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Begin by logging into your SurveySparrow account. Navigate to the survey dashboard where you can view your collected responses. Use the export feature to download the survey data as a CSV file. Ensure that you have the necessary permissions to export data and that you save the file in a secure, accessible location on your local machine.
Open the CSV file using a spreadsheet software like Microsoft Excel or Google Sheets. Check the data format and clean any discrepancies such as missing values or incorrect data types. Make any necessary adjustments to ensure consistency and accuracy. Save the cleaned data as a CSV file again.
Log into your Databricks account and create a new workspace if you do not already have one. Ensure that your environment is set up with the necessary compute resources. You may need to configure a cluster that can handle the data import and processing tasks.
Access the Databricks workspace and navigate to the “Data”� section. Utilize the file upload feature to transfer the CSV file from your local machine to the Databricks File System (DBFS). This step involves using the Databricks UI to locate and upload your file, ensuring it is stored in a directory accessible for further processing.
In the Databricks notebook, write the necessary Spark SQL commands to create a Delta table. Use the DataFrame API to read the CSV file from DBFS and define the schema based on the structure of your survey data. Execute the commands to create a Delta table where the survey data will be stored.
Utilize PySpark or Scala within the Databricks notebook to load the data from the CSV file into the Delta table. This involves reading the CSV data into a DataFrame and using the `write` method to insert the data into the Delta table. Ensure that you handle any data conversion or type casting as needed to match the Delta table schema.
Once the data is loaded, perform a series of queries on the Delta table to verify that the data has been transferred correctly. Check for data integrity and completeness. After verification, clean up any temporary files in DBFS to maintain a tidy workspace. Document the process for future reference or replication.
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.
SurveySparrow is an online survey tool which permits users to create and distribute customer surveys through multiple channels, along with evaluate responses and it is also an experience management platform on a mission to assists businesses refine experiences end to end Conversational Experience Management Platform that helps you get a 40% better response rate. SurveySparrow supports you measure employee motivation by using surveys specially made for them. One can easily measure how engaged they are and their job satisfaction.
SurveySparrow's API provides access to a wide range of data related to surveys and responses. The following are the categories of data that can be accessed through SurveySparrow's API:
1. Survey data: This includes information about the surveys created on the platform, such as survey title, description, and status.
2. Response data: This includes information about the responses received for each survey, such as response ID, respondent email, and response timestamp.
3. Question data: This includes information about the questions asked in each survey, such as question type, question text, and answer options.
4. User data: This includes information about the users who have access to the surveys, such as user ID, email, and role.
5. Analytics data: This includes information about the survey performance, such as response rate, completion rate, and average time taken to complete the survey.
6. Integration data: This includes information about the integrations used with SurveySparrow, such as the API key and endpoint URL.
Overall, SurveySparrow's API provides comprehensive access to all the data related to surveys and responses, enabling users to analyze and utilize the data for various purposes.
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:
