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Determine the specific data you want to move from Google Web Fonts. This could be metadata about the fonts, usage statistics, or other available data through their API. Ensure you have access to this data, possibly via the Google Fonts Developer API.
If you haven't already, set up a Google Cloud Project. Go to the Google Cloud Console, create a new project, and note the Project ID. This project will be used to set up Pub/Sub and any necessary APIs.
In your Google Cloud Project, enable the Google Fonts Developer API (if available and applicable) and Google Pub/Sub API. This can be done by navigating to the "APIs & Services" section in the Google Cloud Console and searching for these APIs to enable them.
Write a script or program to extract the desired data from Google Web Fonts. You can use a language like Python, JavaScript, or any language that can make HTTP requests. If using Python, for example, use the `requests` library to interact with the Google Fonts API and parse the JSON data received.
In the Google Cloud Console, navigate to the Pub/Sub section and create a new topic. This topic will be the destination for the data you are moving. Note the topic name, as you will use it when publishing messages.
Extend your script to publish the extracted data to Google Pub/Sub. Use the Google Cloud Client Libraries for your chosen language to authenticate and publish messages. For Python, you would typically use the `google-cloud-pubsub` library, authenticate using a service account, and publish messages to the topic created in the previous step.
Ensure that the data has been successfully published to Google Pub/Sub. You can do this by subscribing to the topic with a new subscription and pulling messages to verify their content. Use the Google Cloud Console or write a script to pull messages from the subscription and check if they match the data you intended to transfer.
By following these steps, you can extract data from Google Web Fonts and publish it to Google Pub/Sub without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
The Google Web Font service, which is an ever-growing depository of fonts, all are available to use for free on the web, through Open Source Licensing. Whilst it is not the only platform available to provide typefaces to your site, it does have the largest free selection out there. A web font is any font used in a website's design that isn't installed by default on the end user's device a counterpart to a system font.
Google Webfonts API provides access to various types of data related to web fonts. The API allows developers to integrate web fonts into their websites and applications. The following are the categories of data that the Google Webfonts API provides access to:
1. Font families: The API provides access to a wide range of font families that can be used on websites and applications.
2. Font variants: The API provides access to different font variants such as regular, bold, italic, and bold italic.
3. Font subsets: The API provides access to different font subsets such as Latin, Cyrillic, and Greek.
4. Font metadata: The API provides access to metadata related to fonts such as font name, designer, and license information.
5. Font metrics: The API provides access to font metrics such as line height, letter spacing, and font size.
6. Font rendering: The API provides access to font rendering options such as anti-aliasing and sub-pixel rendering.
Overall, the Google Webfonts API provides developers with a comprehensive set of data related to web fonts that can be used to enhance the typography of their websites and applications.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
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