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Begin by exporting your survey data from SurveyMonkey. Log in to your SurveyMonkey account, navigate to the survey you want to export, and select the “Analyze Results”� tab. Click on “Export Results”� and choose a format such as CSV or Excel, which are easy to manipulate and import into other systems. Download the file to your local machine.
Open the exported CSV or Excel file to review and clean the data. Make sure the data is structured properly, with clear headers and consistent data types. Convert any necessary fields to match Firestore data types, like changing text representations of dates into standard timestamp formats.
Go to the Google Cloud Console and create a new project if you don’t have one already. Ensure that the Firestore API is enabled for this project. You can do this by navigating to the “APIs & Services”� > “Library”� and enabling Firestore.
Set up Firestore by navigating to the Firestore section in the Google Cloud Console. Choose between Native and Datastore mode based on your project’s needs (Native mode is recommended for new projects). Set up your Firestore database by choosing a location and clicking “Create Database.”�
Using a programming language like Python, write a script to read the CSV/Excel file. Libraries such as `pandas` can help manage data frames efficiently. Your script should parse each row and prepare it for insertion into Firestore. This may involve creating dictionaries where keys are the column headers, and values are the corresponding cell data.
Set up authentication to allow your script to interact with Firestore. Create a service account in the Google Cloud Console with Firestore permissions. Download the JSON key file and use it in your script to authenticate. In Python, this can be done using the `google-cloud-firestore` library by setting the environment variable `GOOGLE_APPLICATION_CREDENTIALS` to the path of your JSON key file.
Use the Firestore client library in your chosen programming language to upload the parsed data. For each row of data, create a document in a specified Firestore collection. Use a loop to iterate over the data and insert each row, handling any potential exceptions to ensure all data is uploaded successfully. Verify the data integrity by checking a few entries manually in the Firestore console.
By following these steps, you can successfully transfer data from SurveyMonkey to Google Firestore 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.
Survey Monkey uses the power of the web to enable individuals and companies to reach unprecedented numbers of respondents to gain insights into almost anything. An experience management company, Momentive Inc. (formerly SurveyMonkey Inc.) uses a cloud-based software to provide service solutions for businesses and individuals needing brand or market insights, information regarding consumers’ product experiences, employee and customer experiences—information of any kind for which surveys can provide useful information to improve products, events, experiences.
SurveyMonkey'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 SurveyMonkey's API:
1. Survey data: This includes information about the survey itself, such as the survey title, description, and questions.
2. Response data: This includes information about the responses to the survey, such as the respondent's answers to each question.
3. User data: This includes information about the users who created the survey, such as their name, email address, and account type.
4. Team data: This includes information about the teams that the user belongs to, such as the team name and members.
5. Template data: This includes information about the survey templates available on SurveyMonkey, such as the template name and description.
6. Collector data: This includes information about the collectors used to distribute the survey, such as the collector type and status.
7. Analytic data: This includes information about the survey results, such as the response rate, completion time, and average score.
Overall, SurveyMonkey's API provides access to a comprehensive set of data related to surveys and responses, which can be used to gain insights and make data-driven decisions.
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?
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