How to load data from SurveyMonkey to Kafka

Learn how to use Airbyte to synchronize your SurveyMonkey data into Kafka within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a SurveyMonkey connector in Airbyte

Connect to SurveyMonkey or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Kafka for your extracted SurveyMonkey data

Select Kafka where you want to import data from your SurveyMonkey source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the SurveyMonkey to Kafka in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync SurveyMonkey to Kafka Manually

How to Sync SurveyMonkey to Kafka Manually - Method 2:

FAQs

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.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up SurveyMonkey to Kafka as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from SurveyMonkey to Kafka and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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.

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