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Welcome to SQLSupport.org. Today, we will be discussing DB2 Identity Columns and Sequence Management. This topic is crucial for database administrators and developers who are looking to maintain data consistency and integrity when working with IBM DB2 databases. To make this post more comprehensive, we will be exploring technologies such as SQL Server 2022, Azure SQL, Microsoft Fabric, Delta Lake, OpenAI plus SQL, and Databricks.

In DB2, an identity column is a column in a table that is automatically populated by the system. This ensures that each row in the table has a unique value, maintaining data integrity. This is similar to the Auto Increment feature in MySQL or the Identity feature in SQL Server. Here is a simple example of creating a table with an identity column in DB2.

CREATE TABLE employees
(
  id INT NOT NULL GENERATED ALWAYS AS IDENTITY,
  name VARCHAR(100),
  email VARCHAR(100)
);

While identity columns ensure unique values within a single table, sequence objects can generate unique values across multiple tables. This is helpful when you have related tables and need unique identifiers. You can create a sequence in DB2 using the CREATE SEQUENCE statement.

CREATE SEQUENCE emp_seq AS INTEGER
START WITH 1
INCREMENT BY 1;

With the rise of distributed databases and cloud technologies, managing sequences and identity columns in DB2 can get tricky. Azure SQL, Microsoft Fabric, and Delta Lake are often discussed in the context of distributed systems and analytics platforms, but DB2 identity and sequence behavior is still defined by DB2 itself. The key is understanding how values are generated, cached, and consumed so you can avoid collisions and gaps where it matters.

Delta Lake is an open source storage layer that brings ACID transactions to Apache Spark and big data workloads. Databricks provides a unified analytics platform built around these ideas. These tools are excellent for analytics pipelines, but when your source system is DB2, the identity and sequence strategy still starts with the DB2 engine and the application patterns that consume those values.

In the era of AI and automation, OpenAI plus SQL can help you document standards, generate migration checklists, and validate patterns, but it should not be generating identity or sequence values outside the database. Keep key generation inside DB2, and use automation to improve correctness and consistency around it.

In conclusion, DB2 identity columns and sequences are powerful tools for maintaining data integrity. In larger environments, the biggest wins come from clear rules on key generation, consistent use across applications, and careful handling during migrations and bulk loads.

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