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Begin by logging into your SurveyMonkey account and navigating to the survey whose data you wish to export. Use the 'Results' tab to select the data set. Choose the 'Export' option, typically found at the top right corner, and select a format compatible with your needs, such as CSV or Excel. Download the file to your local system.
Open the downloaded file to inspect the data. Clean and format the data as necessary. This involves removing any unwanted columns, renaming columns to adhere to Snowflake naming conventions, and ensuring the data types are consistent and suitable for Snowflake requirements (e.g., dates in a recognizable format).
If you haven't already, create a Snowflake account. Once logged in, set up a new database where you will store the SurveyMonkey data. Within this database, create a schema that will house your tables. Ensure you have the necessary permissions to load data into Snowflake.
Use Snowflake’s SQL editor to create a table that matches the structure of your prepared data. Define the table's schema, ensuring the columns and data types align with those in your CSV or Excel file. For example:
```sql
CREATE TABLE survey_data (
response_id STRING,
question1 STRING,
question2 STRING,
timestamp TIMESTAMP
);
```
Snowflake requires data to be loaded from a stage. Use the Snowflake web interface or SnowSQL (Snowflake's command-line tool) to create a stage. Upload your CSV or Excel file to this stage using the PUT command in SnowSQL:
```bash
PUT file://path_to_your_file.csv @your_stage;
```
Once the data file is in a Snowflake stage, use the COPY INTO command to load the data from the stage into your target table. Ensure the file format options in the command match your file's format:
```sql
COPY INTO survey_data
FROM @your_stage/file.csv
FILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY = '"' SKIP_HEADER = 1);
```
After executing the COPY INTO command, verify that the data has been loaded correctly. You can do this by running a simple SELECT query on your Snowflake table:
```sql
SELECT FROM survey_data;
```
Check that all rows and columns have been imported as expected. This step ensures the integrity and accuracy of your data transfer process.
Following these steps will allow you to transfer data from SurveyMonkey to Snowflake without using third-party connectors or integrations, ensuring a direct and reliable data migration process.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: