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Release/v0.17.0 (#169)
* initial * feature/unstructured-data * add coalesce_cast * update filters * update and consolidate models * model revisions * restructure * documentation * remove extra comma * regen docs * formatting * update max token docs * Update CHANGELOG.md * bug/missing-sla-policies * update changelog and add integrity test * update test * update changelog, readme and tests * update test * bug/intercepted-period-joins * adjustmnt * update weeks * update weeks * add integrity test * update weeks * update changelog * bugfix/too-many-partitions (#165) * bugfix/too-many-partitions * docs regen * Update CHANGELOG.md Co-authored-by: fivetran-catfritz <[email protected]> --------- Co-authored-by: fivetran-catfritz <[email protected]> * update changelog * revert docs to main * Documentation Standard Updates (#166) * MagicBot/documentation-updates * Apply suggestions from code review * Update README.md Co-authored-by: fivetran-catfritz <[email protected]> --------- Co-authored-by: fivetran-catfritz <[email protected]> * update default max_tokens * update changelog * Apply suggestions from code review Co-authored-by: Joe Markiewicz <[email protected]> * update readme * regen docs * update yml * Apply suggestions from code review Co-authored-by: Renee Li <[email protected]> * add comments and update changelog * update changelog * Update packages.yml --------- Co-authored-by: Renee Li <[email protected]> Co-authored-by: Joe Markiewicz <[email protected]> Co-authored-by: Renee Li <[email protected]>
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.buildkite/pipeline.yml

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commands: |
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bash .buildkite/scripts/run_models.sh redshift
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- label: ":bricks: Run Tests - Databricks"
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- label: ":databricks: Run Tests - Databricks"
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key: "run_dbt_databricks"
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plugins:
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- docker#v3.13.0:

.buildkite/scripts/run_models.sh

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dbt seed --target "$db" --full-refresh
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dbt run --target "$db" --full-refresh
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dbt test --target "$db"
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dbt run --vars '{using_schedules: false, using_domain_names: false, using_user_tags: false, using_ticket_form_history: false, using_organization_tags: false}' --target "$db" --full-refresh
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dbt run --vars '{zendesk__unstructured_enabled: true, using_schedules: false, using_domain_names: false, using_user_tags: false, using_ticket_form_history: false, using_organization_tags: false}' --target "$db" --full-refresh
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dbt test --target "$db"
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dbt run-operation fivetran_utils.drop_schemas_automation --target "$db"

.github/ISSUE_TEMPLATE/bug-report.yml

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name: 🐞 Bug
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description: Report a bug or an issue you've found within the dbt package
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title: "[Bug] <title>"
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labels: ["bug", "triage"]
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labels: ["type:bug"]
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body:
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- type: markdown
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attributes:
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description: A concise description of what you expected to happen.
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validations:
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required: true
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- type: textarea
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attributes:
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label: Possible solution
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description: Were you able to investigate and/or discover a potential fix to this bug in your investigation? If so, it would be much appreciated if you could submit code samples to show us how your fix resolved this issue.
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validations:
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required: false
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id: orchestration_type
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attributes:
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label: How are you running this dbt package?
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multiple: true
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options:
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- Fivetran Quickstart Data Model
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- Fivetran Transformations
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- dbt Core™
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- dbt Cloud™
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- other (mention it in "Additional Context")
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validations:
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required: true
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description: Our team will assess this issue and let you know if we will add it to a future sprint. However, if you would like to expedite the solution, we encourage you to contribute to the package via a PR. Our team will then work with you to approve and merge your contributions as soon as possible.
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- label: Yes.
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- label: Yes, but I will need assistance and will schedule time during our [office hours](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) for guidance
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- label: Yes, but I will need assistance.
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.github/ISSUE_TEMPLATE/feature-request.yml

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name: 🎉 Feature
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description: Suggest a new feature for the Fivetran dbt package
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title: "[Feature] <title>"
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labels: ["enhancement"]
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labels: ["type:enhancement"]
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label: How would you implement this feature?
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description: |
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How would you build out this feature with your existing data? Any code examples you can provide to help accelerate development on this issue?
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description: Our team will assess this feature and let you know if we will add it to a future sprint. However, if you would like to expedite the feature, we encourage you to contribute to the package via a PR. Our team will then work with you to approve and merge your contributions as soon as possible.
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- label: Yes.
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- label: Yes, but I will need assistance and will schedule time during your [office hours](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) for guidance.
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- label: Yes, but I will need assistance.
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.github/PULL_REQUEST_TEMPLATE/maintainer_pull_request_template.md

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Before marking this PR as "ready for review" the following have been applied:
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- [ ] The appropriate issue has been linked, tagged, and properly assigned
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- [ ] All necessary documentation and version upgrades have been applied
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<!--- Be sure to update the package version in the dbt_project.yml, integration_tests/dbt_project.yml, and README if necessary. -->
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- [ ] docs were regenerated (unless this PR does not include any code or yml updates)
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- [ ] BuildKite integration tests are passing
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- [ ] Detailed validation steps have been provided below

.github/pull_request_template.md

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<!--- To select a checkbox you simply need to add an "x" with no spaces between the brackets (eg. [x] Yes). -->
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- [ ] Yes
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**Provide an emoji that best describes your current mood**
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**Typically there are additional maintenance changes required before this will be ready for an upcoming release. Are you comfortable with the Fivetran team making a few commits directly to your branch?**
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<!--- If you select Yes this will help expedite your PR in case there are small changes required before approval. We encourage you not to use this branch in a production environment as we may make additional updates. -->
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<!--- If you select No, we will not make any changes directly to your branch and will either communicate any planned changes via the PR thread or will merge your PR into a separate branch so we may make changes without modifying your branch.. -->
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- [ ] Yes
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- [ ] No
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**If you had to summarize this PR in an emoji, which would it be?**
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:dancer:
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**PR Template**
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- [Community Pull Request Template](?expand=1&template=pull_request_template.md) (default)
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- [Maintainer Pull Request Template](?expand=1&template=maintainer_pull_request_template.md) (to be used by maintainers)
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- [Maintainer Pull Request Template](?expand=1&template=maintainer_pull_request_template.md) (to be used by maintainers)

.quickstart/quickstart.yml

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public_models: [
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"zendesk__ticket_enriched",
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"zendesk__ticket_metrics",
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"zendesk__ticket_summary",
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"zendesk__ticket_field_history",
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"zendesk__sla_policies",
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"zendesk__ticket_backlog"
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CHANGELOG.md

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# dbt_zendesk v0.17.0
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## New model ([#161](https://github.com/fivetran/dbt_zendesk/pull/161))
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- Addition of the `zendesk__document` model, designed to structure Zendesk textual data for vectorization and integration into NLP workflows. The model outputs a table with:
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- `document_id`: Corresponding to the `ticket_id`
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- `chunk_index`: For text segmentation
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- `chunk`: The text chunk itself
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- `chunk_tokens_approximate`: Approximate token count for each segment
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- This model is currently disabled by default. You may enable it by setting the `zendesk__unstructured_enabled` variable as `true` in your `dbt_project.yml`.
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- This model was developed with the limit of chunk sizes to approximately 5000 tokens for use with OpenAI, however you can change this limit by setting the variable `zendesk_max_tokens` in your `dbt_project.yml`.
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- See the README section [Enabling the unstructured document model for NLP](https://github.com/fivetran/dbt_zendesk/blob/main/README.md#enabling-the-unstructured-document-model-for-nlp) for more information.
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## Breaking Changes (Full refresh required after upgrading)
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- Incremental models running on BigQuery have had the `partition_by` logic adjusted to include a granularity of a month. This change only impacts BigQuery warehouses and was applied to avoid the common `too many partitions` error some users have experienced when partitioning by day. Therefore, adjusting the partition to a month granularity will decrease the number of partitions created and allow for more performant querying and incremental loads. This change was applied to the following models ([#165](https://github.com/fivetran/dbt_zendesk/pull/165)):
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- `int_zendesk__field_calendar_spine`
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- `int_zendesk__field_history_pivot`
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- `zendesk__ticket_field_history`
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- In the [dbt_zendesk_source v0.12.0 release](https://github.com/fivetran/dbt_zendesk_source/releases/tag/v0.12.0), the field `_fivetran_deleted` was added to the following models for use in `zendesk__document` model ([#161](https://github.com/fivetran/dbt_zendesk/pull/161)):
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- `stg_zendesk__ticket`
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- `stg_zendesk__ticket_comment`
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- `stg_zendesk__user`
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- If you have already added `_fivetran_deleted` as a passthrough column via the `zendesk__ticket_passthrough_columns` or `zendesk__user_passthrough_columns` variable, you will need to remove or alias this field from the variable to avoid duplicate column errors.
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## Bug Fixes
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- Fixed an issue in the `zendesk__sla_policies` model where tickets that were opened and solved outside of scheduled hours were not being reported, specifically for the metrics `requester_wait_time` and `agent_work_time`.
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- Resolved by adjusting the join logic in models `int_zendesk__agent_work_time_business_hours` and `int_zendesk__requester_wait_time_business_hours`. ([#164](https://github.com/fivetran/dbt_zendesk/pull/164), [#156](https://github.com/fivetran/dbt_zendesk/pull/156))
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- Fixed an issue in the `zendesk__ticket_metrics` model where certain tickets had miscalculated metrics.
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- Resolved by adjusting the join logic in models `int_zendesk__ticket_work_time_business`, `int_zendesk__ticket_first_resolution_time_business`, and `int_zendesk__ticket_full_resolution_time_business`. ([#167](https://github.com/fivetran/dbt_zendesk/pull/167))
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## Under the hood
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- Added integrity validations:
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- Test to ensure `zendesk__sla_policies` and `zendesk__ticket_metrics` models produce consistent time results. ([#164](https://github.com/fivetran/dbt_zendesk/pull/164))
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- Test to ensure `zendesk__ticket_metrics` contains all the tickets found in `stg_zendesk__ticket`. ([#167](https://github.com/fivetran/dbt_zendesk/pull/167))
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- Modified the `consistency_sla_policy_count` validation test to group by `ticket_id` for more accurate testing. ([#165](https://github.com/fivetran/dbt_zendesk/pull/165))
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- Updated casting in joins from timestamps to dates so that the whole day is considered. This produces more accurate results. ([#164](https://github.com/fivetran/dbt_zendesk/pull/164), [#156](https://github.com/fivetran/dbt_zendesk/pull/156), [#167](https://github.com/fivetran/dbt_zendesk/pull/167))
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- Reduced the weeks looking ahead from 208 to 52 to improve performance, as tracking ticket SLAs beyond one year was unnecessary. ([#156](https://github.com/fivetran/dbt_zendesk/pull/156), [#167](https://github.com/fivetran/dbt_zendesk/pull/167))
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- Updated seed files to reflect a real world ticket field history update scenario. ([#165](https://github.com/fivetran/dbt_zendesk/pull/165))
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# dbt_zendesk v0.16.0
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## 🚨 Minor Upgrade 🚨
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Although this update is not a breaking change, it will likely impact the output of the `zendesk__sla_policies` and `zendesk__sla_metrics` models. [PR #154](https://github.com/fivetran/dbt_zendesk/pull/154) includes the following changes:

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