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  • Data Ingestion and Replication
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Table of Contents

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  1. Preface
  2. Database Ingestion and Replication

Database Ingestion and Replication

Database Ingestion and Replication

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Schema drift handling

Schema drift handling

Database Ingestion and Replication
 can be configured to automatically detect some source schema changes and handle these changes on the target. This process is referred to as
schema drift
.
Database Ingestion and Replication
 can detect the following types of source schema changes:
  • Add column
  • Modify column
  • Drop column
  • Rename column
When you define a task, on the
Schedule and Runtime Options
 page of the
database ingestion and replication
 task wizard, you can configure how the supported types of schema changes are handled. For example, you can configure schema drift options to ignore the changes, replicate them, or stop the job or subtask when a schema change occurs. For more information, see Configuring schedule and runtime options. Note that dIfferent types of schema changes might have different default settings, depending on the target type.
Schema drift options are supported for the following source - target combinations and load types:
Source
Load Type
Target
Db2 for i
Incremental
Combined initial and incremental
Amazon Redshift, Amazon S3, Databricks, Google BigQuery, Google Cloud Storage, Kafka (incremental loads only), Microsoft Azure Data Lake Storage, Microsoft Azure Synapse Analytics, Oracle, Oracle Cloud Object Storage, PostgreSQL, Snowflake, and SQL Server
Db2 for LUW
Incremental
Combined initial and incremental
Snowflake
Db2 for z/OS, except Db2 11
Incremental
Combined initial and incremental
Amazon Redshift, Amazon S3, Databricks, Google BigQuery, Google Cloud Storage, Kafka (incremental loads only), Microsoft Azure Data Lake Storage, Microsoft Azure Synapse Analytics, Oracle, Oracle Cloud Object Storage, Snowflake, and SQL Server
Microsoft SQL Server
Incremental
Combined initial and incremental
Amazon Redshift, Amazon S3, Databricks, Google BigQuery, Google Cloud Storage, Kafka (incremental loads only), Microsoft Azure Data Lake Storage, Microsoft Azure Synapse Analytics, Oracle, Oracle Cloud Object Storage, PostgreSQL, Snowflake, and SQL Server
Oracle
Incremental
Combined initial and incremental
Amazon Redshift, Amazon S3, Databricks, Google BigQuery, Google Cloud Storage, Kafka (incremental loads only), Microsoft Azure Data Lake Storage, Microsoft Azure Synapse Analytics, Oracle, Oracle Cloud Object Storage, PostgreSQL, Snowflake, and SQL Server
PostgreSQL
Incremental
Combined initial and incremental
Incremental loads: Amazon Redshift, Amazon S3, Databricks, Google BigQuery, Google Cloud Storage, Kafka (incremental loads only), Microsoft Azure Data Lake Storage, Microsoft Azure Synapse Analytics, Oracle, Oracle Cloud Object Storage, PostgreSQL, and Snowflake
Combined initial and incremental loads: Oracle, PostgreSQL, and Snowflake
  • If you try to replicate a type of schema change that is not supported on the target, the
    database ingestion and replication
     job ends with an error.
  • Database Ingestion and Replication
     does not replicate source changes that add, remove, or modify primary key or unique key constraints. If these types of changes occur on the source, you must resynchronize the target tables.
  • If you configured schema drift options to stop the job when
    Database Ingestion and Replication
     detects a schema change, you can use the
    Resume With Options
     command to resume the job with an override schema drift option.
  • Database Ingestion and Replication
     detects a schema change in a source table only after DML operations occur on the altered source table. If multiple schema changes occur without intervening DML operations,
    Database Ingestion and Replication
     detects all of the schema changes at one time, when a DML operation occurs. To ensure that
    Database Ingestion and Replication
     detects all of the supported schema changes correctly, Informatica recommends that you apply schema changes to source tables one by one, each followed by at least one DML change.
  • If you set the
    Add Column
     option to
    Replicate
     for tasks that have an Oracle or SQL Server source,
    Database Ingestion and Replication
     assumes that any new column is added at the end of the table definition. If a new column appears in the middle of a table definition,
    Database Ingestion and Replication
     might treat the change as if the column has been dropped and added again. In this situation, the following alert might report that the same column has been both dropped and added: 
    Column <column_name2> has been added. Column <column_name2> has been dropped.
  • Database ingestion and replication
     tasks that have Microsoft Azure Synapse Analytics targets cannot replicate rename operations on source columns. The
    Replicate
     option is not available.
  • Database ingestion and replication
     tasks that have Snowflake targets support modify operations on source columns with the following limitations:
    • Snowflake targets cannot modify the scale of NUMBER columns.
    • Snowflake targets do not support changing the data type of an existing column to a different data type.
  • Database ingestion and replication
     tasks that have Google BigQuery targets cannot replicate rename or modify operations on source columns. The schema drift options for these operations are not available.
  • If you set the
    Add Column
     option to
    Replicate
     and then add a column with a default value to a Db2 for i source table,
    Database Ingestion and Replication
     adds the default value to the newly added table rows to the target. However, existing rows on the target are not updated to reflect the default values. To get the default value populated to the existing target rows, perform another initial load to re-materialize the target.
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