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Begin by understanding the data you need to transfer from Google Web Fonts. This includes identifying font metadata, such as font names, styles, weights, and any usage data you may have collected.
Since Google Web Fonts does not provide a direct export feature, you will need to manually collect the data. Access the Google Web Fonts API or use the Google Fonts Developer API to pull down the JSON metadata for each font you are interested in. Save this data locally in a structured format like JSON or CSV.
Set up a local directory structure on your system to store the exported font data. Organize the data in a way that aligns with your intended Iceberg table schema. This might involve creating separate directories for different font styles or metadata categories.
Ensure you have an Apache Iceberg environment set up. This involves having a compatible data processing engine like Apache Spark or Flink. Configure your environment to handle Iceberg tables. This may require installation and configuration of necessary libraries and dependencies.
Design the schema for your Iceberg table based on the structure of your Google Web Fonts data. This typically involves mapping JSON fields to table columns, considering data types, and defining any required partitioning strategies to optimize query performance.
Write a script or use a data processing engine to transform your local data into a format suitable for Apache Iceberg. This generally means converting JSON or CSV files into Parquet or Avro format, ensuring that data types align with your Iceberg table schema.
Use your data processing engine to load the transformed data into your Iceberg table. This involves writing a job or script to read the Parquet or Avro files and insert them into the Iceberg table. Ensure that the data is correctly partitioned and indexed according to your schema design for efficient querying.
By following these steps, you can manually move data from Google Web Fonts to Apache Iceberg 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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
