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Begin by logging into your SurveyMonkey account. Navigate to the survey you wish to export data from. Use the "�Export' option to download your survey responses. Choose a CSV format for easy handling and compatibility with PostgreSQL.
Open the exported CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data for any inconsistencies or errors. Ensure that the column names are formatted correctly and are suitable as PostgreSQL table columns. Save the cleaned file, ensuring it remains in CSV format.
If not already done, install PostgreSQL on your machine or server. Initialize a database that will store your SurveyMonkey data. Create a new user with permissions to create tables and insert data. For example, use the following command in the PostgreSQL shell:
```sql
CREATE USER surveyuser WITH PASSWORD 'yourpassword';
CREATE DATABASE surveydb;
GRANT ALL PRIVILEGES ON DATABASE surveydb TO surveyuser;
```
Open the PostgreSQL command-line interface or a graphical tool like pgAdmin. Connect to your `surveydb` database. Create a table with columns that match those in your CSV file. Define appropriate data types for each column. For example:
```sql
CREATE TABLE survey_results (
id SERIAL PRIMARY KEY,
respondent_id INTEGER,
question_1 VARCHAR(255),
question_2 VARCHAR(255),
...
);
```
Use the PostgreSQL `COPY` command to import data from the CSV file into the newly created table. This command reads data from a file and writes it to a table. Execute the following command, adjusting file paths and table names as necessary:
```sql
COPY survey_results FROM '/path/to/your/file.csv' DELIMITER ',' CSV HEADER;
```
Ensure the file path is correct and accessible from the PostgreSQL instance.
After importing, verify that the data has been correctly transferred. Run `SELECT` queries to inspect the data in your PostgreSQL table. Check for any discrepancies or missing entries that may need manual correction.
If this data transfer is to be performed regularly, consider writing a script in a language like Python or Bash to automate steps 1 through 6. Use PostgreSQL libraries (like `psycopg2` for Python) to handle database connections and operations programmatically. This script can be scheduled using cron jobs or Windows Task Scheduler for periodic execution.
By following these steps, you can manually transfer data from SurveyMonkey to a PostgreSQL destination 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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: