As enterprises increasingly adopt data lakes for their analytics needs, they often find themselves grappling more and more with issues like data schema evolution, partitioning, and version control. While Snowflake’s inherent capabilities can go a long way in alleviating these pain points for internal datasets, additional challenges emerge when it comes to leveraging data and metadata stored outside of Snowflake proper.
Apache Iceberg tables for Snowflake address the challenge of managing large-scale data lakes across multiple platforms by allowing users to store data and metadata outside of the Snowflake AI Data Cloud while still leveraging that inside of Snowflake with virtually the same performance. By combining the performance and query semantics of traditional Snowflake tables with the flexibility to store data in any environment of your choice, Apache Iceberg tables for Snowflake are ideal for existing data lakes that you cannot, or choose not to, store in Snowflake.
By integrating Iceberg with Snowflake, users can leverage Snowflake’s powerful SQL capabilities and scalability while benefiting from Iceberg’s advanced data management features, streamlining their analytics workflows and improving overall data reliability. This blog post delves into the essence of Apache Iceberg, its core features, and the advantages it offers, particularly when integrated with the Snowflake AI Data Cloud.
Understanding Apache Iceberg
Apache Iceberg is an open-source table format designed to streamline data processing in data lakes. Developed by Netflix data engineers Ryan Blue and Dan Weeks in 2017, it addresses the challenges of managing large datasets by offering a structured and efficient way to handle data storage and retrieval. Key aspects of Apache Iceberg include:
- Abstracted Metadata Layer: Instead of relying on traditional directory table structures, Apache Iceberg defines tables as a canonical list of files with associated metadata, simplifying data management.
- Flexibility: It supports multiple data sources and has no file system dependencies, making it adaptable to various storage environments.
- Efficient Storage: Provides a cost-effective and reliable solution for handling large datasets.
By breaking away from directory-based structures and offering a versatile, metadata-driven approach, Apache Iceberg ensures more streamlined and efficient data operations. This design allows for better scalability, interoperability, and performance across storage systems.
Key Features of Apache Iceberg
Apache Iceberg boasts several features that make it a standout choice for data lake management. These features include:
- Expressive SQL: Supports flexible SQL commands for tasks such as updating rows, merging data, and performing deletes effortlessly.
- Schema Evolution: Accommodates full schema evolution, allowing for additions, deletions, renaming, reordering, and type promotions without disrupting data processes.
- Storage-System Agnostic: Versatile and adaptable to various storage environments, supporting multiple data sources and having no file system dependencies.
- Efficient Metadata Management: Utilizes an abstracted metadata layer to define tables as a canonical list of files, which simplifies data management and enhances performance.
- Scalability: Designed for handling large datasets efficiently, ensuring robust performance regardless of scale.
- Interoperability: Can be leveraged across different storage systems and data processing engines, facilitating seamless data integration and accessibility.
Benefits of Using Apache Iceberg
Apache Iceberg also offers numerous benefits for efficient and reliable data processing:
- Cost Efficiency: Provides a structured, cost-effective storage solution accessible by multiple engines, reducing overhead.
- Performance and Reliability: Abstracts metadata and defines tables through a canonical file list, enhancing data operations’ performance and reliability.
- Interoperability: Enables seamless data integration and accessibility across various storage systems and processing engines.
- Scalability: Efficiently handles large datasets, ensuring robust performance regardless of scale.
- Simplified Data Management: Utilizes an abstracted metadata layer, streamlining data management and enhancing performance.
- Consistency: Ensures consistent results when computing metrics and KPIs, crucial for driving business decisions.
These benefits make Apache Iceberg a powerful and flexible choice for organizations looking to optimize their data lakes and enhance data processing capabilities.
What are Apache Iceberg Tables for Snowflake?
Apache Iceberg tables for Snowflake merge Snowflake’s performance and query capabilities with external cloud storage managed by users. They are ideal for scenarios where data lakes exist but storing data directly in Snowflake is not feasible or not desirable for any number of reasons. Key points include:
- Seamless Integration: Access external data with performance nearly identical to native Snowflake tables, eliminating the need to load data directly into Snowflake-hosted storage.
- Enhanced Data Management: Utilize Apache Iceberg’s abstracted metadata layer to define tables as canonical lists of files, simplifying data management and boosting efficiency.
- Flexibility: Support multiple data sources without file system dependencies, allowing integration across various storage environments.
- Cost Efficiency: Provide a structured, cost-effective storage solution accessible by multiple engines, reducing overhead.
- Governance and Consistency: Ensure governance and consistency when computing metrics and KPIs, crucial for organizational decision-making.
These tables allow organizations to leverage Snowflake’s powerful data processing while maintaining control over their external data storage, enhancing both flexibility and efficiency.
Real World Use Cases
To convey the full benefits of Apache Iceberg tables for Snowflake, it is worth examining some of the reasons an organization might want to leverage Snowflake functionality while storing datasets outside of the Snowflake environment.
Because Snowflake excels at integrating data from a wide variety of sources, it makes sense for many data leaders to leverage their Snowflake environment as a single source of truth where all of their data and workloads live and operate synergistically.
For many more organizations, however, the elegant simplicity of such a solution can be somewhat unrealistic. This is especially true for large enterprises with long data histories and complex tech stacks of interconnected systems.
Departments and business units also often have unique needs, technologies, and politics that can also introduce situations where storing data in more than one place becomes a necessary evil.This reality results in data silos & fragmentation across Snowflake, AWS, Azure, and on-premise systems.
To provide a real world example from Hakkoda’s client roster, consider the case of a large Children’s Hospital. On one hand, their IT business unit primarily leverages Snowflake for Data + AI/ML, but one subsidiary group is using an additional tech stack comprising AWS Sagemaker, S3, and Bedrock. This group also has the political capital within the organization that allows them to operate independently of IT mandates.
In such a scenario, you can begin to imagine how IT might be able to leverage Iceberg on S3 for their data lake, allowing their department to support both teams’ data consumption needs while better facilitating data sharing between business units. This is a huge selling point for organizations looking to break down data silos, enforce data governance more unilaterally, cut down on duplicate data, and, most importantly, get more consistent results when computing mission-critical metrics and KPIs.
Building a Data Stack That Works for Your Business
At Hakkoda, we believe in the power of the Snowflake AI Data Cloud to break down silos between isolated systems, departments, geographies and business functions, but we also understand that every business has a unique set of challenges and objectives that provide the true backbone of their data strategy.
Data modernization, in other words, isn’t a one-size-fits-all solution. This reality means that data partners like Hakkoda must understand the specific use cases and organizational complexities that our clients bring to their data maturity journeys when helping them chart a course that makes sense for their businesses.
Flexible tools like Apache Iceberg tables for Snowflake enable our teams to to engineer customized data strategies that never lose sight of the practical outcomes they drive. By allowing our clients the option to bring Snowflake’s powerful compute and analytics capabilities to externally hosted datasets, Apache Iceberg tables break down silos and extend the art of the possible without sacrificing the freedom to explore multi-cloud and other multi-platform strategies.
Ready to modernize your data stack with tools and talent tailored to your unique business needs? Talk to one of our experts today.
