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Start by scraping or exporting the required data from Google News. You can use Python libraries such as BeautifulSoup or Requests to scrape news articles. Ensure you comply with Google's terms of service while scraping data. Collect data such as article title, content, publication date, and URL.
Once the data is extracted, structure it into a suitable format such as CSV or JSON. This involves organizing the data into rows and columns if using CSV, or key-value pairs for JSON. This structured format will make it easier to load into Databricks.
Log into your Databricks account and create a new workspace if you haven't already. Make sure your workspace has access to a data storage location like AWS S3, Azure Blob Storage, or Google Cloud Storage.
Move the structured data file(s) to a cloud storage service that is accessible by Databricks. Use the cloud provider's command-line tools or web interface to upload the files. For example, you can use the AWS CLI to upload files to an S3 bucket.
In your Databricks workspace, configure a connection to your cloud storage. This typically involves setting up credentials or IAM roles so that Databricks can read from your storage bucket. Use the Databricks UI or the Databricks CLI to configure these settings.
Use the Databricks platform to load your data. You can write a notebook in Databricks using PySpark or SQL to read the data from your cloud storage into a DataFrame. For instance, use the `spark.read.format("csv").load("s3a://your-bucket/your-file.csv")` command to load a CSV file.
Once the data is loaded into a DataFrame, perform any necessary transformations such as cleaning, filtering, or aggregating the data. Finally, write the transformed DataFrame to the Databricks Lakehouse. You can use the `write` method to store data in the desired format (e.g., Delta Lake format) and location within the Lakehouse.
By following these steps, you can efficiently move data from Google News to a Databricks Lakehouse 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.
GNews stands for Google News which is a news notification program for the Google Chrome internet browser. It is a personalized news aggregator that organizes and highlights what's happening in the world so you can discover more about the stories. Google News assists you organize, find, and understand the news. You can change your settings to find more stories you want. Google News helps you organize, find, and understand the news.
Google News API provides access to a wide range of data related to news articles and sources. The following are the categories of data that can be accessed through the API:
1. Articles: The API provides access to news articles from various sources, including the title, description, author, and publication date.
2. Sources: The API allows users to retrieve information about news sources, including the name, description, and URL.
3. Topics: The API provides access to news articles based on specific topics, such as sports, politics, and entertainment.
4. Locations: The API allows users to retrieve news articles based on specific locations, such as cities, states, and countries.
5. Languages: The API provides access to news articles in different languages, including English, Spanish, French, and German.
6. Images: The API allows users to retrieve images related to news articles, including the image URL and caption.
7. Videos: The API provides access to news videos from various sources, including the video URL and description.
Overall, the Google News API provides a comprehensive set of data related to news articles and sources, making it a valuable resource for developers and researchers.
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:
