Iceberg 101: Better Data Lakes with Apache Iceberg
Key Takeaways:
Data lakes promised flexible, low-cost data storage but often became "data swamps" due to lack of structure and governance
Apache Iceberg transforms data lakes into lakehouses by adding warehouse-like capabilities: ACID transactions, schema evolution, and query optimization
Open-source format ensures vendor independence and interoperability across multiple query engines and platforms
Organizations adopt Iceberg to operationalize data faster while supporting diverse workloads from analytics to machine learning
Qlik Open Lakehouse eliminates implementation complexity for Apache Iceberg with automated ingestion, optimization, and enterprise capabilities including data quality and governance.
A Quick Catch Up on Definitions
A data lake is a centralized repository that allows you to store structured and unstructured data at any scale. Learn more: What is a Data Lake?
A data lakehouse is a data management architecture that combines the cost-effectiveness and flexibility of data lakes with the structure, performance, and management capabilities of data warehouses. Learn more: What is a Data Lakehouse?
Apache Iceberg is an open table format designed for analytics of large data sets. It provides a high-performance format for data lake tables that supports schema evolution, ACID transactions, time travel, partition evolution, and more. Learn more: Apache Iceberg - The Basics
The Problem With Data Lakes
With growing data volumes, organizations are forced to rethink how they store and manage data. Traditional data warehouses, while powerful, became expensive and rigid when faced with the volume, variety, and velocity of modern data - leading to the rise of data lakes as a promising alternative.
However, organizations soon found that data lake in themselves were not a panacea, and often provided limited utility due to their unstructured nature. Apache Iceberg has emerged as the missing piece of the puzzle by bringing warehouse-like capabilities to data lakes, in an architecture known as the data lakehouse.
Promise vs. Reality
Data lakes emerged with a compelling vision: create a centralized repository that could store any type of data at massive scale and low cost. Organizations could dump structured data from databases, semi-structured logs from applications, unstructured content like documents and images, and streaming data from IoT devices—all into the same cost-effective cloud storage. The promise was that you could store everything first, then figure out how to use it later with the flexibility to support any analytics tool or use case.
This "schema-on-read" approach offered unprecedented flexibility compared to the rigid "schema-on-write" requirements of traditional data warehouses. Data teams could ingest data quickly without upfront modeling, adapt to changing business requirements, and support diverse workloads from business intelligence to machine learning. The economic advantages were equally compelling: cloud object storage costs a fraction of traditional data warehouse storage, and the ability to decouple storage from compute meant organizations could scale each independently.
Performance suffered as queries had to scan massive amounts of unoptimized data.
Data consistency became a major problem without transactional support, making it difficult to trust the data for business-critical decisions
Lack of schema enforcement and data cataloging made it nearly impossible for analysts and data scientists to discover and understand available datasets.
Apache Iceberg, an open table format, aims to address these challenges and make data lakes more performant, manageable, and adaptable to various use cases.
The Role of Iceberg in Modern Data Lakes
Apache Iceberg plays a crucial role in modern data lake architectures by bringing a set of warehouse-like capabilities to data lakes (this is often referred to as a Data Lakehouse):
Iceberg provides a table abstraction layer over data files stored in the lake, which allows users to interact with the data using familiar SQL commands, making it easier to query and manage data in the lake.
Iceberg also supports ACID transactions, which ensure data consistency and reliability. With ACID transactions, multiple users can concurrently read and write data to the same table without conflicts or data corruption. This is particularly important in data lake environments where data is constantly being updated and accessed by multiple users and applications.
Iceberg supports schema evolution. As data evolves, Iceberg allows users to easily add, drop, or rename columns in a table without the need to rewrite the entire dataset. This flexibility enables organizations to adapt to changing data requirements and avoid costly data migrations.
Iceberg also provides powerful optimization features that improve query performance in data lakes. One such optimization is data compaction, which involves merging small data files into larger ones, reducing the number of files that need to be scanned during query execution. Iceberg also supports partition pruning, which allows queries to skip irrelevant partitions, significantly reducing the amount of data that needs to be processed.
Iceberg enables time travel – i.e., users can query historical versions of a table. This is particularly useful for auditing, debugging, and reproducing results. With time travel, users can easily compare data changes over time and track the lineage of their data.
Learn more: Apache Iceberg - The Basics
By bringing these warehouse-like capabilities to data lakes, Iceberg makes them more usable and adaptable – enabling organizations to run complex queries, perform real-time updates, and support diverse workloads, including batch processing, streaming, and machine learning.
Why Companies are Adopting Iceberg and the Data Lakehouse
Companies are increasingly adopting Iceberg for their data lake architectures due to several compelling reasons. One of the primary advantages of Iceberg is its open format, which avoids vendor lock-in. Unlike proprietary data formats, Iceberg is an open-source project that is supported by a growing community of developers and organizations. This means that companies can use Iceberg with a wide range of query tools and platforms – including Snowflake, Databricks, Apache Spark, Apache Trino/Presto, Apache Flink etc, giving them the flexibility to choose the best solutions for their needs.
Standardization is another important driver of Iceberg adoption. With growing adoption of Iceberg, organizations can rely on a common set of rules and conventions for storing and accessing data, ensuring consistency and interoperability across different applications used to query or manage the data.
The data management and query optimization described in the previous section are attractive to companies since they offer a simpler way to operationalize data. With Iceberg, data lake projects can get off the ground faster, and are at smaller risk of becoming a data engineering resource sink.
Finally, Iceberg enables companies to expand their data lake use cases beyond traditional SQL analytics. With Iceberg's support for real-time updates and streaming data ingestion, companies can build real-time applications and data pipelines that process data as it arrives. Iceberg also integrates well with machine learning frameworks, allowing data scientists to train models directly on data stored in the lake.
Qlik Open Lakehouse: Simplifying Iceberg Implementation
While Iceberg provides a powerful foundation for building data lakehouses, implementing and managing Iceberg tables – including ingestion, optimization, catalog integration, and governance can still be challenging. This is where Qlik Open Lakehouse comes in.
Qlik Open Lakehouse, a capability within Qlik Talend Cloud, simplifies the process of building and managing data lakehouses using Iceberg. You can easily ingest data in real time from hundreds of sources— including operational databases, SaaS applications, SAP, mainframes and more—and write it directly into your S3 data lake as analytics-ready Iceberg tables with just a few clicks.
By using Qlik Open Lakehouse, you can introduce a set of capabilities that you would be hard-pressed to find in a traditional data lake:
High-Throughput Ingestion: Ingest batch and real-time CDC data from 200+ sources directly into Iceberg tables with automated schema evolution and conflict resolution - without burning any data warehouse compute for ingestion.
Adaptive Iceberg Optimizer: Our proprietary technology continuously monitors and optimizes your Iceberg tables automatically, delivering up to 5x better query performance and 50% reduction in costs—all without manual tuning.
Enterprise Integrations: Native integration with leading Iceberg catalogs (AWS Glue, Apache Polaris, Snowflake Open Catalog) and compatibility with major query engines (Snowflake, Databricks, Amazon Athena, Apache Spark, Trino, Presto, and more).
Data Warehouse Mirroring: Automatically mirror data from your Iceberg tables to cloud data warehouses like Snowflake without duplicating data, ensuring interoperability with existing systems.
Qlik Open Lakehouse allows you to harness the full power of Apache Iceberg while eliminating the operational complexity typically associated with lakehouse implementations. This enables your data teams to focus on deriving insights rather than managing infrastructure.
Learn More
Ready to build your Iceberg-based lakehouse? Learn more about Qlik Open Lakehouse and discover how to unlock the full potential of your data with Apache Iceberg.
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