Demo: Building a Simple Flow

This is a step-by-step tutorial on how to launch your first project in Tomat. Let's get started!

Part 1. Connect Data.

From the home page, you can connect your data. You can either use local CSV or Excel files, or connect directly to your cloud database if you use one.

In this tutorial, we will work with local datasets.

Before we begin, let's go through the Tomat interface.

In our Catalog, you can store files that you work with. Remember that Tomat is a desktop app, which means no data will leave your computer and appear somewhere in the cloud. We prioritize security the most. If you do not have a data warehouse, our Catalog will serve as your database.

In Flows, you can modify your data by merging data, adding new columns, and building dashboards.

Jobs are the list of your completed data analytics tasks. It's like a folder with final reports.

Let's go back to the home page and start working with data. I select a local file upload, pick a CSV, and preview it. Then I click on "Add to Catalog" to keep it handy for future use.

The next window you see is our exploration page. First of all, you can check data validity and control data quality and value distribution for each column.

Using the exploration bar, you can get quick insights into your data. For example, let's filter out only active advertising campaigns.

If you're done with your analysis at this step, you can already download this new dataset onto your computer. To continue working with the data, click on "Add to Flow."

Part 2. Analyze data in Tomat

Now we are ready to start our data analysis project in Tomat!

As you can see, we have uploaded a file with Facebook campaigns. At the bottom, you can preview your dataset. On the right side, you see auto-cleanup suggestions, such as deleting empty rows or formatting the text case. You can apply these suggestions or simply ignore them if you don't need them.

Also, on the right side, you will perform all your data transformations.

As you remember, we have filtered our initial dataset, so the operation is recorded here in the data pipeline.

In the top menu, you see different operators that we call Nodes. Use them to manipulate your data. They are similar to what you have in spreadsheets or when working with SQL.

Now, let's build a chart. I select a Chart node and specify the axes. The dashboard is created. In our future releases, it will be possible to download not only a static image but also a dynamic dashboard with connected data.

Let's take a step back and add a new custom metric to our filtered table. To do this, I will add a new column and, as an example, divide the budget by the number of clicks to calculate the CTR (Click-Through Rate).

And I can create a new chart with the calculated metric.

Part 3: GPT Magic Node

Now let's explore how the magic AI feature works in Tomat. You can format data or create new values using natural language. For example, I can ask the AI to perform a new calculation. Don't forget to click on the purple button to launch the GPT command.

Later, I will show you more advanced examples of how to use AI in your data analysis.

Part 4: Downloading Your Results

When you are finished with your data manipulations, click on the output node. Then, select a destination folder on your computer or push the table directly to the cloud data warehouse. After that, click on "Run" at the top of the screen to apply all the transformations you have created.

Go to the jobs section and find your results: two charts and the table.

Part 5: GPT Tutorials

I suggest studying our tutorials available in Flows, particularly paying attention to the GPT tutorial. You can format unstructured data like phone numbers or translate text into another language. Explore our examples, and we look forward to seeing your own AI use cases. Don't hesitate to share them with our community in the Slack channel.

Here is a quick overview of how you can handle data in Tomat. If you need more tutorials like this, please let us know!

See you in Tomat!

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