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Product announcement: Keboola is launching no-code transformations!

Business experts can build something valuable without coding or help from IT

How To
April 21, 2023
Product announcement: Keboola is launching no-code transformations!
Business experts can build something valuable without coding or help from IT

In this new exciting development, Keboola is launching no-code data transformations for everybody on the platform. 

No-code transformations empower users without the technical know-how to build robust, feature-rich applications without having a degree in computer science or waiting for the IT department to develop the apps for them.

Why are no-code transformations so great?

No-code data transformations empower everyone to clean and reshape their data without writing a single line of code.

Having quality data is necessary for every data-driven decision down the line. Now you don’t have to rely on the IT department or experts who know how to code to provide the data you need.

With no-code transformations your team can:

  1. Build data products without coding. Everybody at your company can build something awesome without having a degree in computer science.
  2. Save time. No-code transformations deliver the same results as low-code transformations. But they take just a couple of clicks.
  3. Collaborate. No-code transformations are part of the same environment as all the other features offered by Keboola. Coders and non-coders can work side-by-side on the same projects with the tools they need. 
  4. Scale. Automate and reuse your transformations to speed up and scale your data initiatives.

What can I do with no-code data transformations?

With no-code transformations, it just takes a couple of clicks to:

  1. Combine tables. Join data across 250+ different sources. With Keboola you can easily extract data with over 250 connectors. With no-code transformations, you can join that data together to get insights beyond a single data set.
  2. Do math. Use the function Sum, Count, Min, and Max to create new columns and new metrics from existing ones.
  3. Create and update data. Modify existing columns, create new columns and use fine-grained “if-then-else” logic to create new columns from existing values in your dataset. This feature is perfect for creating new features and dimensions.
  4. Filter data. Remove data you don’t want to see in your tables. From removing null values to removing rows under specific conditions (column is/is not equal to X).
  5. Clean and format data. Replace data within a column, fill in missing and null values, remove unwanted characters, rename a column, get rid of duplicate entries or even drop an entire column, clean your data, and format those pesky date and times values to your required standard.

How do no-code data transformations work?

Under the hood, no-code transformations tap into the power of dbt. 

This design allows you to extend all the benefits of dbt, such as modular development, reusable blocks, and high performance. 

And you don’t have to know SQL or how to use dbt to get all the benefits. 

No-code transformations do the heavy lifting for you in a couple of clicks.

What if I need something more complex not covered by no-code transformations?

Sometimes your data needs will take you beyond what the powerful no-code transformations can do. 

Luckily, Keboola was built for collaboration.

You can switch the manipulations from no-code to low-code and open the transformation blocks to engineers who code alongside you.

The low-code transformations available in Keboola empower your engineers to take over and code the transformations in Python, SQL, Julia, R, or native dbt. 

Guided example: Email cleaning

Let’s go through a common email cleaning process to demonstrate the power, simplicity, and speed of no-code transformations.

🧙‍♂️Or, let the wizard guide you. Click here to follow the prompts directly from Keboola.

Task description

We have a CSV with email signups for our super-amazing new product launch. However, a lot of emails are ill-formatted, duplicated, and currently need a lot of cleaning before we can import them into our marketing tool and run a marketing campaign.

Our goal: get clean emails, so we can import them into our CRM for a highly personalized marketing campaign.

Step 1: Inspect the emails to understand what cleaning is needed

We open up the file with email signups to get a better understanding of what will need to be cleaned.

We can see a couple of problems:

  1. Some emails are duplicated (in rows 8 and 9, Sarah Williams appears twice).
  2. Some leads did not enter email addresses (row 5) or the email is not valid (row 6).
  3. It would be nice if we could segment the emails into corporate versus private emails based on domain. For example, gmail domains seem like personal emails. While company emails could be used for company-focused marketing offering B2B discounts.

Let’s start cleaning!

Step 2: Load the email data into Keboola

Login into Keboola. If you don’t already have an account you can make one for free (no credit card required).

Within your dashboard, click Transformations > New Transformation > No-code Transformation. A popup window will appear asking you to name your new transformation. We named it “Email cleaning”.

You will be redirected to the Transformation window. Click the button Select data and choose the location where your CSV file is. If it’s on your computer, pick Upload data from your computer. If you’ve used Visual Flow Builder to load the data into Keboola, choose Select an existing table.

Step 3: Remove duplicate emails

Scroll down to the Data Manipulation panel > pick Clean & Format> click Remove Duplicate Rows.

A window will pop up asking you to pick the column from which we’d like to remove duplicates. We pick the email column and click Save.

Step 4: Remove rows with missing or ill-formatted emails

To remove rows without an email address or with garbage characters, we will filter the table to only keep rows with valid emails. 

Go to Data Manipulation > Filter Data > click Filter Rows.

In the configuration panel, specify that you would only like to keep the rows that contain the “@” character in the column called “email” and Save your results.

Step 5: Create a new column signaling private vs company emails

To personalize the email campaign, we’d like to create a new column called “prospect_type” based on the email domain. If the email address uses gmail, we’ll categorize it as “private”. Otherwise, it will be a “company” email.

Go to Data Manipulation > Create & Update Data > click If/Else New Column.

In the configuration panel, specify that email addresses that contain “” should be categorized as “private”, while the other emails should be categorized as “company”.

Step 6: Determine where to save clean data and run the transformation

Lastly, we just need to specify where we’d like the clean email data to be saved.

Scroll down to Result > click Save to storage and create or select an existing table for your clean data.

On top, press the Run transformation button to run the cleaning data manipulation that gets you a cleaned full list of emails for your marketing campaign.

The entire process takes 5 minutes with no-code transformations. Even with veteran coders, the same SQL or Python transformation pipeline would take around an hour to code.

I’m ready to start. Where can I get no-code transformations?


Just login into Keboola.

Within your dashboard, click Transformations > New Transformation > No-code Transformation

You’ll be greeted by the no-code transformation UI where you simply pick and click the transformation you need.

Don’t have an account yet? 

No worries. 

Simply create a free account (no credit card required) and start exploring the powerful automations offered by Keboola.

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