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Begin by accessing Trustpilot's API documentation. Identify the endpoints that provide the data you need. Use Python or a similar programming language to write a script that sends HTTP GET requests to these API endpoints. Parse the JSON response and extract the required data fields.
Once you have extracted the data, clean and transform it locally on your machine. This may involve filtering out unnecessary fields, handling missing values, and converting data types to match the schema you plan to use in the Databricks Lakehouse. Use libraries like pandas in Python to assist in data transformation.
Log in to your Databricks account and create a new cluster if needed. Ensure your cluster is configured correctly with the necessary resources and libraries, such as PySpark, to handle the data processing.
Save the transformed data into a format suitable for uploading to Databricks. Common formats include CSV, JSON, or Parquet. Compress the file if necessary to optimize for faster upload speeds.
Utilize the Databricks CLI or the Databricks web interface to upload your data file to the Databricks File System (DBFS). If using the CLI, execute the appropriate command to copy your local file to DBFS, ensuring you specify the correct path.
In Databricks, create a new notebook or use an existing one. Utilize PySpark or SQL to load the data from DBFS into a Delta Lake table, which is part of the Lakehouse architecture. Define the schema and execute the appropriate commands to read the file and write it into a Delta table.
Once the data is loaded, perform checks to ensure that the data integrity and consistency are maintained. Write queries to validate row counts, check for data duplication, and ensure that data types and values are as expected. This step is crucial to confirm the accuracy of the data migration process.
By following these steps, you can successfully move data from Trustpilot to the 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.
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.
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