Mastering Data Type Mapping from Oracle to Azure Hyperscale Citus

Oracle to Azure Hyperscale

In the evolving landscape of database technologies, the shift from Oracle’s robust transactional and warehousing capabilities to Azure Hyperscale Citus’s distributed PostgreSQL architecture represents a paradigm shift. This migration is not merely a change of platforms but a strategic move towards scalability, performance, and real-time analytics. Yet, amidst this transition lies a significant challenge: the intricate process of data type mapping. How do you ensure that your data retains its integrity, and applications remain seamless post-migration?

Understanding the Challenge

Data type compatibility is pivotal in maintaining the integrity of migrated data and ensuring seamless operation of applications post-migration. The differences between Oracle and PostgreSQL’s data handling mechanisms introduce a spectrum of challenges that need meticulous attention. Below are further intricacies involved in this process:

    1. Precision and Performance Considerations

      • Precision Loss: Migrating data from Oracle to PostgreSQL could result in precision loss for certain data types. For instance, Oracle’s floating-point numbers might not map perfectly to PostgreSQL’s, potentially leading to slight discrepancies in calculations and aggregations. Careful consideration and, if necessary, adjustment of the data types in the target database are required to mitigate these effects.
      • Performance Implications: The choice of data types can significantly affect database performance. For example, using PostgreSQL’s TEXT data type for all string data might seem like a simple solution to Oracle’s VARCHAR2 and CLOB differences, but it could lead to performance degradation in scenarios where fixed-length data types could be more efficient.
    1. Boolean and Enum Types

      • Boolean Types: Oracle does not have a built-in BOOLEAN data type, often emulated using NUMBER(1). This practice necessitates a direct conversion to PostgreSQL’s BOOLEAN type, which requires careful mapping of existing data values (e.g., 0 and 1) to true and false.
      • Enum Types: PostgreSQL supports enumerated types (ENUM), allowing for the creation of custom data types with a static, ordered set of values. Migrating Oracle’s CHECK constraints used to enforce a set of specific values for a column into PostgreSQL ENUM types requires additional steps to define these custom types explicitly.
    1. Handling NULLs and Defaults

      • NULL Value Differences: The way NULL values are treated in Oracle versus PostgreSQL can lead to subtle bugs if not properly handled. For example, Oracle treats empty strings as NULL, whereas PostgreSQL differentiates between an empty string and NULL. This difference requires careful handling during migration to avoid unintended data loss or behavior change.
      • Default Value Considerations: When migrating schema definitions, it’s essential to accurately translate default values, especially when they involve expressions or functions specific to Oracle. PostgreSQL might not have direct equivalents for all Oracle functions or behave differently, requiring adjustments or custom function implementations to preserve intended defaults.
    1. Character Sets and Encoding
      • Character Encoding: Oracle databases can use various character sets, and so can PostgreSQL. Ensuring that the character encoding is compatible between the source and target databases is crucial to preventing data corruption, especially for non-ASCII characters. A thorough analysis and possibly conversion of character sets are required as part of the migration process.
    1. Schema and Naming Conventions

      • Naming Conventions: Oracle and PostgreSQL have different limitations and conventions for identifiers (e.g., table names, column names). Oracle’s uppercase naming convention and the limit on identifier lengths could necessitate renaming entities in PostgreSQL to adhere to its case-sensitive nature and naming practices.

Strategies for Effective Data Type Mapping

      • Comprehensive Mapping Tables: Creating detailed mapping tables that list Oracle data types alongside their PostgreSQL equivalents can serve as a valuable reference throughout the migration process. This table should also include notes on any special considerations or conversion functions required.
      • Utilizing Conversion Tools and Extensions: Several tools and extensions facilitate database migration by automatically converting data types where possible and flagging potential issues for manual review. For instance, the use of Foreign Data Wrappers (FDWs) in PostgreSQL can help integrate Oracle data directly, easing the transition.
      • Custom Conversion Functions: In cases where direct mapping is not possible, writing custom conversion functions in SQL or PL/pgSQL can provide a way to transform data from Oracle formats to PostgreSQL equivalents. These functions can handle complex scenarios like converting Oracle’s RAW data type to PostgreSQL’s BYTEA.
      • Advanced Data Modeling Techniques: Before starting the migration, reevaluate your data model to ensure it’s optimized for PostgreSQL and Azure Hyperscale Citus. This step may involve normalizing or denormalizing data structures, depending on the specific use cases and performance goals. Advanced data modeling can mitigate potential data type mapping issues by aligning the data structure more closely with PostgreSQL’s strengths, particularly in distributed environments like Citus.
      • Data Quality Checks: Data quality checks are essential during and after the migration process. This involves not only validating data types but also ensuring that the data itself has been transferred accurately and completely. Employing tools that can automate data comparison between Oracle and PostgreSQL can save significant time and reduce errors. Pay particular attention to edge cases and data anomalies that might not translate directly between systems.

Leap Towards the Future

Migrating from Oracle to Azure Hyperscale Citus is a significant endeavor, with data type mapping posing a considerable challenge. However, with careful planning, the use of appropriate tools, and adherence to best practices, organizations can navigate these challenges successfully. The move to Azure Hyperscale Citus can unlock new levels of scalability and performance, making the effort well worth it.

Visit to explore how we can support your migration from Oracle to Azure Hyperscale Citus, ensuring a smooth transition and unlocking the full potential of your data architecture. For a personalized consultation and to learn more about our services, reach out to us at

With Newt Global’s expertise, your journey toward a scalable, high-performance database system is just a click away. Contact us today to start your migration project with confidence.