So, what makes Iceberg so compelling? Here's the gist:
Organized Data: Transforms your messy data lake into a structured, queryable resource.
Vendor Freedom: Avoid getting trapped by a single vendor's pricing and limitations.
Engine Flexibility: Use the best tool for each data job, maximizing efficiency and cost-effectiveness.
Future-Proof Architecture: Adapt to evolving technology without painful migrations.
In the world of historical analysis, platforms like Snowflake, Redshift, BigQuery, and recently ClickHouse have long been staples. Yet, a noticeable trend is emerging in the data engineering landscape—Apache Iceberg is becoming a hot topic. Users are increasingly sending data directly to Iceberg to build lakehouses, redefining how they manage and query data.
At its core, Iceberg offers transformative capabilities like the ability to easily adapt your data structure as your needs change (schema evolution), look back at previous versions of your data (time travel), and use various tools to analyze your data (compatibility with diverse engines). These features are a game-changer for managing vast datasets, but they also go beyond technical advantages. Adopting Iceberg requires businesses to think strategically—about cost, vendor independence, and future scalability. As such, the rise of Iceberg isn't just about technology; it reflects a shift in how companies approach their data architecture to be more open, flexible, and future-proof.
Beyond Cost Savings: The True Power of Apache Iceberg
Despite these challenges, Iceberg adoption continues to grow for several compelling reasons, not just from a technical perspective but also due to its transformative business implications:
1. Taming the Data Lake: How Iceberg Brings Order to S3
Without Iceberg, trying to find specific information in your raw data files on S3 can be like searching for a needle in a haystack. Tools like AWS Athena can query files, but managing the structure of your data (schema) and controlling who has access (access control) requires manual setup. Iceberg transforms your S3 buckets into well-structured, queryable datasets with proper access controls, making them compatible with any modern query engine. By layering Iceberg on top of S3, businesses gain a cohesive way to organize and make sense of sprawling data lakes, which would otherwise remain chaotic and unmanageable.
2. Breaking Free: The Power of No Vendor Lock-In
Vendor lock-in is a significant concern for organizations using proprietary systems like Snowflake. Historically, if your data was stored in Snowflake, you had little bargaining power when Snowflake decided to increase fees. Migrating data to another platform involved significant effort, giving Snowflake a strong upper hand. Iceberg offers a way to break free from this dependency.
Iceberg eliminates vendor lock-in by providing broad compatibility. Data stored in Iceberg format can be queried by numerous engines, giving organizations the freedom to switch vendors and negotiate pricing more effectively. For instance, businesses can pair Iceberg with cloud-native compute engines like Amazon EMR or Databricks, ensuring adaptability as their data requirements evolve. This flexibility not only fosters cost efficiency but also enables companies to future-proof their data strategies, remaining agile in an ever-changing technological landscape.
3. The Right Tool for the Job: Multi-Engine Compatibility
Different data processing tools (engines) excel at different tasks. Iceberg enables multi-engine usage, allowing you to pick the best tool for the job. For example, you might pair Iceberg with Snowflake for complex analytical queries (OLAP) and DuckDB for lightweight analytics—saving costs without sacrificing flexibility.
Other query engines, such as Trino (for federated queries), RisingWave (for stream processing), LanceDB (for vector search), PuppyGraph (for graph analytics), and more, are further enhancing the Iceberg ecosystem by providing low-latency query capabilities for specific use cases. Moreover, this multi-engine approach enables businesses to explore advanced analytics, from interactive dashboards to real-time streaming analysis, without being locked into a single technology.
4. Speak Any Language: Multi-Language Support
Iceberg supports various programming languages, making it appealing for cross-functional teams. Data engineers can use SQL, while data scientists can leverage Python. For ML/AI workloads, Iceberg’s compatibility with Python-based tools can be transformative, providing seamless access to data for model training and inference. Additionally, frameworks like PyIceberg are making Python integrations even easier, enabling advanced data manipulation directly from Python environments. Teams can also use languages like Java or Scala, ensuring that Iceberg fits seamlessly into diverse enterprise workflows, from backend systems to advanced data science pipelines.
The Lakehouse Vision: Building the Future of Warehousing on Iceberg
Open table formats like Apache Iceberg represent the future of data management. Their unmatched flexibility and ecosystem compatibility provide a compelling alternative to proprietary systems, setting a new standard for modern data architectures.
By 2025, I believe all databases will evolve into data engines that inherently store data in Iceberg format. How could this happen? At RisingWave Labs, we have fully embraced this vision. RisingWave is a cloud-native streaming database that now offers full support of Iceberg tables, empowering users to seamlessly store and query data in Iceberg format:
This integration allows RisingWave users to effortlessly connect with the Iceberg ecosystem, leveraging its open, future-proof design. Users will have the flexibility to interact with their data using any engine or programming language, ensuring compatibility across the ever-expanding analytics landscape. This is a significant step towards a truly open and interoperable data ecosystem.
Final Thoughts
Apache Iceberg is more than just a new technology; it's a paradigm shift in how we manage and utilize data. By embracing open access, flexibility, and vendor independence, Iceberg empowers organizations to build truly future-proof data architectures. The support from industry giants like Databricks, Snowflake, and AWS further solidifies its position as a cornerstone of modern data engineering. As the data landscape continues to evolve, Iceberg offers a path towards a more open, adaptable, and powerful future. Are you ready to take the plunge?
