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Updated for dbt-teradata 1.9.0
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tallamohan committed Jan 19, 2025
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18 changes: 10 additions & 8 deletions website/docs/docs/core/connect-data-platform/teradata-setup.md
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Expand Up @@ -38,17 +38,19 @@ import SetUpPages from '/snippets/_setup-pages-intro.md';
| 1.6.x | ✅ | ✅ | ✅ | ❌ |
| 1.7.x | ✅ | ✅ | ✅ | ❌ |
| 1.8.x | ✅ | ✅ | ✅ | ✅ |
| 1.9.x | ✅ | ✅ | ✅ | ✅ |

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## dbt dependent packages version compatibility

| dbt-teradata | dbt-core | dbt-teradata-util | dbt-util |
|--------------|------------|-------------------|----------------|
| 1.2.x | 1.2.x | 0.1.0 | 0.9.x or below |
| 1.6.7 | 1.6.7 | 1.1.1 | 1.1.1 |
| 1.7.x | 1.7.x | 1.1.1 | 1.1.1 |
| 1.8.x | 1.8.x | 1.1.1 | 1.1.1 |
| 1.8.x | 1.8.x | 1.2.0 | 1.2.0 |
| 1.8.x | 1.8.x | 1.3.0 | 1.3.0 |
| dbt-teradata | dbt-core | dbt-teradata-util | dbt-util |
|--------------|----------|-------------------|----------------|
| 1.2.x | 1.2.x | 0.1.0 | 0.9.x or below |

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| 1.6.7 | 1.6.7 | 1.1.1 | 1.1.1 |
| 1.7.x | 1.7.x | 1.1.1 | 1.1.1 |

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| 1.8.x | 1.8.x | 1.1.1 | 1.1.1 |

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| 1.8.x | 1.8.x | 1.2.0 | 1.2.0 |
| 1.8.x | 1.8.x | 1.3.0 | 1.3.0 |
| 1.9.x | 1.9.x | 1.3.0 | 1.3.0 |


### Connecting to Teradata
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32 changes: 3 additions & 29 deletions website/docs/reference/resource-configs/teradata-configs.md
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Expand Up @@ -12,25 +12,6 @@ id: "teradata-configs"
+quote_columns: false #or `true` if you have csv column headers with spaces
```
* *Enable view column types in docs* - Teradata Vantage has a dbscontrol configuration flag called `DisableQVCI`. This flag instructs the database to create `DBC.ColumnsJQV` with view column type definitions. To enable this functionality you need to:
1. Enable QVCI mode in Vantage. Use `dbscontrol` utility and then restart Teradata. Run these commands as a privileged user on a Teradata node:
```bash
# option 551 is DisableQVCI. Setting it to false enables QVCI.
dbscontrol << EOF
M internal 551=false
W
EOF
# restart Teradata
tpareset -y Enable QVCI
```
2. Instruct `dbt` to use `QVCI` mode. Include the following variable in your `dbt_project.yml`:
```yaml
vars:
use_qvci: true
```
For example configuration, see [dbt_project.yml](https://github.com/Teradata/dbt-teradata/blob/main/test/catalog/with_qvci/dbt_project.yml) in `dbt-teradata` QVCI tests.

## Models
### <Term id="table" />
Expand Down Expand Up @@ -351,14 +332,7 @@ If a user sets some key-value pair with value as `'{model}'`, internally this `'
## Unit testing
* Unit testing is supported in dbt-teradata, allowing users to write and execute unit tests using the dbt test command.
* For detailed guidance, refer to the [dbt unit tests documentation](/docs/build/documentation).

* QVCI must be enabled in the database to run unit tests for views.
* Additional details on enabling QVCI can be found in the General section.
* Without QVCI enabled, unit test support for views will be limited.
* Users might encounter the following database error when testing views without QVCI enabled:
```
* [Teradata Database] [Error 3706] Syntax error: Data Type "N" does not match a Defined Type name.
```
> In Teradata, reusing the same alias across multiple common table expressions (CTEs) or subqueries within a single model is not permitted, as it results in parsing errors; therefore, it is essential to assign unique aliases to each CTE or subquery to ensure proper query execution.

## valid_history incremental materialization strategy
_This is available in early access_
Expand Down Expand Up @@ -388,8 +362,8 @@ The valid_history strategy in dbt-teradata involves several critical steps to en
* Remove duplicates and conflicting values from the source data:
* This step ensures that the data is clean and ready for further processing by eliminating any redundant or conflicting records.
* The process of removing primary key duplicates (two or more records with the same value for the `unique_key` and BEGIN() bond of the `valid_period` fields) in the dataset produced by the model. If such duplicates exist, the row with the lowest value is retained for all non-primary-key fields (in the order specified in the model). Full-row duplicates are always de-duplicated.
* Identify and adjust overlapping time slices (if use_valid_to_time='yes):
* Overlapping time periods in the data are corrected to maintain a consistent and non-overlapping timeline. To do so, the valid period end bound of a record is adjusted to meet the begin bound of the next record with the same `unique_key` value and overlapping `valid_period` value if any.
* Identify and adjust overlapping time slices:
* Overlapping or adjacent time periods in the data are corrected to maintain a consistent and non-overlapping timeline. To achieve this, the macro adjusts the valid period end bound of a record to align with the begin bound of the next record (if they overlap or are adjacent) within the same `unique_key` group. If `use_valid_to_time = 'yes'`, the valid period end bound provided in the source data is used. Otherwise, a default end date is applied for missing bounds, and adjustments are made accordingly.
* Manage records needing to be adjusted, deleted, or split based on the source and target data:
* This involves handling scenarios where records in the source data overlap with or need to replace records in the target data, ensuring that the historical timeline remains accurate.
* Compact history:
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