Post by sumiseo558899 on Nov 7, 2024 1:10:57 GMT -6
Hi, my name is Sasha Lyubimova. I am a web analyst at the Original Works agency . My colleagues and I love to learn and apply new tools in our work. In this article, I decided to share another find that appeared not so long ago and has not yet managed to gain due popularity. We are talking about third-party visualizations of Google Data Studio. I did not find a full-fledged setup guide in Russian, so I wrote my own detailed guide for my colleagues.
news
Third-party visualizations are used to content writing service
expand the capabilities of Google Data Studio — the function allows you to create your own visualizations, set the necessary style settings. Of course, a standard set of graphs and charts is quite enough to solve visualization problems, but when it comes to the need to provide a report that will look non-standard, or the data structure is such that it is difficult to present it with current tools, you can use third-party visualizations. An example is the visualization of the sales funnel. It is not included in the standard set of Data Studio visualizations, but third-party developers have already created it, published it in the gallery and now it can be used in reports.
There are two options for creating your own visualization in Google Data Studio: manually and by "local development" (using the dscc-gen tool).
The dscc-gen tool allows you to generate a visualization template and also allows you to preview changes in real time, which allows you to create visualizations faster. Here is a description of the tool and a video from Google.
Google has created 2 practical works, after completing which you can get acquainted with the creation of visualizations. They are not Russified, but if necessary, you can use automatic translation.
A practical guide to manual development.
A tutorial for developing with dscc-gen.
I will not dwell on the manual method in detail, but will describe a way to create a third-party visualization using the "local development" method (dscc-gen). The convenience of this method lies in the speed of making changes to the visualization code and viewing the result without connecting the visualization to the report. We also immediately have access to a visualization template that is easy to work with
Briefly, the process looks like this:
Installation and configuration of development tools: node.js, Google Cloud SDK, Google Cloud account;
Generating a visualization template - working with Google's "dscc-gen" tool;
Changing the visualization template;
Publishing visualization to DS.
Step 1. Preparing the environment
Download and install Node.js with the npm package. Download from nodejs.org/, choose the one that suits your system
.
1
Install Google Cloud SDK . You can download it on the page . There are versions for different systems. I have Windows, so I just go through the steps of the installation wizard.
2
Google Cloud SDK is installed with all the components required for operation, including the Python language.
Next, we need to complete the configuration by running gcloud init. It will start automatically when the installation is complete. A window will open:
3
Here we will need the Google Cloud account data. We will enter this data when answering the wizard's questions.
To the question "Would you like to log in", answer Y and press "Enter". A browser will open, here you will need to log in to the Google account that you created or use to work with Google Cloud.
4
After we log in, we return to the gcloud init window:
5
Now we need to choose which project in Google Cloud we will use. If the project has not been created in advance, you can select Create a new project. It is more convenient to create a project in advance through the web interface. I had created project number 3. So I just select the item and press Enter.
Now we need to create a dataset that will be available at gs://dataset-name. This address will be public, it must be absolutely unique, otherwise the system will return an error.
The dataset is created using the command in the terminal: gsutil mb -b on -l us-east1 gs://specify-dataset-name/
6
The line creating gs://your-dataset-name appears, indicating that the command has completed successfully.
Step 2. Working with the dscc-gen tool (generation of a visualization template)
We go to the local folder where we will store the visualization files for working with them and type the command npx @google/dscc-gen viz.
In the command line, you will need to enter the project name and the dataset address. We specify the project name and dataset name as those in "Stage 1". After that, we create the dev and prod folders inside the dataset.
news
Third-party visualizations are used to content writing service
expand the capabilities of Google Data Studio — the function allows you to create your own visualizations, set the necessary style settings. Of course, a standard set of graphs and charts is quite enough to solve visualization problems, but when it comes to the need to provide a report that will look non-standard, or the data structure is such that it is difficult to present it with current tools, you can use third-party visualizations. An example is the visualization of the sales funnel. It is not included in the standard set of Data Studio visualizations, but third-party developers have already created it, published it in the gallery and now it can be used in reports.
There are two options for creating your own visualization in Google Data Studio: manually and by "local development" (using the dscc-gen tool).
The dscc-gen tool allows you to generate a visualization template and also allows you to preview changes in real time, which allows you to create visualizations faster. Here is a description of the tool and a video from Google.
Google has created 2 practical works, after completing which you can get acquainted with the creation of visualizations. They are not Russified, but if necessary, you can use automatic translation.
A practical guide to manual development.
A tutorial for developing with dscc-gen.
I will not dwell on the manual method in detail, but will describe a way to create a third-party visualization using the "local development" method (dscc-gen). The convenience of this method lies in the speed of making changes to the visualization code and viewing the result without connecting the visualization to the report. We also immediately have access to a visualization template that is easy to work with
Briefly, the process looks like this:
Installation and configuration of development tools: node.js, Google Cloud SDK, Google Cloud account;
Generating a visualization template - working with Google's "dscc-gen" tool;
Changing the visualization template;
Publishing visualization to DS.
Step 1. Preparing the environment
Download and install Node.js with the npm package. Download from nodejs.org/, choose the one that suits your system
.
1
Install Google Cloud SDK . You can download it on the page . There are versions for different systems. I have Windows, so I just go through the steps of the installation wizard.
2
Google Cloud SDK is installed with all the components required for operation, including the Python language.
Next, we need to complete the configuration by running gcloud init. It will start automatically when the installation is complete. A window will open:
3
Here we will need the Google Cloud account data. We will enter this data when answering the wizard's questions.
To the question "Would you like to log in", answer Y and press "Enter". A browser will open, here you will need to log in to the Google account that you created or use to work with Google Cloud.
4
After we log in, we return to the gcloud init window:
5
Now we need to choose which project in Google Cloud we will use. If the project has not been created in advance, you can select Create a new project. It is more convenient to create a project in advance through the web interface. I had created project number 3. So I just select the item and press Enter.
Now we need to create a dataset that will be available at gs://dataset-name. This address will be public, it must be absolutely unique, otherwise the system will return an error.
The dataset is created using the command in the terminal: gsutil mb -b on -l us-east1 gs://specify-dataset-name/
6
The line creating gs://your-dataset-name appears, indicating that the command has completed successfully.
Step 2. Working with the dscc-gen tool (generation of a visualization template)
We go to the local folder where we will store the visualization files for working with them and type the command npx @google/dscc-gen viz.
In the command line, you will need to enter the project name and the dataset address. We specify the project name and dataset name as those in "Stage 1". After that, we create the dev and prod folders inside the dataset.