add Weights and Biases connector example

[fivetran-surabhisingh]

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Description of Change

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Testing

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• Tested the connector with fivetran debug command.
• Added/Updated example specific README.md file, refer here for template.
• Followed Python Coding Standards, refer here

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[Copilot]

Pull Request Overview

This PR adds a new connector for Weights & Biases (W&B) that synchronizes experiment run metadata and metric time-series data. The connector implements incremental synchronization using timestamp-based watermarking, pagination for large datasets, and robust error handling with exponential backoff.

Key changes:

• New W&B connector with API integration for runs and metrics endpoints
• Implements incremental sync using updatedAt timestamp tracking
• Includes retry logic with exponential backoff for transient errors

Reviewed Changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 16 comments.

File	Description
connectors/weights_and_biases/connector.py	Main connector implementation with update/schema functions, API client with retry logic, and incremental sync handling
connectors/weights_and_biases/configuration.json	Configuration template with API key, entity, and project parameters using proper placeholder format
connectors/weights_and_biases/README.md	Comprehensive documentation covering connector overview, features, authentication, data handling, and table schemas

[Copilot]

The validate_configuration() function must be removed when a configuration.json file exists. The SDK automatically validates required fields from configuration.json, making this function redundant and violating SDK v2+ best practices.

[Copilot]

The schema function docstring must use the exact required format. Replace with: 'Define the schema function which lets you configure the schema your connector delivers. See the technical reference documentation for more details on the schema function: https://fivetran.com/docs/connectors/connector-sdk/technical-reference#schema'

Suggested change

	"""Define schema tables."""
	"""
	Define the schema function which lets you configure the schema your connector delivers.
	See the technical reference documentation for more details on the schema function:
	https://fivetran.com/docs/connectors/connector-sdk/technical-reference#schema
	Args:
	configuration: a dictionary that holds the configuration settings for the connector.
	"""

[Copilot]

The update function is missing the required docstring. It must include: 'Define the update function which lets you configure how your connector fetches data. See the technical reference documentation for more details on the update function: https://fivetran.com/docs/connectors/connector-sdk/technical-reference#update' with Args section for configuration and state parameters.

Suggested change

	def update(configuration: dict, state: dict):
	def update(configuration: dict, state: dict):
	"""
	Define the update function which lets you configure how your connector fetches data.
	See the technical reference documentation for more details on the update function:
	https://fivetran.com/docs/connectors/connector-sdk/technical-reference#update
	Args:
	configuration: a dictionary that holds the configuration settings for the connector.
	state: a dictionary that holds the state of the connector.
	"""

[Copilot]

Remove this call to validate_configuration(). The SDK handles configuration validation automatically when configuration.json exists.

[Copilot]

Convert this to a module-level constant with the proper naming convention: __PAGE_SIZE = 100 (placed after imports with other constants). Remove the variable assignment inside the update function and reference the constant instead.

[Copilot]

The main block comments are incorrect. Line 211 should be removed (comment is redundant), line 214 should be removed, and line 215 should be preceded by: '# Test the connector locally'

Suggested change

	# Open the configuration.json file and load its contents into a dictionary.
	with open("configuration.json", "r") as f:
	configuration = json.load(f)
	# Adding this code to your `connector.py` allows you to test your connector by running your file directly from your IDE.
	with open("configuration.json", "r") as f:
	configuration = json.load(f)
	# Test the connector locally

[Copilot]

The page_size parameter is documented in the README but does not exist in configuration.json. Either add it to configuration.json or remove this row from the README. Configuration in the README must exactly match configuration.json.

[Copilot]

The Additional considerations section must contain the exact required disclaimer. Replace the entire section content with: 'The examples provided are intended to help you effectively use Fivetran's Connector SDK. While we've tested the code, Fivetran cannot be held responsible for any unexpected or negative consequences that may arise from using these examples. For inquiries, please reach out to our Support team.'

[Copilot]

Remove bold text formatting. Bold should only be used for UI element names (tabs, menus, buttons, fields). Change to plain text: 'This connector is designed for educational and demonstration purposes using the Fivetran Connector SDK.'

[Copilot]

Mixing implicit and explicit returns may indicate an error, as implicit returns always return None.

[fivetran-chinmayichandrasekar]

@fivetran-surabhisingh Left a few suggestions. The main Readme.md file is missing.

[fivetran-chinmayichandrasekar]

Suggested change

	This connector demonstrates how to fetch experiment and metric data from [Weights & Biases (W&B)](https://wandb.ai/) and upsert it into your destination using the **Fivetran Connector SDK**.
	It synchronizes **run metadata** and **metric time-series data** from W&B projects, supports incremental synchronization via timestamps, and includes comprehensive retry and error handling logic for large-scale ML experiment tracking.
	This connector demonstrates how to fetch experiment and metric data from [Weights & Biases (W&B)](https://wandb.ai/) and upsert it into your destination using the Fivetran Connector SDK.
	It synchronizes run metadata and metric time-series data from W&B projects, supports incremental synchronization via timestamps, and includes comprehensive retry and error handling logic for large-scale ML experiment tracking.

[fivetran-chinmayichandrasekar]

Suggested change

	The connector uses **Bearer Token authentication** to connect securely to the W&B API.
	The connector uses Bearer Token authentication to connect securely to the W&B API.

[fivetran-chinmayichandrasekar]

Suggested change

	## Data handling
	The connector performs the following operations:
	1. **Runs Table**
	- Fetches experiment metadata such as:
	- Run ID
	- User
	- State
	- Creation and update timestamps
	- Tags, config, and summary metrics
	- Converts nested objects (`tags`, `config`, `summaryMetrics`) into JSON strings for storage.
	- Performs incremental filtering using the `updatedAt` field to avoid re-fetching unchanged runs.
	2. **Metrics Table**
	- For each run, retrieves metric history including:
	- Step number
	- Metric name
	- Metric value
	- Timestamp
	- Each metric record is upserted to maintain a complete time-series view.
	3. **Incremental Sync**
	- Tracks the latest synchronization timestamp (`runs_hwm_utc`) to fetch only new or updated runs during subsequent syncs.
	- Uses checkpointing to store this state.
	4. **Checkpointing**
	- Saves synchronization progress after each run:
	```python
	op.checkpoint({"runs_hwm_utc": current_hwm})
	```
	5. **Upserts**
	- Inserts or updates data in Fivetran using:
	```python
	op.upsert("runs", run_record)
	## Data handling
	The connector performs the following operations:
	- **Runs Table**
	- Fetches experiment metadata such as:
	- Run ID
	- User
	- State
	- Creation and update timestamps
	- Tags, config, and summary metrics
	- Converts nested objects (`tags`, `config`, `summaryMetrics`) into JSON strings for storage.
	- Performs incremental filtering using the `updatedAt` field to avoid re-fetching unchanged runs.
	- **Metrics Table**
	- For each run, retrieves metric history including:
	- Step number
	- Metric name
	- Metric value
	- Timestamp
	- Each metric record is upserted to maintain a complete time-series view.
	- **Incremental Sync**
	- Tracks the latest synchronization timestamp (`runs_hwm_utc`) to fetch only new or updated runs during subsequent syncs.
	- Uses checkpointing to store this state.
	- **Checkpointing**
	- Saves synchronization progress after each run:
	```python
	op.checkpoint({"runs_hwm_utc": current_hwm})
	```
	- **Upserts**
	- Inserts or updates data in Fivetran using:
	```python
	op.upsert("runs", run_record)

[fivetran-rishabhghosh]

Please address copilot comments

[fivetran-sahilkhirwal]

Please check for failing checks and existing review comments and re-request the review :)

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