Typeform helps businesses build interactive forms and surveys through which they can communicate with customers easily. Since it allows organizations to collect customer data with millions of users, it is essential to export data from Typeform and store it in Data Warehouses for further analysis. Snowflake is one such Data Warehousing service where you can store your Typeform data. You can use APIs or third-party ETL (Extract, Load, and Transform) tools to integrate your Typeform data to Snowflake.
In this article, you will learn to connect Typeform with Snowflake using APIs and manual processes. You will also learn about a fully-automated way to load data from Typeform to Snowflake!
Image credit: Typeform
Developed in 2012, Typeform is widely used for collecting and sharing information easily by creating interactive forms and surveys. In other words, Typeform is a platform where businesses can create visually appealing forms. Organizations use Typeform to build communication with customers through effective forms and surveys. With Typeform, customers can experience a real one-to-one conversation that keeps them engaged and entertained to complete the surveys.
Forms and surveys built with Typeform are interactive, attracting your customers to answer the questions and complete the surveys. Typeform allows users to store the collected data in various formats, including .csv and .xlsx, for further analysis.
- Interactive Designs: Typeform consists of interactive designs which attract customers to answer the questions and finish the surveys. It includes built-in designs, templates with drag and drop functionality, and colourful and user-friendly forms which engage the customer.
- Personalize Journeys: The Logic Jump is an intelligent feature of Typeform that prompts questions depending on the customer’s previous answers. It helps to collect data in less time by skipping irrelevant questions.
What is Snowflake?
Image credit: Snowflake
Developed in 2012, Snowflake is a fully managed Cloud Data Warehouse available to the customers in the form of Software-as-a-Service (SaaS) or Database-as-a-Service (DaaS). Users need not worry about backend or administrative work such as installation, maintenance, configuration, and more since they are performed automatically by Snowflake. With Snowflake, users can also choose a Cloud provider from Microsoft Azure, Amazon Web Service, and Google Cloud Storage to store their data.
One of the advantages of Snowflake Architecture is that it separates compute and storage units, thereby offering a flexible and attractive pricing structure. Since users don’t have to scale compute if they are scaling storage, they can pay for both units/resources independently.
Key Features of Snowflake
- Caching Results: Snowflake enables an essential feature that caches results at different levels, meaning the results can last for 24 hrs after the query is executed. Therefore, if the same query is executed, the results are quickly delivered.
- UNDROP Command: It might happen at times that users may delete the wrong tables. Therefore, to retrieve these tables, you can use the UNDROP command. Although you can use the traditional recovery and backup method, they are time-consuming. The UNDROP command is a better and quicker alternative.
- Data Sharing: The Data Sharing feature in Snowflake allows you to share items from one database account to another without creating duplicates. This feature guarantees more storage space and less computation, resulting in faster data accessibility.
You can connect Typeform with Snowflake using APIs, UI, or third-party ETL tools. In this article, you will learn to connect Typeform with Snowflake using APIs, UI, and Hevo.
Follow the below steps to connect Typeform with Snowflake using APIs.
Typeform provides several APIs that you can use to data from the websites. For example, to retrieve the form response, you can call GET using the below command.
https://api.typeform.com/forms/{form_id}/responses
By using the queries like above, you can get the sample data of Typeform.
If you do not have a data structure to store your collected data, you have to create a schema for the data. You need to identify the data type as Integer, String, DateTime, and more for each value in the response form and then build a table for each of them.
Step 3: Preparing Data for Snowflake
You need to prepare your data for loading depending on its structure. Go through the supported datatypes for Snowflake and ensure that you map them correctly.
Note: When loading JSON data into Snowflake, you do not need to define a schema in advance.
Step 4: Loading Data into Snowflake
To load your data, you can refer to the data loading overview section in Snowflake’s documentation. If you want to load data in small volumes, you can use the Snowflake UI. However, you can also use the two main commands to load data into Snowflake.
- Use the PUT command.
- Use the COPY INTO table command to load prepared data into an awaiting table.
You can get the script for the data you want to retrieve and successfully load it to the Data Warehouse at this stage. But getting this data is not enough, as you need to update it. However, when you update this data, your existing data can be replicated. As a result, this process is resource-intensive and requires a lot of time.
Therefore, it becomes essential to build in a way that it can identify incremental updates to your data. Typeform consists of API results, with fields like submitted_at, allowing you to identify records since your last update. Once you have taken your new or updated data into a Snowflake account, you can set up the continuous loop for pulling down new data as it appears.
You can connect Typeform to Snowflake manually using the User Interface of Typeform and Snowflake.
Exporting Typeform data consists of two sub-steps: export Typeform data and download Typeform data. Follow the below steps to get started:
- Go to the Account tab in the top right corner and then click on settings.
Image credit: Typeform
- You are in your Typeform account now. Click on the Request Data tab. Typeform can email you a zip file containing your Typeform data in JSON files.
- Unzip these JSON files.
Image credit: Typeform
- You can now download the results of your Typeform into an Excel sheet or CSV file. From your workspace, open your Typeform and click on Results.
Image credit: Typeform
- Click on the Responses that can open a new panel. Now, click on the Download all Responses tab in the top right.
Image credit: Typeform
- It allows you to download your results in CSV or XLS format. Choose your preferred option, and then click on Download.
Image credit: Typeform
- Your results can then be downloaded to your browser’s default download folder. While designing forms or surveys using Typeform, you can enable the File Upload option, where your respondents can also upload files as responses. You can download all files your respondents have sent you by clicking on the Download all responses tab. It can then show you the option to download all the files your respondents uploaded, as shown below.
Step 2: Importing Data to Snowflake
After obtaining the data from Typeform, you can import the CSV files containing the forms and survey data into the Snowflake account using the below steps.
Follow the below steps to open the data wizard to load the data.
- Click on Databases. Click on the link of a specific database to view the objects stored in that database.
- Click on the Tables tab.
- Click on the table row to select data and then click on the Load Data tab or click on the table name to open the Tables details page, then click on the Load Data tab.
The Load Data wizard opens and loads the data into the table you selected.
B) Select your Data Warehouse
- From the drop-down list, select your warehouse. This list includes any warehouse to which you can have the USAGE privilege. Snowflake can use this warehouse for loading data into the table.
- Click on the Next tab.
- Select the source files.
Using the steps below, you can load your data from your local machine or existing files in Amazon S3, Snowflake, Microsoft Azure, or Google Cloud Storage.
C) Loading data from Cloud Storage
You can also load your data from the existing files on the Cloud by following the below steps.
- Select the existing name Stage from the drop-down list.
- Click on the Next tab to select the format for the file.
D) Creating a New Stage
If you do not have existing files on your Cloud, you can create a new stage in the Cloud by following the below steps.
- Click on the ‘+’ sign beside the Stage drop-down list.
- Select the location where your files are located, like Snowflake or any other supported Cloud Storage services. Then click on the Next tab.
- You can follow the Create Stage section to describe your Cloud Storage location.
- Click on the Finish tab.
- From the Stage drop-down list, select your new named Stage.
- Click on the Next tab to select the format for the file.
A drop-down list allows you to select a named set of options describing the format of your data files.
Choosing the Existing Named File Format
- Select the existing named file format from the drop-down list.
- Click on the Next tab to select the data load options.
If you do not have any files describing your file format, you can create a new file for your required format by following the steps below.
- Click on the ‘+’ sign beside the drop-down list.
- According to your file format, fill in the relevant information. You can follow Create File Format for more details.
- Click on the Finish tab.
- Select your named file format from the drop-down list.
- Click on the Next tab to select the data load options.
G) Selecting Data Load Options
- You need to specify how Snowflake should behave if there are errors in your data files. You can check the COPY INTO table section for more information about errors.
- Click on the load tab.
Snowflake can then load the data into your selected table of the Data Warehouse you chose. If the warehouse is not running, you might need to resume the warehouse, which can take time to load.
Limitation of Manual Methods for connecting Typeform to Snowflake
- Connecting Typeform to Snowflake directly leveraging APIs is time-consuming and a bit complex. If you have the required skills or technical expertise, you can build this integration by maintaining a script as above.
- Even manually exporting data from Typeform and importing data into Snowflakes using the web interface is challenging. Although this process is very straightforward, it does not help you extract real-time data.
Therefore, to avoid the above limitations, you can use ETL tools such as Hevo to create easily automated pipelines from Typeform to Snowflake to store and analyze the real-time data.
Image Source
All the limitations of manual methods can be overcome by using an Automated Data Pipeline like Hevo. Connecting Typeform to Snowflake using Hevo is a simple two-step process. Hevo Data is a No-code Data Pipeline solution that can help you seamlessly replicate data in real-time from 100+ Data Sources (Including 40+ free sources) like Typeform to your desired destination like Snowflake, other Data Warehouses, or Databases in a completely hassle-free & automated manner.
Perform the following steps to configure Typeform as the Source in your Pipeline:
- On the Asset Palette, click on PIPELINES.
- On the Pipelines List View, Click + CREATE.
- Select Typeform on the Select Source Type page,
- In the Configure your Typeform account page, click + ADD TYPEFORM ACCOUNT.
Image Credit: Hevo
- Sign in using your Typeform credentials.
- Click Accept to authorize Hevo to access your Typeform account.
Image Credit: Hevo
Image Credit: Hevo
- In the Configure your Typeform Source page, specify the following:
- Pipeline Name: A unique name for your Pipeline, not exceeding 255 characters.
- Historical Sync Duration: The duration for which the existing data in the Source must be ingested.
- Select Workspaces: The workspaces in your Typeform account, from which you want to ingest the data.
- On the Asset Palette, click on PIPELINES.
- On the Pipelines List View , Click + CREATE.
- On the Add Destination page, select Snowflake as the Destination type.
Image Credit: Hevo
- In the Configure your Snowflake Warehouse page, specify the following:
- Destination Name: A unique name for your Destination.
- Snowflake Account URL: The account URL that you retrieved in Step 6 above.
- Database User: The Hevo User that you created. This user has a non-administrative role in the Snowflake database.
- Database Password: Password of the database user.
- Database Name: Name of the Destination database where the data is to be loaded.
- Database Schema: Name of the schema in the Destination database where the data is to be loaded. Note: Schema name is case-sensitive.
- Warehouse: The Snowflake warehouse is associated with your database, where the SQL queries and DML operations are performed.
- Click TEST CONNECTION.
This will create a robust connection from Typeform to Snowflake.
Conclusion
Typeform is like an online surveying tool that allows businesses to build delightful interactions with customers for collecting data in a specific format. Businesses can then use this data to analyze Product Development, Business Intelligence, Customer Journey, Actionable Insights, and more. This article provided 3 different methods that can be used for connecting Typeform to the Snowflake account.
There are various trusted sources that companies use as it provides many benefits, but, transferring data from it into a data warehouse is a hectic task. The Automated Data Pipeline helps in solving this issue and this is where Hevo comes into the picture. Hevo Data is a No-code Data Pipeline and has awesome 100+ pre-built Integrations such as Typeform & Snowflake that you can choose from.
visit our website to explore hevo
Hevo can help you Integrate your data from 100+ Data Sources such as Typeform and load them into a destination like Snowflake, to analyze real-time data. It will make your life easier and data migration hassle-free. It is user-friendly, reliable, and secure.
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Share your experience of learning about Loading Data from Typeform to Snowflake in the comments section below.
Manjiri is a proficient technical writer and a data science enthusiast. She holds an M.Tech degree and leverages the knowledge acquired through that to write insightful content on AI, ML, and data engineering concepts. She enjoys breaking down the complex topics of data integration and other challenges in data engineering to help data professionals solve their everyday problems.
