures data integrity through a transactional log that records all data changes, supporting complex operations. This improves data reliability and consistency.
  
• Schema Enforcement: The schema enforcement functionality of Delta Lake ensures that all the data matches the defined schema. It automatically rejects any incorrect or incomplete entries.
  
• Time Travel: The transaction log within Delta Lake has a master record that tracks every change made in the data. This data versioning helps you recreate past datasets, simplifying historical insights and data recovery.
  
• DML Operations: Delta Lake supports various DML operations, including updates, merges, and deletes. These operations help you manage complex data easily, particularly for cases like streaming data integration, managing slowly changing dimensions, or implementing CDC.

Comparing Apache Iceberg vs Delta Lake

Key Differences: Apache Iceberg vs Delta Lake

Let’s look at the detailed explanation of the features listed in the above table:

Metadata Management

Iceberg uses manifest files to manage metadata. These files serve as an inventory for all data files in a table, detailing file locations, partition data, and statistical metrics. Iceberg also maintains a snapshot log, a metadata log showcasing how a table’s current snapshot has changed over time. This log is a list of timestamps and ID pairs. The information in the manifest file is used to prune unnecessary splits during scan planning.

On the other hand, Delta Lake stores data in the Parquet files, and metadata is stored in the transaction log (DeltaLog). This log stores basic metadata, such as unique IDs, names, descriptions, partitions, and creation timestamps about the table. Such metadata facilitates transactional consistency and rollback capabilities.

Schema Evolution

Iceberg supports flexible schema evolution, enabling you to modify the table schema without rewriting the entire table. This feature works well with nested structures, making Iceberg adaptable to changing business needs.

Delta Lake also supports schema evolution but is limited compared to Iceberg. The schema changes in Delta Lake require validation against the predefined schema when any new data is written in the table. This helps maintain consistency and prevent data corruption.

Data Consistency and Reliability 

Apache Iceberg and Delta Lake use ACID transactions and data versioning to ensure data consistency. The main difference is that Iceberg employs a merge-on-read strategy, where changes recorded in a deleted file are applied when data is read, enabling deferred processing and faster writes. Delta Lake uses a merge-on-write strategy where changes are processed during write operations. This approach results in faster read time but can slow write operations.

Integration Capabilities

Iceberg is compatible with various data processing engines such as Apache Spark, Flink, and Hive. It also supports multiple data formats, including Avro, ORC, and Parquet. This flexibility makes Iceberg versatile for diverse data ecosystems.

Delta Lake, however, is deeply integrated with Apache Spark and Databricks ecosystem. It only supports the Parquet format. The tight coupling with Spark provides impressive performance for Spark-based workloads but limits compatibility with other compute engines compared to Iceberg. 

Cost Considerations

Apache Iceberg is entirely open source and does not support any specific vendor, reducing the risk of vendor lock-in. The only potential costs related to Iceberg depend on cloud storage and computing resources used.

The cost of Delta Lake depends on aspects like computing function, region, and cloud provider. For instance, on AWS, the cost of Delta Live Tables can range from $0.20/dbu for DLT Core Compute Photon to $0.36/dbu for DLT Advanced Compute.

Use Cases: Apache Iceberg vs Delta Lake

Apache Iceberg and Delta Lake are prominent and widely used data lakehouse solutions. Here are some of the use case scenarios highlighting which is ideal to use where:

When to Use Apache Iceberg

• For Cloud-Native Data Lakes: You can optimize Iceberg to build cloud-native data lakes, as it allows modifications to table data without affecting ongoing queries. It also helps efficiently prune data, reducing document scan times, and can easily manage billions of records and petabytes of data.
  
• Building Complex Data Models: Iceberg is particularly useful for creating complex data models as it supports nested data types and complex relationships. It also helps you reconstruct the state of your data at any given point by optimizing its snapshot logs.

When to Use Delta Lake

• High-Performance Data Warehousing: Delta Lake is well-suited for environments that need high-performance, consistent, and cost-effective data warehouse layers on cloud storage. It offers faster data access because of the pre-merged data structure and data versioning through Delta Log. This makes data easily accessible for audit and historical analysis.
  
• For Data Engineering on Databricks and Spark: Delta Lake is a top choice for data engineering tasks when your organization is heavily integrated with Databricks and Spark. Its deep integration with these platforms enables efficient data processing and compatibility with various Spark APIs.

How to Choose the Right Solution for Your Data Lakehouse

When choosing a data lakehouse solution between Delta Lake and Iceberg, you should consider the following points:

• Scalability: Evaluate how well the solution can handle your growing data volumes. Apache Iceberg is built for cloud-native scalability. It can help you easily handle petabytes of data. Delta Lake also supports petabyte-scale tables, which allow you to manage billions of files efficiently.
  
• Data Versioning: Data versioning is essential because it helps you manage data evolution, error recovery, and maintaining historical records. Both Apache Iceberg and Delta Lake offer data versioning capabilities.
  
• Analytics and Querying Capabilities: The data lakehouse solution must have robust query optimization, SQL compatibility, and real-time analytics support to help you handle large datasets efficiently. Apache Iceberg's advanced filtering and optimistic concurrency suit complex queries. On the other hand, Delta Lake features like compaction and clustering enhance query performance.
  
• Data Governance and Compliance: Governance tools help manage data access, security, and lineage. Ensure the solution offers robust data governance features to meet regulatory compliance. Apache Iceberg and Delta Lake support governance frameworks that help you manage access controls.

How to Easily Integrate Data Within a Data Lakehouse Using Estuary Flow

A data lakehouse solution such as Apache Iceberg or Delta Lake requires a streamlined approach to data integration, where real-time and batch data flow are synchronized. Estuary Flow is a real-time ETL platform that simplifies this integration process, enabling smooth data transfer between sources and destinations. Here’s how Estuary Flow can enhance data lakehouse integration: 

• Pre-Built Connectors: Estuary Flow has a library of 200+ pre-built connectors consisting of databases, data lakehouses, SaaS applications, and APIs. You can utilize these no-code connectors and build a data pipeline to transfer data between source and destination within minutes.
  
• Schema Evolution: Schema evolution allows you to update the whole data flow, reflecting the changes made to your data collections at the destination. It helps maintain data accuracy throughout the migration process.
  
• Flexible Deployment: Estuary offers three deployment options to cater to your organization's needs. The first is public deployment, which is a standard SaaS offering that is ideal for mid-size businesses. Second is Private deployment, where you can run Estuary within your organization’s private infrastructure. The third option is BYOC, where you can run Estuary on your cloud environment.

To get an idea of how to streamline your real-time data using Estuary, refer to this tutorial: PostgreSQL to Apache Iceberg, Streamlining Data Lakehouse Foundation.

While this article focuses on Apache Iceberg and Delta Lake, you may also want to explore how Apache Iceberg compares to Apache Hudi in terms of features and use cases: Apache Iceberg vs Apache Hudi

Conclusion 

The choice between Apache Iceberg and Delta Lake mainly depends on factors like your organization's data environment, integration needs, and performance requirements.

Apache Iceberg provides flexibility in data formats, accommodating varied storage needs. On the other hand, Delta Lake provides faster read times, which is helpful for real-time data workflows.

Ultimately, Apache Iceberg and Delta Lake bring distinct advantages, and you can opt for one that best suits your operational needs.

Are you seeking an efficient data integration platform to move data into Iceberg or another destination? Register for your Estuary account to get started right away!

FAQs

Is Delta Lake the Same as Iceberg? 

No, Delta Lake and Apache Iceberg are not the same. However, they both enhance the capabilities of data lakes with features like ACID transactions, data versioning, and schema evolution. 

What is Apache Iceberg Used for? 

Apache Iceberg is used to create scalable storage solutions and enable efficient analysis of large datasets. While data engineers build robust data architectures, analysts use it for advanced data querying.

Is Delta Lake a Part of Databricks? 

Databricks initially developed Delta Lake, but it is now an open-source project. While Databricks actively supports Delta Lake, it is available for use beyond the Databricks platform.