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First, determine which Google Web Fonts you need to download. Visit [Google Fonts](https://fonts.google.com/) and select the fonts you want. Make a note of the font names and their variations (e.g., regular, bold, italic).
Go to the Google Fonts website, select your desired fonts, and add them to your collection. Once you've selected all the fonts you need, click on the "Download" button. This will download a ZIP file containing all the font files and CSS needed for implementation.
After downloading the ZIP file, extract its contents to a directory on your local machine. You should now have the font files (usually in .ttf, .woff, or .woff2 formats) and a CSS file that includes the font-face declarations.
Log into your AWS Management Console and navigate to S3. Create a new bucket to store your fonts, ensuring that the bucket name is unique across all of AWS. Configure the bucket settings, including permissions, to allow public access if you plan on serving the fonts directly from S3.
Use the AWS Management Console to upload the extracted font files to the newly created S3 bucket. Navigate to the bucket, click on the "Upload" button, and select the font files from your local directory. Ensure that each file retains its original directory structure if needed.
After uploading, you'll need to set permissions on these files so they can be publicly accessible. Go to the permissions tab for each file and set the "Public" access option on the permissions tab. Alternatively, you can set bucket policies to make all files within the bucket publicly accessible.
Replace the font URLs in your CSS file with the URLs pointing to your S3 bucket. Each font file's URL should follow the pattern `https://[your-bucket-name].s3.amazonaws.com/[font-file-path]`. Update your website's CSS file to use these new URLs so that your fonts are now served from your S3 bucket.
By following these steps, you can successfully move your Google Web Fonts to an Amazon S3 bucket without using 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.
What should you do next?
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