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Begin by logging into your Retently account. Navigate to the data or report section you wish to export. Retently typically allows you to export data as CSV or Excel files. Choose the export option and save the file to your local machine.
Open the exported file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data for any inconsistencies, missing values, or errors. Clean and format the data to ensure it matches the schema that you will define in BigQuery.
Log into your Google Cloud Platform account and navigate to BigQuery. Before importing the data, define a schema that matches the structure of the data in your file. This involves specifying field names, data types (e.g., STRING, INTEGER, FLOAT), and any other constraints or nullability considerations.
If necessary, make any final adjustments to your data file to match the BigQuery schema. Save the cleaned and formatted file as a CSV, as this is a commonly supported format for BigQuery imports.
Access Google Cloud Storage through your Google Cloud Platform account. Create a new bucket if you don"t have one already. Upload your CSV file to this bucket, which will serve as a staging area before importing it to BigQuery.
In the BigQuery console, select the dataset where you want to load the data. Use the "Create table" option and choose "From Google Cloud Storage" as the source. Specify the path to your file in the bucket, and configure the table settings, ensuring that the schema matches what you defined earlier. Execute the load job to import the data into BigQuery.
Once the data is loaded, run a simple query in BigQuery to verify that the data has been imported correctly. Check for completeness and accuracy by comparing a sample of the imported data to the original file. Make any necessary adjustments by reloading the data if discrepancies are found.
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.
Retently is a tool for measuring and increasing customer satisfaction and loyalty through Net Promoter Score surveys and collecting feedback and The tool is packed with various robust features to help you segment your audience, create custom polls, and collect multichannel polls. With Retently, businesses can collect customer feedback and analyze the results with advanced analytics and reports for corrective action. Retently's cloud-based platform is designed to help businesses track their Net Promoter Score, collect valuable customer reviews, and build customer loyalty by converting detractors into repeat customers.
Retently's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Retently's API include:
1. Customer feedback data: This includes data related to customer feedback, such as NPS scores, customer comments, and ratings.
2. Customer satisfaction data: This includes data related to customer satisfaction, such as customer satisfaction scores, customer loyalty, and customer retention rates.
3. Customer behavior data: This includes data related to customer behavior, such as customer purchase history, customer demographics, and customer preferences.
4. Campaign data: This includes data related to Retently's campaigns, such as campaign performance metrics, campaign engagement rates, and campaign conversion rates.
5. User data: This includes data related to Retently's users, such as user activity, user preferences, and user engagement.
Overall, Retently's API provides access to a wide range of data related to customer feedback and satisfaction, which can be used to improve customer experience and drive business growth.
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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