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Begin by logging into your SurveyMonkey account. Navigate to the survey whose data you want to export. Click on the "Analyze Results" tab and then select "Export Results." Choose a file format that is compatible with AWS services, such as CSV or XLSX. Download the export file to your local machine.
Log into your AWS Management Console and navigate to the S3 service. Create a new S3 bucket if you don�t have one already. Ensure the bucket name is globally unique and follows AWS naming conventions. Configure the bucket settings, such as setting the appropriate permissions and enabling versioning if needed.
If not already installed, download and install the AWS Command Line Interface (CLI) on your local machine. The AWS CLI will allow you to interact with AWS services directly from your command line. Configure the CLI by running `aws configure` and inputting your AWS Access Key ID, Secret Access Key, region, and output format.
Open your command line interface and navigate to the directory where your exported SurveyMonkey data is located. Use the AWS CLI to upload the file to your S3 bucket with a command similar to the following:
```
aws s3 cp ./your-survey-data.csv s3://your-bucket-name/
```
Replace `your-survey-data.csv` with your actual file name and `your-bucket-name` with the name of your S3 bucket.
In the AWS Management Console, navigate to the AWS Glue service. Create a new Glue Crawler to catalog your data into a Glue Data Catalog. Specify your S3 bucket as the data source and run the crawler to detect the schema and create metadata tables in the Glue Data Catalog.
Navigate to AWS Lake Formation in the AWS Management Console. Register your S3 bucket as a data lake location by defining a new data lake and pointing it to your S3 bucket. Set up necessary permissions for users and services that will access the data.
Finally, use AWS Athena to query your data. Navigate to AWS Athena in the AWS Management Console and ensure it is set up to read from your AWS Glue Data Catalog. Use SQL queries to explore, transform, and analyze your SurveyMonkey data directly from the data lake.
By following these steps, you can effectively and securely transfer data from SurveyMonkey to an AWS Data Lake 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:
