# Connecting to Netezza with AWS Schema Conversion Tool
You can use AWS SCT to convert schemas, code objects, and application code from Netezza to Amazon Redshift.
## Privileges for Netezza as a source
The following privileges are required for using Netezza as a source:
+ select on system.definition\$1schema.system view
+ select on system.definition\$1schema.system table
+ select on system.definition\$1schema.management table
+ list on **
+ list on **
+ list on **.all.table
+ list on **.all.external table
+ list on **.all.view
+ list on **.all.materialized view
+ list on **.all.procedure
+ list on **.all.sequence
+ list on **.all.function
+ list on **.all.aggregate
In the preceding example, replace placeholders as following:
+ Replace *database\$1name* with the name of the source database.
+ Replace *schema\$1name* with the name of the source schema.
AWS SCT requires access to the following system tables and views. You can grant access to these objects instead of granting access to `system.definition_schema.system view` and `system.definition_schema.system tables` in the preceding list.
+ select on system.definition\$1schema.\$1t\$1aggregate
+ select on system.definition\$1schema.\$1t\$1class
+ select on system.definition\$1schema.\$1t\$1constraint
+ select on system.definition\$1schema.\$1t\$1const\$1relattr
+ select on system.definition\$1schema.\$1t\$1database
+ select on system.definition\$1schema.\$1t\$1grpobj\$1priv
+ select on system.definition\$1schema.\$1t\$1grpusr
+ select on system.definition\$1schema.\$1t\$1hist\$1config
+ select on system.definition\$1schema.\$1t\$1object
+ select on system.definition\$1schema.\$1t\$1object\$1classes
+ select on system.definition\$1schema.\$1t\$1proc
+ select on system.definition\$1schema.\$1t\$1type
+ select on system.definition\$1schema.\$1t\$1user
+ select on system.definition\$1schema.\$1t\$1usrobj\$1priv
+ select on system.definition\$1schema.\$1vt\$1sequence
+ select on system.definition\$1schema.\$1v\$1aggregate
+ select on system.definition\$1schema.\$1v\$1constraint\$1depends
+ select on system.definition\$1schema.\$1v\$1database
+ select on system.definition\$1schema.\$1v\$1datatype
+ select on system.definition\$1schema.\$1v\$1dslice
+ select on system.definition\$1schema.\$1v\$1function
+ select on system.definition\$1schema.\$1v\$1group
+ select on system.definition\$1schema.\$1v\$1obj\$1relation
+ select on system.definition\$1schema.\$1v\$1obj\$1relation\$1xdb
+ select on system.definition\$1schema.\$1v\$1procedure
+ select on system.definition\$1schema.\$1v\$1relation\$1column
+ select on system.definition\$1schema.\$1v\$1relation\$1keydata
+ select on system.definition\$1schema.\$1v\$1relobjclasses
+ select on system.definition\$1schema.\$1v\$1schema\$1xdb
+ select on system.definition\$1schema.\$1v\$1sequence
+ select on system.definition\$1schema.\$1v\$1synonym
+ select on system.definition\$1schema.\$1v\$1system\$1info
+ select on system.definition\$1schema.\$1v\$1sys\$1constraint
+ select on system.definition\$1schema.\$1v\$1sys\$1object\$1dslice\$1info
+ select on system.definition\$1schema.\$1v\$1sys\$1user
+ select on system.definition\$1schema.\$1v\$1table
+ select on system.definition\$1schema.\$1v\$1table\$1constraint
+ select on system.definition\$1schema.\$1v\$1table\$1dist\$1map
+ select on system.definition\$1schema.\$1v\$1table\$1organize\$1column
+ select on system.definition\$1schema.\$1v\$1table\$1storage\$1stat
+ select on system.definition\$1schema.\$1v\$1user
+ select on system.definition\$1schema.\$1v\$1view
+ select on system.information\$1schema.\$1v\$1relation\$1column
+ select on system.information\$1schema.\$1v\$1table
+ select on \$1hist\$1column\$1access\$1\$1
## Connecting to Netezza as a source
Use the following procedure to connect to your Netezza source database with the AWS Schema Conversion Tool.
**To connect to a Netezza source database**
1. In the AWS Schema Conversion Tool, choose **Add source**.
1. Choose **Netezza**, then choose **Next**.
The **Add source** dialog box appears.
1. For **Connection name**, enter a name for your database. AWS SCT displays this name in the tree in the left panel.
1. Use database credentials from AWS Secrets Manager or enter them manually:
+ To use database credentials from Secrets Manager, use the following instructions:
1. For **AWS Secret**, choose the name of the secret.
1. Choose **Populate** to automatically fill in all values in the database connection dialog box from Secrets Manager.
For information about using database credentials from Secrets Manager, see [Configuring AWS Secrets Manager in the AWS Schema Conversion Tool](CHAP_UserInterface.SecretsManager.md).
+ To enter the Netezza source database connection information manually, use the following instructions:
****
[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/SchemaConversionTool/latest/userguide/CHAP_Source.Netezza.html)
1. Choose **Test Connection** to verify that AWS SCT can connect to your source database.
1. Choose **Connect** to connect to your source database.
## Configuring ongoing data replication
After you convert your Netezza database schemas and apply them to your Amazon Redshift database, you can migrate data with AWS SCT data extraction agents. The agent extracts your data and uploads it to your Amazon S3 bucket. You can then use AWS SCT to copy the data from Amazon S3 to Amazon Redshift.
If data in your source database changes during the migration process, you can capture ongoing changes with your AWS SCT data extraction agents. Then you can replicate these ongoing changes in your target database after you complete the initial data migration. This process is called ongoing data replication or *change data capture* (CDC).
**To configure ongoing data replication for migrations from Netezza to Amazon Redshift**
1. In your source database, create a history database. You can use the following code example in the Netezza command line interface (CLI).
```
nzhistcreatedb -d history_database_name -t query -v 1 -u load_user -o histdb_owner -p your_password
```
In the preceding example, replace *history\$1database\$1name* with the name of your history database. Next, replace *load\$1user* with the name of the user that you have defined to load history data to the database. Then, replace *histdb\$1owner* with the name of the user that you have defined as the owner of the history database. Make sure that you have already created this user and granted the `CREATE DATABASE` permission. Finally, replace *your\$1password* with a secure password.
1. Configure the history logging. To do so, use the following code example.
```
CREATE HISTORY CONFIGURATION history_configuration_name HISTTYPE QUERY
DATABASE history_database_name USER load_user PASSWORD your_password COLLECT PLAN, COLUMN
LOADINTERVAL 1 LOADMINTHRESHOLD 0 LOADMAXTHRESHOLD 0 STORAGELIMIT 25
LOADRETRY 2 VERSION 1;
```
In the preceding example, replace *history\$1configuration\$1name* and *history\$1database\$1name* with the names of your history configuration and your history database. Next, replace *load\$1user* with the name of the user that you have defined to load history data to the database. Then, replace *your\$1password* with a secure password.
1. Grant read permissions for all tables in the history database. You can use the following code example to grant the `SELECT` permission.
```
GRANT SELECT ON history_database_name.ALL.TABLE TO your_user;
```
In the preceding example, replace *history\$1database\$1name* with the name of your history database. Next, replace *your\$1user* with the name of the user with minimal permissions to work with your Netezza database. You use the credentials of this database user in AWS SCT.
1. Collect statistics for each table in your source schema to get the information about the cardinality of columns. You can use the following command to generate statistics in your history database.
```
GENERATE STATISTICS on "schema_name"."table_name";
```
In the preceding example, replace *schema\$1name* and *table\$1name* with the name of your database schema and table.
1. Make sure that you completed the prerequisites by running the following query:
```
SELECT COUNT(*) FROM history_database_name.history_schema_name."$hist_column_access_N";
```
In the preceding example, replace *history\$1database\$1name* and *history\$1schema\$1name* with the name of your history database and schema. Next, replace *N* with the the version number of your history database. For more information about history database versions, see the [IBM Netezza Documentation](https://www.ibm.com/docs/en/netezza?topic=history-database-versions).
1. Install your data extraction agents. For more information, see [Installing extraction agents](agents.md#agents.Installing).
Make sure that the `{working.folder}` parameter in the `settings.properties` file for all extractor instances points to the same folder. In this case, your extractors can coordinate the CDC session and use a single transaction point for all subtasks.
1. Register your data extraction agent. For more information, see [Registering extraction agents with the AWS Schema Conversion Tool](agents.md#agents.Using).
1. Create your CDC task. For more information, see [Creating, running, and monitoring an AWS SCT task](agents.md#agents.Tasks).
1. Open your project in AWS SCT. In the left pane, choose your source table. Open the context (right-click) menu, and choose **Create local task**.
1. For **Task name**, enter a descriptive name for your data migration task.
1. For **Migration mode**, choose **Extract, upload, and copy**.
1. Select **Enable CDC**.
1. Choose the **CDC settings** tab and define the scope and the schedule of CDC sessions.
1. Choose **Test task** to verify that you can connect to your working folder, Amazon S3 bucket, and Amazon Redshift data warehouse.
1. Choose **Create** to create your task.
1. Choose the **Tasks** tab, choose your task from the list, and choose **Start**.
1. The AWS SCT task maintains transactional consistency on the target database. The data extraction agent replicates transactions from the source in transaction ID order.
If you stop any of the migration sessions or if it fails, then the CDC processing also stops.
## Netezza to Amazon Redshift conversion settings
To edit Netezza to Amazon Redshift conversion settings, choose **Settings** in AWS SCT, and then choose **Conversion settings**. From the upper list, choose **Netezza**, and then choose **Netezza – Amazon Redshift**. AWS SCT displays all available settings for Netezza to Amazon Redshift conversion.
Netezza to Amazon Redshift conversion settings in AWS SCT include options for the following:
+ To limit the number of comments with action items in the converted code.
For **Add comments in the converted code for the action items of selected severity and higher**, choose the severity of action items. AWS SCT adds comments in the converted code for action items of the selected severity and higher.
For example, to minimize the number of comments in your converted code, choose **Errors only**. To include comments for all action items in your converted code, choose **All messages**.
+ To set the maximum number of tables that AWS SCT can apply to your target Amazon Redshift cluster.
For **The maximum number of tables for the target Amazon Redshift cluster**, choose the number of tables that AWS SCT can apply to your Amazon Redshift cluster.
Amazon Redshift has quotas that limit the use tables for different cluster node types. If you choose **Auto**, AWS SCT determines the number of tables to apply to your target Amazon Redshift cluster depending on the node type. Optionally, choose the value manually. For more information, see [Quotas and limits in Amazon Redshift](https://docs.aws.amazon.com/redshift/latest/mgmt/amazon-redshift-limits.html) in the *Amazon Redshift Management Guide*.
AWS SCT converts all your source tables, even if this is more than your Amazon Redshift cluster can store. AWS SCT stores the converted code in your project and doesn't apply it to the target database. If you reach the Amazon Redshift cluster quota for the tables when you apply the converted code, then AWS SCT displays a warning message. Also, AWS SCT applies tables to your target Amazon Redshift cluster until the number of tables reaches the limit.
+ To apply compression to Amazon Redshift table columns. To do so, select **Use compression encoding**.
AWS SCT assigns compression encoding to columns automatically using the default Amazon Redshift algorithm. For more information, see [Compression encodings](https://docs.aws.amazon.com/redshift/latest/dg/c_Compression_encodings.html) in the *Amazon Redshift Database Developer Guide*.
By default, Amazon Redshift doesn't apply compression to columns that are defined as sort and distribution keys. You can change this behavior and apply compression to these columns. To do so, select **Use compression encoding for KEY columns**. You can select this option only when you select the **Use compression encoding** option.
## Netezza to Amazon Redshift conversion optimization settings
To edit Netezza to Amazon Redshift conversion optimization settings, choose **Settings** in AWS SCT, and then choose **Conversion settings**. From the upper list, choose **Netezza**, and then choose **Netezza – Amazon Redshift**. In the left pane, choose **Optimization strategies**. AWS SCT displays conversion optimization settings for Netezza to Amazon Redshift conversion.
Netezza to Amazon Redshift conversion optimization settings in AWS SCT include options for the following:
+ To work with automatic table optimization. To do so, select **Use Amazon Redshift automatic table tuning**.
Automatic table optimization is a self-tuning process in Amazon Redshift that automatically optimizes the design of tables. For more information, see [Working with automatic table optimization](https://docs.aws.amazon.com/redshift/latest/dg/t_Creating_tables.html) in the *Amazon Redshift Database Developer Guide*.
To rely only on the automatic table optimization, choose **None** for **Initial key selection strategy**.
+ To choose sort and distribution keys using your strategy.
You can choose sort and distribution keys using Amazon Redshift metadata, statistical information, or both these options. For **Initial key selection strategy** on the **Optimization strategies** tab, choose one of the following options:
+ Use metadata, ignore statistical information
+ Ignore metadata, use statistical information
+ Use metadata and statistical information
Depending on the option that you choose, you can select optimization strategies. Then, for each strategy, enter the value (0–100). These values define the weight of each strategy. Using these weight values, AWS SCT defines how each rule influences on the choice of distribution and sort keys. The default values are based on the AWS migration best practices.
You can define the size of small tables for the **Find small tables** strategy. For **Min table row count** and **Max table row count**, enter the minimum and maximum number of rows in a table to define it as a small table. AWS SCT applies the `ALL` distribution style to small tables. In this case, a copy of the entire table is distributed to every node.
+ To configure strategy details.
In addition to defining the weight for each optimization strategy, you can configure the optimization settings. To do so, choose **Conversion optimization**.
+ For **Sort key columns limit**, enter the maximum number of columns in the sort key.
+ For **Skewed threshold value**, enter the percentage (0–100) of a skewed value for a column. AWS SCT excludes columns with the skew value greater than the threshold from the list of candidates for the distribution key. AWS SCT defines the skewed value for a column as the percentage ratio of the number of occurrences of the most common value to the total number of records.
+ For **Top N queries from the query history table**, enter the number (1–100) of the most frequently used queries to analyze.
+ For **Select statistics user**, choose the database user for which you want to analyze the query statistics.
Also, on the **Optimization strategies** tab, you can define the size of small tables for the **Find small tables** strategy. For **Min table row count** and **Max table row count**, enter the minimum and maximum number of rows in a table to consider it as a small table. AWS SCT applies the `ALL` distribution style to small tables. In this case, a copy of the entire table is distributed to every node.