Why Apache Iceberg Should Be Your Organization’s Single Source of Truth?
Apache Iceberg is revolutionizing data lake architectures by providing a modern, open-table format that decouples storage from compute, enabling true data democratization. By leveraging Iceberg as your primary data lake/lakehouse, you eliminate vendor lock-in and gain full control over your data, ensuring cost-effective, scalable, and flexible data management.
The Medallion Architecture: Structuring Your Data Lakehouse
A data lakehouse is a modern data architecture that unifies the flexibility and scalability of data lakes with the performance and governance of data warehouses—delivering the best of both in a single, streamlined platform. A data lakehouse merges schema-on-read agility with ACID-compliant performance, enabling a unified architecture that supports both analytics and machine learning at scale.
Adopting the Medallion Architecture within an Apache Iceberg-powered data lake or lakehouse allows organizations to efficiently manage data as it progresses through different layers of refinement:
1. Bronze Layer (Raw Data)
1. All incoming data from various systems and sources is first ingested into the Iceberg lake.
2. Acts as the single source of truth for raw data.
3. Eliminates the need to first send raw data to a data warehouse, saving on expensive compute costs.
2. Silver Layer (Refined Data)
1. Data is cleaned, transformed, normalized, denormalized, flattened, and aggregated.
2. Acts as a prepared layer for further analytical processes.
3. Gold Layer (Business-Ready Data)
1. Further refined datasets, tailored for business end-users and specific analytical use cases.
2. Supports direct querying from BI tools, ML models, and data applications.
By structuring data within these layers inside Apache Iceberg, organizations avoid unnecessary data movement, reduce ETL/ ELT complexities, and achieve significant cost savings.
True Data Democratization: Query Directly from Iceberg
One of the key advantages of Apache Iceberg is that business users and downstream systems can directly query Bronze, Silver, or Gold tables from the data lake/ lake houses, leveraging data stored in Cloud object stores. There is no need to move the data into separate vendor-controlled warehouses (e.g., Snowflake, Redshift) before analysis.
This ensures:
- Open-format storage: Retain full ownership of your data without vendor lock-in.
- Seamless integration: Query Iceberg tables using engines like Trino, Spark, Dremio, Snowflake, Databricks or more…
- Scalability and cost efficiency: Process data at lake economics instead of expensive warehouse compute costs.
- Data products at the source: Build reusable data products directly from Iceberg tables.
Additional Use Cases for Apache Iceberg
Beyond structured data transformation, Apache Iceberg offers several other compelling use cases:
- Machine Learning & AI Pipelines
- Supports scalable and efficient feature engineering.
- Enables ML models to train on the latest datasets without unnecessary data movement.
- Real-Time Data Processing & Streaming
- Integrates with Apache Flink, Spark Streaming, and Kafka.
- Facilitates real-time analytics while maintaining ACID compliance.
- Data Versioning & Time Travel
- Enables querying past versions of datasets for audits and reproducibility.
- Enhances debugging and rollback capabilities.
- Multi-Cloud & Hybrid Data Architectures
- Consistent Data Access Across Cloud Providers: Apache Iceberg operates on open table formats stored in object storage (S3, ADLS, GCS), allowing seamless access across AWS, Azure, and Google Cloud.
- Decoupled Compute and Storage: Workloads can run using Apache Spark, Trino, Presto, Flink, and other query engines without being restricted to a single vendor’s or cloud provider’s analytics services.
- Cross-Cloud Data Analytics: Iceberg’s open-table format allows organizations to store data in one cloud (e.g., AWS S3) but process it in another (e.g., using Google BigQuery or Azure Synapse).
- Avoid Vendor Lock-in: Unlike cloud-native warehouses that tie data to their ecosystems, Iceberg enables an open and portable approach to data management.
- Regulatory Compliance & Governance
- Enables time travel queries, allowing organizations to query historical snapshots of data for audits and compliance checks.
- Integrates with Lake Formation and other catalogs to enforce fine-grained access control.
- Facilitates compliance with GDPR, HIPAA, and other data governance policies.
Transitioning from Traditional Data Warehouses to Apache Iceberg Lake houses
Many organizations currently send all their data to cloud warehouses like Snowflake or Redshift, where the data transformation and refinement happen. While moving completely to an Iceberg-centric architecture isn’t always immediate, the transition can be strategically phased:
- For New Data Projects: Start by loading data directly into object stores with Iceberg instead of having to land it in warehouses.
- Minimize Warehouse Footprint:
- Instead of sending all data including raw (Bronze) and refined (Silver) data to the warehouse, prioritize utilizing data warehouse to process the Gold layer.
- Keep preprocessing within the data lake/lakehouse to leverage cost-effective Iceberg storage and compute.
- If occasional access to Bronze/Silver is needed, leverage warehouse catalog integration to query Iceberg tables directly.
- For Hive-Based Lakes: If you are using Hive as your existing data lake, start migrating new datasets to Iceberg.
- Gradually transition existing Hive tables to Iceberg with minimal downstream impact.
- Over time, modify pipelines to stop sending data to downstream systems and let them query Iceberg directly.
By strategically implementing these changes, organizations can progressively unbundle their data storage and compute from vendor-controlled architectures, lowering costs while enhancing data accessibility.
Visualizing the Transition to an Iceberg-Based Data Lake
To help illustrate the evolution of modern data architectures, we’ve outlined three key models that represent different stages in the journey from traditional data warehousing to a more flexible, scalable Iceberg-based data lake. These visualizations highlight how data flows from ingestion to consumption across each model, and how organizations can strategically transition toward a hybrid or fully Iceberg-native architecture.
Traditional Warehouse-Centric Architecture
Data Sources → Snowflake/Redshift → Transformation → Business Consumption
Apache Iceberg Medallion Architecture
Data Sources → Iceberg Lake (Bronze) → Transformation (Silver) → Business Data (Gold) → Query from Iceberg
Optimized Hybrid Approach (Transition Strategy)
Data Sources → Iceberg Lake (Bronze/Silver) → Gold (Sent to Snowflake/Redshift if needed) → Query Bronze/Silver from Iceberg
The Future: Apache Iceberg as the Default Standard
Organizations aiming for long-term scalability, cost-effectiveness, and data control could increasingly consider Apache Iceberg the default architecture for all new data projects. Iceberg enables true data ownership, open-format flexibility, and unbundling from vendor-controlled ecosystems, ensuring that organizations are prepared for the future of data.
With the growing trend of Data Products, Iceberg plays a crucial role in building high-quality, reusable data products directly from the lake without unnecessary duplication or vendor dependency.
By embracing Iceberg, businesses can realize the full potential of data lakes while optimizing costs and ensuring an open, scalable future for their data architecture.
For information about Qlik™, click here: qlik.com.
For specific and specialized solutions from QQinfo, click here: QQsolutions.
In order to be in touch with the latest news in the field, unique solutions explained, but also with our personal perspectives regarding the world of management, data and analytics, click here: QQblog !