SurveyMonkey is a popular online tool that allows users to collect feedback from prospects and customers about their experiences. It’s a user-friendly platform that offers a wide range of features and templates for expediting the survey development process. Businesses can benefit from connecting their survey data with a Data Warehouse for storage and analysis.
Amazon Redshift is one such Data Warehouse that can store and manage petabytes of data. You can connect SurveyMonkey to Redshift and get insights into your survey audience.
This blog will give you an overview of SurveyMonkey and Redshift, along with their features and steps to connect SurveyMonkey to Redshift. You can load data from SurveyMonkey to Redshift for storage and analysis in 2 simple steps. Read along to understand better the step-by-step procedure to replicate data from SurveyMonkey’s to Redshift manually.
We recommend you understand the basics of AWS S3 to get the most out of this article.
What is SurveyMonkey?
SurveyMonkey is a cloud-based tool used for creating, sending, and analyzing surveys. You can email surveys to participants or post them on their websites and social media profiles to increase the response rate.
Companies can run employee engagement surveys to understand employee satisfaction within the company or customer satisfaction surveys to get feedback on products and services. With SurveyMonkey, you can also send surveys and check results from a mobile device.
SurveyMonkey offers three plans: Individual, team, and team premier. It also offers an enterprise-grade solution.
Features of SurveyMonkey
- Question Bank: Not everyone can come up with the right questions. SurveyMonkey has a question bank filled with hundreds of questions. You can browse various categories to find the right questions. Users can edit questions according to their preferences. Some questions are benchmarkable, meaning you can compare that question’s response data against other SurveyMonkey users who used that question in their surveys.
- Survey Logic: Unless a survey is relevant to the user, they’ll leave your survey or answer questions dishonestly. That’s where survey logic can help to improve the user experience. There are three most valuable types of survey logic in SurveyMonkey:
- Question Skip Logic: This allows respondents to skip a specific page or question on another page based on their answers to the previous question.
- Advanced Branching: You can apply ‘skip logic’ based on various “conditions.”
- Question & Answer Piping: You can personalize surveys by plugging respondents’ previous answers into future question prompts. It shifts the survey process to a more personal conversation.
- Recurring Surveys: With periodic surveys, users can regularly share the same survey over a given period. Periodically surveying employees, prospects, customers, and other audiences will offer a timely understanding of their experiences. This tells companies when the engagement or customer experience is dropping and take the appropriate steps to improve the engagement.
- Survey Reminders: People have hectic schedules, and survey reminders are the solution to overcoming this. With SurveyMonkey, you can politely remind people to take the survey at the first touchpoint. You can use the email collector option in SurveyMonkey to send reminders.
- SurveyMonkey Genius: With SurveyMonkey Genius, users can get survey design feedback irrespective of the stage of the survey. It is a Machine Learning and AI-powered tool that provides insights like estimated completion rate, the balance of right question types, and the time to finish the survey.
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What is Redshift?
Amazon Redshift or AWS Redshift is a petabyte-scale, fully managed cloud-based Data Warehouse for storing, migrating, and analyzing large-scale data sets. Redshift’s Column-oriented Database can connect Business Intelligence tools with SQL-based clients, offering real-time data to users. It is based on PostgreSQL and can deliver efficient querying and fast performance to help teams make sound business decisions.
Every Data Warehouse has a collection of computing nodes, and every cluster has at least one Database and Engine. Although Redshift can manage petabytes of data, users can start with a few gigabytes of data. Redshift quick performance comes from two architectural elements: Massively Parallel Processing design (MPP) and Columnar Data Storage.
Redshift has no substantial upfront costs, recurrent hardware, and maintenance costs. Database admins can set up Data Warehouses that can handle massive amounts of data without going through the lengthy procurement process.
Features of Redshift
- Columnar-oriented Databases: Data can be organized into rows or columns. The most common method is Row-oriented systems, which process a lot of small operations like online transaction processing. Column-oriented Databases allow for increased speed in accessing large amounts of data. Redshift uses a Column-oriented Database, where users apply a smaller number of queries to larger datasets.
- Massively Parallel Processing (MPP): Redshift employs a distributed design approach in which several processors use a “divide and conquer” strategy for large data jobs. It organizes a large processing job into smaller jobs and then distributes them among a cluster of processors. Each processor completes its computations simultaneously, reducing Redshift’s time to complete one enormous job.
- Encryption: Encryption is a critical component of data security for any business or organization. They must comply with GDPR, HIPAA, the Sarbanes-Oxley Act, and the California Privacy Act. Redshift offers robust and highly customizable encryption that allows users to configure a standard that best fits their needs. Redshift has the following security encryption features:
- Choosing between AWS-managed or a customer-managed key
- Migrating data between unencrypted and encrypted clusters
- The choice between AWS Key Management Service or Hardware Security Module
- Single or Double encryption
- Fault Tolerance: Fault tolerance is the ability of a system to continue operating even when some components (or clusters) fail. It determines the capacity of a job to continue running when some clusters go offline. AWS monitors its clusters around the clock, and Redshift can automatically re-replicate data and shift it to healthy nodes in the event of drives, nodes, or cluster failure.
- Concurrency Limits: Concurrency limits determine the maximum number of clusters users can provision at any time. These limits are helpful to ensure that every user has adequate computing resources. Redshift maintains concurrency limits but with a degree of flexibility. There are no single limits for every user. Instead, Redshift configures limits based on regions, and lets users submit a limit increase request.
How to Connect SurveyMonkey to Redshift?
You can export data from SurveyMonkey to Redshift for storage and analysis. Here are the steps to perform SurveyMonkey Redshift Integration:
Step 1: Exporting Data From SurveyMonkey
You can Export survey data from SurveyMonkey and get an offline copy of your survey results. You can access your data from the Analyze Results section by clicking the Exports Icon. All the exports will be downloaded to your computer’s Downloads folder.
Follow these steps to export all SurveyMonkey results:
- Go to the Analyze Results section of the survey you want to export from your SurveyMonkey account.
- Click Save As.
- Click Export file.
- Before exporting the file, you can select an export type: All responses data, All summary data, or All individual responses.
- You also have the option to choose the export format.
- After selecting the export format and type, Click Export.
Step 2: Uploading Data to Redshift
You can load data to Redshift by leveraging MPP architecture from files on Amazon S3, DynamoDB table, or from text output from remote hosts. You can easily and quickly load large amounts of data using the COPY command.
Uploading Data to Amazon S3
In Amazon S3, a Bucket is a container storing data like documents, photos, videos, etc. Users can have up to 100 Buckets in their account and upload any number of objects. You can also request to increase the Bucket quota.
Start by creating an Amazon S3 Bucket that can hold your data from SurveyMonkey and then upload your downloaded file to your system. You can select a specific region for creating an Amazon S3 Bucket or specify an endpoint using CLI. You can manage stored objects via Amazon S3 API or Amazon S3 console.
Loading Data From Amazon S3 to Amazon Redshift
Follow the steps below to load data from SurveyMonkey to Redshift:
- Use the CREATE TABLE command to create a sample table in Redshift that can hold your data. You can provide parameters according to the data you have from SurveyMonkey.
- The COPY command copies the data from the Amazon S3 Bucket to Amazon Redshift.
You will have to provide the following value: Table name, Column list, Data source, and Credentials.
COPY table_name [ column_list ] FROM data_source CREDENTIALS access_credentials [options]
The COPY command is a better alternative to INSERT commands since it uses parallel processing to load data from SurveyMonkey to Redshift quickly.
Limitations of Connecting SurveyMonkey to Redshift
There is no direct way to connect SurveyMonkey to Redshift and move the data for analysis. Companies must first Export data from SurveyMonkey and import it into the Amazon S3 Bucket. After the data is in S3, companies can load it into Amazon Redshift.
However, this process is time-consuming and doesn’t offer real-time data transfer. Manually handling and replicating Big Data from SurveyMonkey to Redshift can also lead to Data Quality issues like duplicate data, human error, and more. Consequently, businesses should use third-party ETL tools to connect SurveyMonkey to Redshift for real-time and quick data transfer.
This blog provides you with an overview of SurveyMonkey and Redshift along with their features before delving into the steps for connecting SurveyMonkey to Redshift. Connecting SurveyMonkey to Redshift helps companies analyze the data collected from surveys of their customers or employees through BI tools or Machine Learning techniques.
As a result, going beyond the analysis capabilities of SurveyMonkey can give you an edge in the competitive market. Moving your data from SurveyMonkey to Redshift using the manual method is time-consuming and requires much effort. This is where Hevo Data comes into the picture.
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You can share your learning experience on SurveyMonkey Redshift Integration in the comments section below.