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Begin by familiarizing yourself with Trustpilot's API documentation. Trustpilot provides several APIs that allow you to extract data. You'll need to obtain an API key by creating a Trustpilot business account and registering an application to access these APIs.
Write a script in a programming language of your choice (Python is commonly used for such tasks) to extract data from Trustpilot using their APIs. Use HTTP requests to fetch the data, and ensure you handle pagination if Trustpilot limits the number of results per request.
Once data is extracted, transform it into a format suitable for BigQuery. CSV or JSON formats are typically used. Ensure that your data structure aligns with the schema you plan to use in BigQuery, making necessary transformations to match data types and field names.
Set up a Google Cloud account if you don’t have one. Create a new project in Google Cloud Console, and enable the BigQuery API. Ensure that you have the necessary permissions and billing set up to use BigQuery.
Before loading data into BigQuery, upload it to Google Cloud Storage (GCS). Use the `gsutil` command-line tool or Google Cloud Console to create a bucket and upload your CSV or JSON files. Ensure the files are named appropriately for easy identification.
Use the BigQuery Console, `bq` command-line tool, or a custom script to load data from Google Cloud Storage into BigQuery. Create a dataset and table in BigQuery that matches your data schema, and execute the load job. Monitor the job for any errors and ensure data is correctly loaded.
To keep your BigQuery data updated, automate the entire process. Use a cron job or a cloud function to schedule regular data extraction and loading. Ensure your script handles exceptions and logs errors for troubleshooting. This will help maintain data consistency and integrity over time.
By following these steps, you can effectively move data from Trustpilot to BigQuery 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.
TrustPilot is an online review platform that allows customers to share their experiences and opinions about businesses they have interacted with. The platform provides a space for customers to leave reviews and ratings, which can help other potential customers make informed decisions about whether to use a particular business or not. TrustPilot also offers businesses the opportunity to respond to reviews and engage with customers, helping to build trust and improve their reputation. The platform is used by millions of people worldwide and covers a wide range of industries, from retail and hospitality to finance and healthcare.
TrustPilot's API provides access to a wide range of data related to customer reviews and ratings. The following are the categories of data that can be accessed through TrustPilot's API:
1. Reviews: TrustPilot's API provides access to all the reviews submitted by customers, including the text of the review, the rating given, and the date of submission.
2. Ratings: The API also provides access to the overall rating of a business, as well as the individual ratings for different aspects of the business, such as customer service, product quality, and delivery.
3. TrustScore: TrustPilot's TrustScore is a measure of a business's overall reputation based on customer reviews. The API provides access to this score, as well as the factors that contribute to it.
4. Business information: The API provides access to information about the business, such as its name, address, and website.
5. Reviewer information: The API also provides access to information about the reviewers, such as their name, location, and the number of reviews they have submitted.
6. Analytics: TrustPilot's API provides access to analytics related to customer reviews, such as the number of reviews submitted over time, the average rating, and the sentiment of the reviews.
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:
