Format of a data filter Filter logic Filter precedence Aurora MySQL examples Aurora PostgreSQL examples Adding data filters Removing data filters

Data filtering for Aurora zero-ETL integrations with Amazon Redshift

Aurora zero-ETL integrations support data filtering, which lets you control which data is replicated from your source Aurora DB cluster to your target Amazon Redshift data warehouse. Instead of replicating the entire database, you can apply one or more filters to selectively include or exclude specific tables. This helps you optimize storage and query performance by ensuring that only relevant data is transferred. Currently, filtering is limited to the database and table levels. Column- and row-level filtering are not supported.

Data filtering can be useful when you want to:

Join certain tables from two or more different source clusters, and you don't need complete data from either cluster.
Save costs by performing analytics using only a subset of tables rather than an entire fleet of databases.
Filter out sensitive information—such as phone numbers, addresses, or credit card details—from certain tables.

You can add data filters to a zero-ETL integration using the Amazon Web Services Management Console, the Amazon Command Line Interface (Amazon CLI), or the Amazon RDS API.

If the integration has a provisioned Amazon Redshift cluster as its target, the cluster must be on patch 180 or higher to use data filtering.

Topics

Format of a data filter
Filter logic
Filter precedence
Aurora MySQL examples
Aurora PostgreSQL examples
Adding data filters to an integration
Removing data filters from an integration

Format of a data filter

You can define multiple filters for a single integration. Each filter either includes or excludes any existing and future database tables that match one of the patterns in the filter expression. Aurora zero-ETL integrations use Maxwell filter syntax for data filtering.

Each filter has the following elements:

Element Description

Filter type

Element	Description
Filter type	An `Include` filter type includes all tables that match one of the patterns in the filter expression. An `Exclude` filter type excludes all tables that match one of the patterns.
Filter expression	A comma-separated list of patterns. Expressions must use Maxwell filter syntax.
Pattern	A filter pattern in the format `database.table` for Aurora MySQL, or `database.schema.table` for Aurora PostgreSQL. You can specify literal names, or define regular expressions. Note For Aurora MySQL, regular expressions are supported in both the database and table name. For Aurora PostgreSQL, regular expressions are supported only in the schema and table name, not in the database name. You can't include column-level filters or denylists. A single integration can have a maximum of 99 total patterns. In the console, you can enter patterns within a single filter expression, or spread them out among multiple expressions. A single pattern can't exceed 256 characters in length.

An Include filter type includes all tables that match one of the patterns in the filter expression. An Exclude filter type excludes all tables that match one of the patterns.

Filter expression

A comma-separated list of patterns. Expressions must use Maxwell filter syntax.

Pattern

A filter pattern in the format database.table for Aurora MySQL, or database.schema.table for Aurora PostgreSQL. You can specify literal names, or define regular expressions.

Note

For Aurora MySQL, regular expressions are supported in both the database and table name. For Aurora PostgreSQL, regular expressions are supported only in the schema and table name, not in the database name.

You can't include column-level filters or denylists.

A single integration can have a maximum of 99 total patterns. In the console, you can enter patterns within a single filter expression, or spread them out among multiple expressions. A single pattern can't exceed 256 characters in length.

Important

If you select an Aurora PostgreSQL source DB cluster, you must specify at least one data filter pattern. At minimum, the pattern must include a single database (database-name.*.*) for replication to Amazon Redshift.

The following image shows the structure of Aurora MySQL data filters in the console:

Important

Do not include personally identifying, confidential, or sensitive information in your filter patterns.

Data filters in the Amazon CLI

When using the Amazon CLI to add a data filter, the syntax differs slightly from the console. You must assign a filter type (Include or Exclude) to each pattern individually, so you can't group multiple patterns under one filter type.

For example, in the console you can group the following comma-separated patterns under a single Include statement:

Aurora MySQL


mydb.mytable, mydb./table_\d+/

Aurora PostgreSQL


mydb.myschema.mytable, mydb.myschema./table_\d+/

However, when using the Amazon CLI, the same data filter must be in the following format:

Aurora MySQL


'include: mydb.mytable, include: mydb./table_\d+/'

Aurora PostgreSQL


'include: mydb.myschema.mytable, include: mydb.myschema./table_\d+/'

Filter logic

If you don't specify any data filters in your integration, Aurora assumes a default filter of include:*.*, which replicates all tables to the target data warehouse. However, if you add at least one filter, the default logic switches to exclude:*.*, which excludes all tables by default. This lets you explicitly define which databases and tables to include in replication.

For example, if you define the following filter:


'include: db.table1, include: db.table2'

Aurora evaluates the filter as follows:


'exclude:*.*, include: db.table1, include: db.table2'

Therefore, Aurora only replicates table1 and table2 from the database named db to the target data warehouse.

Filter precedence

Aurora evaluates data filters in the order you specify. In the Amazon Web Services Management Console, it processes filter expressions from left to right and top to bottom. A second filter or an individual pattern that follows the first can override it.

For example, if the first filter is Include books.stephenking, it includes only the stephenking table from the books database. However, if you add a second filter, Exclude books.*, it overrides the first filter. This prevents any tables from the books index from being replicated to Amazon Redshift.

When you specify at least one filter, the logic starts by assuming exclude:*.* by default, which automatically excludes all tables from replication. As a best practice, define filters from broadest to most specific. Start with one or more Include statements to specify the data to replicate, then add Exclude filters to selectively remove certain tables.

The same principle applies to filters that you define using the Amazon CLI. Aurora evaluates these filter patterns in the order that you specify them, so a pattern might override one that you specify before it.

Aurora MySQL examples

The following examples demonstrate how data filtering works for Aurora MySQL zero-ETL integrations:

Include all databases and all tables:
```
'include: *.*'
```
Include all tables within the books database:
```
'include: books.*'
```
Exclude any tables named mystery:
```
'include: *.*, exclude: *.mystery'
```

Include two specific tables within the books database:


'include: books.stephen_king, include: books.carolyn_keene'

Include all tables in the books database, except for those containing the substring mystery:
```
'include: books.*, exclude: books./.*mystery.*/'
```
Include all tables in the books database, except those starting with mystery:
```
'include: books.*, exclude: books./mystery.*/'
```
Include all tables in the books database, except those ending with mystery:
```
'include: books.*, exclude: books./.*mystery/'
```
Include all tables in the books database that start with table_, except for the one named table_stephen_king. For example, table_movies or table_books would be replicated, but not table_stephen_king.
```
'include: books./table_.*/, exclude: books.table_stephen_king'
```

Aurora PostgreSQL examples

The following examples demonstrate how data filtering works for Aurora PostgreSQL zero-ETL integrations:

Include all tables within the books database:
```
'include: books.*.*'
```
Exclude any tables named mystery in the books database:
```
'include: books.*.*, exclude: books.*.mystery'
```
Include one table within the books database in the mystery schema, and one table within employee database in the finance schema:
```
'include: books.mystery.stephen_king, include: employee.finance.benefits'
```
Include all tables in the books database and science_fiction schema, except for those containing the substring king:
```
'include: books.science_fiction.*, exclude: books.*./.*king.*/
```
Include all tables in the books database, except those with a schema name starting with sci:
```
'include: books.*.*, exclude: books./sci.*/.*'
```
Include all tables in the books database, except those in the mystery schema ending with king:
```
'include: books.*.*, exclude: books.mystery./.*king/'
```
Include all tables in the books database that start with table_, except for the one named table_stephen_king. For example, table_movies in the fiction schema and table_books in the mystery schema are replicated, but not table_stephen_king in either schema:
```
'include: books.*./table_.*/, exclude: books.*.table_stephen_king'
```

Adding data filters to an integration

You can configure data filtering using the Amazon Web Services Management Console, the Amazon CLI, or the Amazon RDS API.

Important

If you add a filter after you create an integration, Aurora treats it as if it always existed. It removes any data in the target Amazon Redshift data warehouse that doesn’t match the new filtering criteria and resynchronizes all affected tables.

To add data filters to a zero-ETL integration

Sign in to the Amazon Web Services Management Console and open the Amazon RDS console at https://console.amazonaws.cn/rds/.
In the navigation pane, choose Zero-ETL integrations. Select the integration that you want to add data filters to, and then choose Modify.
Under Source, add one or more Include and Exclude statements.

The following image shows an example of data filters for a MySQL integration:
When you're satisfied with the changes, choose Continue and Save changes.

To add data filters to a zero-ETL integration using the Amazon CLI, call the modify-integration command. In addition to the integration identifier, specify the --data-filter parameter with a comma-separated list of Include and Exclude Maxwell filters.

The following example adds filter patterns to my-integration.

For Linux, macOS, or Unix:


aws rds modify-integration \
    --integration-identifier my-integration \
    --data-filter 'include: foodb.*, exclude: foodb.tbl, exclude: foodb./table_\d+/'

For Windows:


aws rds modify-integration ^
    --integration-identifier my-integration ^
    --data-filter 'include: foodb.*, exclude: foodb.tbl, exclude: foodb./table_\d+/'

To modify a zero-ETL integration using the RDS API, call the ModifyIntegration operation. Specify the integration identifier and provide a comma-separated list of filter patterns.

Removing data filters from an integration

When you remove a data filter from an integration, Aurora reevaluates the remaining filters as if the removed filter never existed. It then replicates any previously excluded data that now meets the criteria into the target Amazon Redshift data warehouse. This triggers a resynchronization of all affected tables.

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Creating zero-ETL integrations

Adding and querying data