Connecting to Apache Iceberg with REST APIs

Managing and accessing massive datasets is increasingly challenging as the pace of data generation accelerates. Enterprises face growing difficulties in ensuring secure, scalable, and high-performance interaction with data systems while adapting to dynamic requirements.

This blog explores how Apache Iceberg and DreamFactory address these issues. By combining Iceberg's advanced table format for large-scale datasets with DreamFactory's API generation capabilities, organizations can create secure, well-documented REST APIs with minimal effort.

Introduction to DreamFactory

What is DreamFactory?

DreamFactory is an on-premise API generation and management platform designed to automate the creation of REST APIs for diverse data sources. Built on a security-first architecture, it ensures robust control and compliance while minimizing manual API development overhead.

Why DreamFactory?

DreamFactory automates REST API generation, providing standardized endpoints for databases, file systems, and more. Its architecture prioritizes security with role-based access control (RBAC), API key management, and rate limiting, offering scalable solutions for managing data interactions efficiently.

Combining Apache Iceberg and DreamFactory

In a typical implementation, Apache Iceberg tables are stored in AWS S3 and accessed through Snowflake for query processing. DreamFactory simplifies the interaction by automating the generation of REST APIs, enabling structured access to Iceberg tables without custom development. This approach ensures secure, standardized data access while leveraging Iceberg’s scalability and Snowflake’s query engine.

Step-by-Step Process

1. Connecting DreamFactory to Snowflake

To connect DreamFactory to a Snowflake instance hosting Iceberg tables, you need to follow a structured configuration process:

• Set Up Snowflake Credentials: Ensure you have the necessary account identifier, username, password, role, database, warehouse, and schema information for your Snowflake instance. These details are essential for authentication.
• Configure DreamFactory: In the DreamFactory admin interface, navigate to the "API Services" section and create a new service under the "Database" category. Select Snowflake as the database type.
• Input Connection Details: Enter the Snowflake credentials in the configuration fields. Verify the connection by testing the configuration to ensure DreamFactory can successfully connect to the Snowflake instance.
• Specify Data Scope: Optionally, restrict the connection to specific schemas or databases within Snowflake to limit the API's data access scope.

2. API Generation

Once the connection is configured, DreamFactory automatically generates RESTful endpoints for all Iceberg tables within the selected schema:

• Endpoint Creation: REST APIs are created for each table, enabling operations such as querying data (GET), inserting new records (POST), updating existing records (PUT/PATCH), and deleting records (DELETE).
• OpenAPI Documentation: For each generated endpoint, DreamFactory produces OpenAPI-compliant documentation. This includes details on available operations, required parameters, and response formats. The documentation can be exported or accessed via the admin interface, making it easier for developers to integrate the APIs into their applications.
• Immediate Availability: The APIs are functional as soon as the connection is saved, requiring no additional manual coding or setup

3. Access Management

DreamFactory provides robust tools to control access to the generated APIs, ensuring secure interaction with the Iceberg tables:

• Role-Based Access Control (RBAC): Define user roles and assign granular permissions to each API endpoint. For example, you can allow certain users to read data (GET) but restrict write operations (POST, PUT, DELETE) to admin roles only.
• API Key Generation: Create unique API keys tied to specific roles. Each API request must include the key, ensuring only authorized users or systems can access the APIs.
• Permission Auditing: Regularly review and update roles to align with organizational security policies and ensure least-privilege access.

Advanced Features

API Security and Access Management

• Role-Based Access Control (RBAC): Assign granular permissions to users and services, defining access levels for specific endpoints and operations.
• API Key Management: Enforce authentication using unique API keys tied to roles, ensuring secure access to resources.
• Rate Limiting: Implement request throttling to prevent unauthorized access and mitigate system abuse.

Performance Optimization

• Caching: Utilize caching solutions like Redis to store frequently accessed query results, reducing response times and database load.
• Rate Limit Configuration: Define per-user, service, or endpoint request thresholds to maintain consistent performance under high load.

Best Practices for Working with Big Data APIs

Secure API Design

Designing APIs for large datasets requires stringent security measures to prevent unauthorized access and ensure compliance with organizational and regulatory requirements. One key practice is implementing granular permissions using role-based access control (RBAC). By assigning specific roles to users and services, you can limit access to only the resources and operations necessary for their tasks. This minimizes the risk of accidental or malicious data exposure. Additionally, leveraging API key management tied to these roles ensures that every interaction with the API is authenticated and traceable.

Another essential practice is rate limiting, which protects APIs from abuse and ensures consistent system performance. By defining request thresholds at the user, service, or endpoint level, you can control resource usage and prevent excessive strain on the backend. For example, setting limits based on roles—such as lower limits for public APIs and higher limits for trusted internal services—balances accessibility with resource protection

Efficient Data Interaction

When working with large datasets, efficient data interaction is crucial to avoid overloading the system and to improve performance. Pagination is a fundamental practice that divides large result sets into manageable chunks, reducing the memory required to process and transfer data. This not only improves API response times but also enhances the user experience by delivering data incrementally.

Another important practice is using filtering and schema-specific queries. By querying only the relevant fields or records, you can significantly reduce the volume of data retrieved, minimizing bandwidth usage and processing overhead. For example, instead of retrieving all columns from a table, you might request only the necessary columns for a specific operation. Similarly, applying filters—such as date ranges or conditional constraints—reduces the size of the result set, ensuring that the API returns only actionable data.

Together, these practices ensure that APIs for large datasets remain secure, performant, and efficient, even under heavy usage or with complex data structures.

Conclusion

Apache Iceberg and DreamFactory offer a robust solution for managing and accessing large-scale datasets. Iceberg's architecture supports advanced features like ACID transactions, schema evolution, and time travel, making it ideal for modern data challenges. When integrated with DreamFactory, organizations can automatically generate secure, efficient, and scalable REST APIs, enabling rapid and controlled interaction with these datasets. This combination reduces development overhead while maintaining strict security and performance standards.

Want to give it a try? Spin up DreamFactory in your own environment for free.