# Data filtering for Aurora zero-ETL integrations Aurora zero-ETL integrations support data filtering, which lets you control which data is replicated from your source Aurora DB cluster to your target 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 AWS Management Console, the AWS Command Line Interface (AWS CLI), or the Amazon RDS API. If the integration has a provisioned cluster as its target, the cluster must be on [patch 180](https://docs.aws.amazon.com/redshift/latest/mgmt/cluster-versions.html#cluster-version-180) or higher to use data filtering. **Topics** + [Format of a data filter](#zero-etl.filtering-format) + [Filter logic](#zero-etl.filtering-evaluate) + [Filter precedence](#zero-etl.filtering-precedence) + [Aurora MySQL examples](#zero-etl.filtering-examples-mysql) + [Aurora PostgreSQL examples](#zero-etl.filtering-examples-postgres) + [Adding data filters to an integration](#zero-etl.add-filter) + [Removing data filters from an integration](#zero-etl.remove-filter) ## 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](https://maxwells-daemon.io/filtering/) for data filtering. Each filter has the following elements: | 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](https://maxwells-daemon.io/filtering/). | | 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. 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 the target data warehouse. The following image shows the structure of Aurora MySQL data filters in the console: ![\[Data filters for a zero-ETL integration\]](http://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/images/zero-etl-filter.png) **Important** Do not include personally identifying, confidential, or sensitive information in your filter patterns. ### Data filters in the AWS CLI When using the AWS 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 AWS 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 AWS 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 the target data warehouse. 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 AWS 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 examples 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 AWS Management Console, the AWS 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 data warehouse that doesn’t match the new filtering criteria and resynchronizes all affected tables. ### RDS console **To add data filters to a zero-ETL integration** 1. Sign in to the AWS Management Console and open the Amazon RDS console at [https://console.aws.amazon.com/rds/](https://console.aws.amazon.com/rds/). 1. In the navigation pane, choose **Zero-ETL integrations**. Select the integration that you want to add data filters to, and then choose **Modify**. 1. Under **Source**, add one or more `Include` and `Exclude` statements. The following image shows an example of data filters for a MySQL integration: ![\[Data filters for a zero-ETL integration in the RDS console\]](http://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/images/zero-etl-filter-data.png) 1. When you're satisfied with the changes, choose **Continue** and **Save changes**. ### AWS CLI To add data filters to a zero-ETL integration using the AWS CLI, call the [modify-integration](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/rds/modify-integration.html) command. In addition to the integration identifier, specify the `--data-filter` parameter with a comma-separated list of `Include` and `Exclude` Maxwell filters. **Example** 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+/' ``` ### RDS API To modify a zero-ETL integration using the RDS API, call the [ModifyIntegration](https://docs.aws.amazon.com/AmazonRDS/latest/APIReference/API_ModifyIntegration.html) 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 data warehouse. This triggers a resynchronization of all affected tables.