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Apache Iceberg FAQs

What is Apache Iceberg?

Apache Iceberg is an open-source data table format and management system designed to simplify and enhance the way organizations store, manage, and query large volumes of structured data in distributed data lakes or cloud storage environments. It offers features like schema evolution, transaction support, metadata management, and query optimization, making it an excellent choice for organizations dealing with large-scale, evolving datasets. Its open-source nature ensures that it remains a flexible and evolving tool for data management in the modern data ecosystem.

What is the history of Apache Iceberg?

The history of Apache Iceberg is a testament to the need for a versatile, open-source data table format and management system that addresses the evolving challenges of big data processing. Apache Iceberg emerged as a response to the complexities associated with managing structured data in large-scale distributed storage environments. 

Here is an overview of the history and evolution of Apache Iceberg:

2018: Inception and origin:

  • The story of Apache Iceberg begins with the need for a more efficient and flexible data management system for Apache Hive, a popular data warehousing and SQL query tool in the Hadoop ecosystem.
  • In 2018, engineers at Netflix recognized the limitations of existing data management solutions, particularly regarding schema evolution, data integrity, and performance. This realization prompted the inception of the Iceberg project.

2019: Open-source contribution:

  • In February 2019, Netflix open-sourced the Iceberg project, making it available to the broader community under the Apache License 2.0.
  • The project aimed to provide a schema evolution mechanism that wouldn't require expensive and disruptive table rewrites, making it suitable for real-world, evolving data workloads.

2019-2020: Gradual maturation:

  • Over the course of 2019 and 2020, the Apache Iceberg community grew, and contributions from various organizations began to pour in.
  • The project focused on improving the stability, performance, and feature set of Iceberg, making it a viable choice for production workloads.

2020: Apache incubation:

  • In July 2020, Apache Iceberg entered the Apache Incubator, the first step towards becoming a top-level Apache Software Foundation (ASF) project.
  • This move underscored the project's commitment to open-source collaboration and its desire to build a diverse and sustainable community.

2021: top-level project:

  • In January 2021, Apache Iceberg graduated from the Apache Incubator to become a top-level Apache Software Foundation project.
  • This achievement marked a significant milestone for Iceberg, indicating its maturity, stability, and broad community support.

2021 and beyond: Ongoing development:

  • Since becoming a top-level project, Apache Iceberg has continued to evolve and thrive. The community has actively worked on enhancing features, improving compatibility with various data processing frameworks, and addressing user feedback.
  • New releases have introduced important capabilities like transaction support, time-travel queries, and optimized data pruning.

What are the key features of Apache Iceberg?

Schema evolution:

  • Apache Iceberg enables seamless schema evolution, allowing you to modify data schemas without disrupting existing data or requiring costly data migrations.
  • It supports adding, dropping, and renaming columns, as well as changing data types and nested structures within the schema.

Transaction support:

  • Iceberg provides ACID transaction support, ensuring data consistency and reliability in multi-user and concurrent write scenarios.
  • Transactions can span multiple operations, ensuring that modifications are atomic and durable.

Metadata management:

  • Apache Iceberg maintains detailed metadata about tables, including schema information, data file locations, and historical snapshots.
  • This metadata is crucial for query optimization, data lineage, and auditing purposes.

Time-travel and snapshots:

  • Iceberg allows you to create snapshots of tables at specific points in time, facilitating time-travel queries.
  • Historical data versions can be easily accessed, providing a comprehensive view of data changes over time.

Data partitioning:

  • It supports efficient data partitioning, allowing you to organize data based on specific criteria (e.g., date, category).
  • Partitioning enhances query performance by reducing the amount of data that needs to be scanned.

Optimized data pruning:

  • Apache Iceberg optimizes data pruning during query execution, minimizing data scan costs and improving query performance.
  • It intelligently skips irrelevant data files based on query predicates.


  • Iceberg is compatible with popular data processing frameworks and query engines, including Apache Spark, Apache Hive, and Presto.
  • This compatibility ensures easy integration into existing data pipelines and workflows.

Cloud-native support:

  • It is well-suited for cloud-based storage systems like Amazon S3, Google Cloud Storage, and Azure Data Lake Storage.
  • Iceberg leverages cloud-native features like object versioning and storage tiering.

Open source community:

  • As part of the Apache Software Foundation, Apache Iceberg benefits from a vibrant open-source community that continuously enhances and extends its capabilities.
  • Users can leverage community-contributed extensions and plugins to customize Iceberg for specific use cases.

What are the requirements for Apache Iceberg?

Apache Iceberg is a versatile data management system designed to work in diverse environments, and its requirements encompass a combination of technical, software, and infrastructure prerequisites. 

To successfully deploy and use Apache Iceberg, here are the key requirements:

Java Runtime Environment (JRE):

  • Apache Iceberg is built in Java, so a compatible Java Runtime Environment (JRE) is essential. Typically, it is recommended to use Java 8 or higher.

Storage system:

  • Apache Iceberg supports various storage systems, including distributed file systems and cloud object storage services. Common choices include Hadoop Distributed File System (HDFS), Amazon S3, Google Cloud Storage, and Azure Data Lake Storage.

Distributed processing framework:

  • Iceberg integrates with distributed data processing frameworks like Apache Spark, Apache Hive, and Presto. You'll need to have one of these frameworks installed and configured to work with Iceberg.

Compatible version of Iceberg:

  • Ensure that you use a compatible version of Apache Iceberg that aligns with your chosen storage system and processing framework. Compatibility information can be found in the Iceberg documentation.

Data serialization libraries:

  • Iceberg supports various data serialization formats like Apache Parquet and Apache Avro. You should have the necessary libraries for these formats installed.

Dependency management:

  • Managing dependencies for Iceberg and its integrations is crucial. Utilize dependency management tools like Apache Maven or Apache Gradle to handle library dependencies.


  • Configure Apache Iceberg based on your specific use case and deployment environment. This includes specifying storage locations, metadata storage settings, and integration configurations.

Resource allocation:

  • Allocate sufficient computing resources (CPU, memory, and storage) to accommodate your data processing needs. The specific resource requirements depend on the size and complexity of your datasets and the query workloads.

Network connectivity:

  • Ensure that network connectivity is available between the nodes or clusters where Apache Iceberg is deployed, the storage system, and any distributed processing frameworks being used.

Security considerations:

  • Implement appropriate security measures, including access controls and authentication mechanisms, to safeguard your data and Apache Iceberg infrastructure.

Monitoring and logging:

  • Set up monitoring and logging mechanisms to track the performance and health of your Iceberg deployment. Tools like Apache Hadoop Metrics2 and centralized logging systems can be valuable.

Backups and disaster recovery:

  • Establish backup and disaster recovery strategies to prevent data loss and ensure data availability in case of unexpected failures.

Documentation and knowledge:

  • Ensure that your team has access to relevant documentation and training resources to effectively deploy, configure, and maintain Apache Iceberg.

What industries should be leveraging Apache Iceberg?

Apache Iceberg is a versatile data management system that offers benefits across a wide range of industries and is particularly valuable for organizations dealing with large volumes of structured data and facing challenges related to data evolution, schema management, and query performance. 

Here are some key industries that can benefit from leveraging Apache Iceberg:

Financial services:

  • Financial institutions can use Apache Iceberg to manage and analyze vast amounts of financial data, including transaction records, customer data, and market data.
  • Iceberg's support for schema evolution and ACID transactions ensures data integrity and allows for seamless adaptation to changing regulatory requirements.

Retail and e-commerce:

  • Retailers can employ Iceberg to handle customer transaction data, inventory management, and sales analytics.
  • Efficient data partitioning and optimized data pruning contribute to faster insights and improved inventory forecasting.


  • Healthcare organizations and research institutions can benefit from Iceberg's capabilities for managing patient records, clinical trial data, and genomics data.
  • Data partitioning and time-travel features aid in tracking patient histories and conducting research.


  • Telecom companies can leverage Iceberg for managing call detail records, network performance data, and customer profiles.
  • The ability to evolve data schemas without disruptions and optimize data storage is essential in this industry.

Media and entertainment:

  • Media companies dealing with vast libraries of content, user engagement data, and advertising analytics can use Iceberg to streamline data management and analysis.
  • Time-travel and snapshot capabilities assist in content versioning and historical analysis.

Energy and utilities:

  • The energy sector can benefit from Iceberg when managing data related to grid operations, energy consumption, and equipment maintenance.
  • Efficient data partitioning and pruning can improve decision-making in energy distribution and infrastructure management.


  • Manufacturing companies can use Iceberg to manage production data, quality control metrics, and supply chain information
  • Schema evolution supports changes in product specifications and production processes.

Transportation and logistics:

  • In the transportation and logistics industry, Iceberg can help manage data related to route optimization, fleet management, and shipment tracking.
  • Data partitioning assists in analyzing transportation routes and improving operational efficiency.

Government and public sector:

  • Government agencies can utilize Iceberg for managing diverse datasets, including census data, public health records, and regulatory information.
  • The system's ability to maintain data integrity and track changes is valuable for transparency and compliance.

Technology and software development:

  • Technology companies dealing with large volumes of user and performance data can benefit from Iceberg's capabilities.
  • Compatibility with popular data processing frameworks simplifies data pipeline development.

How can businesses define the successful use of Apache Iceberg?

Defining the successful use of Apache Iceberg in a business context involves setting clear objectives and achieving tangible outcomes that contribute to improved data management, analytics, and overall operational efficiency. 

Here are key steps and criteria for businesses to consider when gauging the successful use of Apache Iceberg:

Clear business goals:

  • Begin by identifying specific business objectives that Apache Iceberg should help achieve. These goals could include improving data reliability, enhancing query performance, reducing operational costs, or facilitating data-driven decision-making.

Data integrity and consistency:

  • A successful use of Apache Iceberg should ensure data integrity and consistency. This means that data is accurate, reliable, and meets defined quality standards.
  • Verify that schema evolution is handled without compromising data quality and that ACID transactions are effectively maintaining data consistency.

Query performance improvement:

  • Measure the impact of Iceberg on query performance. Successful implementation should result in faster query execution times, which translates to quicker access to insights.
  • Analyze query execution plans and query performance metrics to assess improvements.

Cost efficiency:

  • Evaluate cost savings achieved through optimized data storage and query processing. Successful use of Iceberg should contribute to cost efficiency by minimizing data scan and storage costs.
  • Compare the cost of data operations before and after Iceberg implementation.

Scalability and adaptability:

  • Assess how well Apache Iceberg scales with growing data volumes and evolving data requirements. It should adapt smoothly to changing business needs without significant disruptions.
  • Successful use includes the ability to handle increased data loads and evolving data schemas seamlessly.

Data lineage and auditing:

  • Verify that Apache Iceberg enables effective data lineage tracking and auditing. Businesses should be able to trace data changes over time, facilitating compliance and data governance.
  • Ensure that historical snapshots and time-travel features are utilized for auditing purposes.

Compatibility with existing tools:

  • The successful use of Iceberg involves seamless integration with existing data processing frameworks and tools. Verify that it works well with the organization's chosen ecosystem of technologies.
  • Assess how easily data pipelines and workflows have been adapted to incorporate Iceberg.

User satisfaction and adoption:

  • Gather feedback from data engineers, analysts, and other stakeholders to gauge their satisfaction with Apache Iceberg.
  • Successful adoption is often indicated by positive user experiences and a smooth transition to the new data management approach.

Return on investment (ROI):

  • Calculate the ROI of implementing Apache Iceberg by comparing the benefits (e.g., cost savings, improved performance) against the investment in terms of time, resources, and infrastructure.
  • A positive ROI demonstrates the successful use of Iceberg in delivering value to the business.

Continuous improvement:

  • The successful use of Apache Iceberg involves ongoing monitoring and refinement. Regularly assess its performance and consider updates or optimizations to keep it aligned with evolving business needs.

Documentation and knowledge transfer:

  • Ensure that documentation and knowledge transfer processes are in place to empower teams to effectively use and maintain Apache Iceberg.
  • Successful use includes building internal expertise and resources for ongoing support and development.

Apache Iceberg blog posts


Getting Started With Cloudera Open Data Lakehouse on Private Cloud

Bill Zhang | Monday, October 16, 2023

From Hive Tables to Iceberg Tables: Hassle-Free

Srinivas Rishindra Pothireddi | Friday, July 14, 2023

Learn more about Apache Iceberg and Cloudera

Get more details on the benefits Apache Iceberg brings to Cloudera’s open data lakehouse by boosting performance and increasing scalability to meet enterprise demands.

Open Data Lakehouse

Integrates Iceberg with Cloudera SDX to unify security, fine-tune governance policies, and track lineage and metadata across multiple clouds.

Cloudera Data Warehouse

Leverages Apache Iceberg to improve multi-function analytics and speed up BI and querying across diverse data types and quality, wherever your data lives.

Cloudera Machine Learning

Retrains models with data in its original state and match predictions to historical data to re-evaluate models, identify deficiencies, and deploy better models.  

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