New Integration Request: URL Tables

## Tell us about the new integration you’d like to have
* Which source and which destination?
* Do you need a specific version of the underlying data source e.g: you specifically need support for an older version of the API or DB?

OK this is a slightly strange one, however I often seem to need to snapshot table data from public URLs and stream the content as JSON into BigQuery.

## Describe the context around this new integration
* Which team in your company wants this integration, what for? This helps us understand the use case.
* How often do you want to run syncs?
* If this is an API source connector, which entities/endpoints do you need supported?

Typically I need to take daily snapshots for a historical record of data which is not available via any API.

## Describe the alternative you are considering or using
What are you considering doing if you don’t have this integration through Airbyte?

I have tested this code in a notebook and it works fine, and I could spin it up into a scheduled Cloud Function (triggered by PubSub with the config in the payload), however I am keen on trying to use Airbyte as my single point of data ingestion so I thought this might be an interesting lightweight connector which could enable Airbyte to be used as a (limited) very simple web table scraper:

```
import ssl
import os
import json
import pandas as pd
from datetime import datetime
from google.cloud import bigquery

# specific to this URL due to SSL verification issues
ssl._create_default_https_context = ssl._create_unverified_context

destination_ref = 'beepbeeptechnology.urltable.granada_covid_stats'
source_url = "https://www.juntadeandalucia.es/institutodeestadisticaycartografia/salud/static/resultadosProvincialesCovid_18.html5?prov=18&CodOper=b3_2314&codConsulta=38667"

response = pd.read_html(source_url,encoding='UTF-8')

for response_table in response:
response_table_json = json.dumps(response_table.to_dict(orient='records'), ensure_ascii=False)
stream_json_into_bq_with_id(source_url, response_table_json, destination_ref)
```

Note that this depends on the following function:

```
def stream_json_into_bq_with_id(id_string: str, json_string: str, destination_ref: str) -> dict:
"""
Streams a JSON string as text into a single row in a three column (all string) BigQuery table for subsequent decoding, with the following schema:

Args:
id_string (string): row identifier
json_string (string): data payload
destination_ref (string): BigQuery table destination reference (project_id.dataset_id.table_name)

Returns:
A dict containing
status (string): "success" for successful stream, "error" for a failed stream or "fail" for other failure.
message (string): Exception or error message if applicable
"""
try:
current_time = datetime.now()
current_timestamp = current_time.strftime("%Y-%m-%d %H:%M:%S.%f")

project_id = os.environ.get('GCP_PROJECT')
BQ = bigquery.Client(project=project_id)
table = BQ.get_table(destination_ref)

rows_to_insert = [(id_string, json_string, current_timestamp)]

errors = BQ.insert_rows(
table, rows_to_insert, row_ids=[None] * len(rows_to_insert)
)

stream_log: dict
if len(errors) == 0:
stream_log = {"status": "success", "message": "no errors"}
else:
error_string = json.dumps(errors)
stream_log = {"status": "error", "message": error_string}

except Exception as e:
stream_log = {"status": "fail", "message": e}

return stream_log
```

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