added readme file stub (#623)

* added readme file stub

* added details for updating tableau

* Renamed file to pass build checks.

* P in python upper case.

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Author: ayeshalshukri1-nhs

scripts/tableau/README.md

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+# Tableau DQ Metrics
+
+This README describes the process
+for publishing the DQ metrics data from AWS S3 to the Tableau Server.
+
+There are two methods for publishing the data to Tableau Server:
+
+1. [Running the Python scripts locally](#python-scripts)
+2. Using the [GitHub Actions workflow](#github-actions-workflow) to run the scripts on a schedule
+
+## Overview
+
+This section provides an overview of the process for publishing DQ metrics data to Tableau Server.
+
+The Python scripts are used to generate a Tableau `.hyper` datasource file from the DQ metric
+JSON files stored in S3.
+This `.hyper` file is then published to Tableau Server by overwriting an existing datasource.
+Once this datasource is updated, a ping is sent to a Tableau view to trigger a cache refresh,
+ensuring that the latest data is updated and available for visualisation.
+
+The following sections provide more details on the Python scripts and the GitHub Actions workflow that automates this process.
+
+## Python Scripts
+
+There are two Python scripts involved:
+
+- `generate_tableau_data.py`
+  Reads DQ metric JSON files from the S3 bucket `eligibility-signposting-api-dev-dq-metrics`, filters to approximately the last 3 months, and writes a Tableau Hyper extract called `converted.hyper`.
+
+- `tableau_refresh.py`
+  Publishes `./converted.hyper` to Tableau Server by overwriting an existing datasource, then pings a Tableau view to trigger a cache refresh.
+
+This work supports the EliD DQ metrics Tableau MVP, where Tableau is being used to visualise DQ metrics for monitoring and comparison against expected thresholds.
+
+---
+
+### What the scripts do
+
+#### 1. Generate Hyper extract
+
+`generate_tableau_data.py`:
+
+- connects to S3
+- scans daily `processing_date=YYYYMMDD/` prefixes for the last 90 days
+- reads `.json` files from the bucket
+- parses JSON or JSONL content into a pandas DataFrame
+- creates a Tableau Hyper file named `converted.hyper`
+
+The S3 source bucket is currently hard coded as:
+
+```python
+S3_BUCKET = "eligibility-signposting-api-dev-dq-metrics"
+```
+
+and the output file is:
+
+```python
+LOCAL_HYPER_PATH = "converted.hyper"
+```
+
+#### 2. Publish to Tableau
+
+`tableau_refresh.py`:
+
+- checks that `./converted.hyper` exists
+- validates the file type
+- reads Tableau credentials and settings from environment variables
+- signs in using a Tableau Personal Access Token (PAT)
+- overwrites the configured datasource
+- pings the Tableau view `EligibilityData-DQMetrics/DataQualityMetrics?:refresh=y` to trigger refresh
+
+NOTE: PAT credentials must be set as GitHub secrets for the workflow, and as environment variables for local testing.
+
+---
+
+### Repository structure
+
+The GitHub Actions workflow expects the scripts to exist at:
+
+```text
+scripts/tableau/generate_tableau_data.py
+scripts/tableau/tableau_refresh.py
+```
+
+because it runs:
+
+```yaml
+python scripts/tableau/generate_tableau_data.py
+python scripts/tableau/tableau_refresh.py
+```
+
+---
+
+### Running locally
+
+### Prerequisites
+
+You will need:
+
+- Python 3.13 recommended, to match the workflow setup.
+- Access to AWS with permission to read from the S3 bucket `eligibility-signposting-api-dev-dq-metrics`.
+- Tableau Personal Access Token credentials
+- The required Python packages installed:
+  - `boto3`
+  - `pandas`
+  - `tableauserverclient`
+  - `tableauhyperapi`
+  - `requests`
+
+#### Install dependencies
+
+If installing dependencies locally
+
+```bash
+pip install boto3 pandas tableauserverclient tableauhyperapi requests
+```
+
+#### Required environment variables
+
+Before publishing to Tableau, set the following environment variables:
+
+```bash
+export TABLEAU_TOKEN_NAME="your-token-name"
+export TABLEAU_TOKEN_VALUE="your-token-value"
+export TABLEAU_SERVER_URL="https://your-tableau-server"
+export TABLEAU_DATASOURCE_ID="your-datasource-id"
+export TABLEAU_SITE_ID="NHSD_DEV"
+```
+
+`TABLEAU_SERVER_URL` is the base URL of the Tableau Server instance, for example `https://tableau.nhsd.com`.
+
+`TABLEAU_DATASOURCE_ID` is the ID of the datasource to overwrite,
+which can be found in the Tableau Server URL when viewing the datasource (LUID).
+
+`TABLEAU_SITE_ID` defaults to `NHSD_DEV` if not set.
+
+#### AWS credentials
+
+You also need AWS credentials available locally so `boto3` can read from S3.
+Also, may need to set the AWS region if not configured globally:
+
+```bash
+export AWS_REGION=eu-west-2
+```
+
+The workflow uses `eu-west-2`.
+
+#### Run the scripts
+
+Step 1: Generate the Hyper file
+
+```bash
+python scripts/tableau/generate_tableau_data.py
+```
+
+This should create:
+
+```text
+converted.hyper
+```
+
+Step 2: Publish to Tableau
+
+```bash
+python scripts/tableau/tableau_refresh.py
+```
+
+If you want the publish step to continue even when the cache refresh ping fails:
+
+```bash
+python scripts/tableau/tableau_refresh.py --ignore-refresh-failure
+```
+
+The optional `--ignore-refresh-failure` flag prevents the script from exiting with an error
+if the Tableau refresh ping fails.
+
+---
+
+### Expected local flow
+
+1. Read recent DQ metric JSON data from S3
+2. Build `converted.hyper`
+3. Sign in to Tableau using PAT credentials
+4. Overwrite the target datasource
+5. Trigger cache refresh for the relevant workbook view
+
+---
+
+## GitHub Actions workflow
+
+The GitHub Actions workflow is named:
+
+```yaml
+Daily Tableau Data Update
+```
+
+It supports:
+
+- scheduled execution every day at `10:00 AM UTC`
+- manual triggering using `workflow_dispatch` for testing
+
+### Workflow triggers
+
+```yaml
+on:
+  schedule:
+    - cron: '0 10 * * *'
+  workflow_dispatch:
+```
+
+### Workflow jobs
+
+The workflow has two jobs:
+
+### 1. `metadata`
+
+This job:
+
+- checks out the repo
+- reads versions from `.tool-versions`
+- sets CI metadata such as build timestamp and version string
+
+### 2. `publish`
+
+This job:
+
+- sets up Python 3.13
+- checks out the repository
+- installs the required Python packages
+- assumes the AWS deployment role using GitHub OIDC
+- runs the S3 to Hyper script
+- publishes the datasource to Tableau
+
+---
+
+## GitHub Actions secrets and variables
+
+The workflow requires the following GitHub environment configuration.
+
+### Secrets
+
+- `AWS_ACCOUNT_ID`
+- `TABLEAU_TOKEN_NAME`
+- `TABLEAU_TOKEN_VALUE`
+- `TABLEAU_DATASOURCE_ID`
+
+### Variables
+
+- `TABLEAU_SITE_ID`
+- `TABLEAU_SERVER_URL`
+
+### GitHub environment
+
+The workflow runs under the `dev` environment.
+
+---
+
+## Example GitHub Actions execution flow
+
+```text
+Schedule or manual trigger
+  -> metadata job
+  -> publish job
+      -> setup Python
+      -> install dependencies
+      -> assume AWS role
+      -> generate converted.hyper from S3 JSON files
+      -> publish converted.hyper to Tableau datasource
+      -> trigger Tableau cache refresh
+```
+
+---
+
+## Troubleshooting
+
+### `Datasource file not found: ./converted.hyper`
+
+The publish script expects `converted.hyper` to exist in the current working directory. Run the data generation script first.
+
+### `Missing required environment variables`
+
+Ensure the required Tableau environment variables are set before running `tableau_refresh.py`.
+
+### No data found
+
+If no JSON files are found for the date range, the generation script will print:
+
+```text
+No data found for the selected date range.
+```
+
+and no Hyper file will be created.
+
+### Cache refresh fails
+
+By default, a Tableau cache refresh failure causes the script to exit with a non zero status. Use:
+
+```bash
+python scripts/tableau/tableau_refresh.py --ignore-refresh-failure
+```
+
+if you want to allow publish success even when the refresh ping fails.