What is plx google?

This article will take you through some of the basics of Google Data Studio – how to create a dashboard in no time, how to share your report, and more. Let’s get started, shall we? 😉

Data Studio is a relative newcomer in the world of data visualization and reporting tools. It was created by Google in 2016. And it has been gaining a lot of traction ever since, especially among marketers and data analysts.

Data Studio is completely free. There’s no paid version of it. You can use it as an alternative to paid reporting tools such as Tableau and Power BI.

No upfront investment is required. You can learn and access all of its features for free. You can even use it for business purposes as well, without paying a dime!

While you won’t get super-advanced features, that shouldn’t stop you from creating professional dashboards. Plus, it’s very easy to share your dashboards and collaborate with others.

There are many pre-built templates in Data Studio, allowing you to create beautiful dashboards full of charts quickly and easily.

Data Studio is cloud-based. It’s accessible as long as you have a browser and internet connection. The reports you create are saved automatically into Google Drive, so they’re available anytime and anywhere. No worries about losing the files.

With Data Studio, you can connect, analyze, and present data from different sources. You don’t even need to be tech-savvy or know programming languages to get started with Data Studio!

To start using Google Data Studio, you’ll need to sign in with your Google account (you can create it for free if you don’t already have one).

After successfully signing in, head on over to https://datastudio.google.com. You’ll see the following home screen, which is your launchpad into the world of Data Studio:

Before creating your first dashboard, let’s get familiar with Data Studio’s interface. The home page has these key parts:

1 – The left menu gives you a quick way to:

2 – The toolbar menu, which lets you:

3 – The search bar at the top is a convenient tool that lets you search reports by name.

4 – The Template Gallery gives you a quick way to get started with a new dashboard. You can either use a blank report or a pre-made template that the Data Studio team created. To see more templates, click “Template Gallery” in the top-right corner of the area.

5 – The report list is just below the Template Gallery area. Here, you can sort your reports based on name, owner, or last opened.

Every time you want to create a report, first, you’ll need to create a data source. It’s important to note that data sources are not your original data. To clarify and avoid confusion, see the explanation below:

When Data Studio was first released, there were only six Google-based data sources you could connect to. But a lot has changed since then!

As of this writing, there are 400+ connectors to access your data from 800+ datasets. Besides Google Connectors, there are also Partner Connectors (third-party connectors).

You can see the full list of available connectors here.

You’ll be able to get data from non-Google services such as LinkedIn, PayPal, Facebook, Twitter, HubSpot, etc., with third-party connectors. But most of them are not free.

In Google Data Studio, you’ll need metrics and dimensions to create a meaningful dashboard. Your dataset must have at least one metric and dimension for your visualization to be insightful.

So, what are metrics and dimensions in Data Studio?

Metrics are numeric values that measure or count. These values come from implicitly or explicitly applying an aggregation function, such as COUNT(), SUM(), AVG(), etc.

Dimensions are the names, descriptions, or other characteristics of the things you are measuring or counting.

Let’s continue trying to understand what those are with an example. Suppose you want to create a dashboard about the sales of your products. You may choose the following metrics:

There are many different ways to look at your data. And, with dimensions, you can analyze your data from different angles. See the text in bold in the following list for examples of dimensions:

Planning is essential to everything.

Before you create a dashboard in Google Data Studio, you should prepare your dataset and the dashboard layout. This includes deciding which metrics are important for your report or dashboard so that they can be displayed as columns, rows, or charts.

In Data Studio, you can connect to a large variety of datasets. But in this tutorial, we will be using Google Sheets. Why?

We’re big fans of Google Sheets. Apart from being simple and easy to use even for beginners, here are a few other reasons:

For this tutorial, we have already prepared a spreadsheet file containing online sales data as the dataset. Thus, you don’t need to spend time preparing your own.

To follow along with our example later, please make a copy of the following file by clicking on File > Make a copy and save it to your Google Drive.

File: FLW Online Sales

Take some time to design the layout of your report before you start building it. Your design doesn’t need to be detailed. But at least, decide what information you’re going to present and what charts you’re going to use to visualize it. A rough sketch using a pen and paper is better than nothing. That will help you avoid staring at a blank report for too long.

It’s a good idea to ask potential viewers what information they want to see, too. And please keep in mind that the dashboard should provide them with easy-to-digest information about primary metrics so they can tell whether there are issues that require attention. Think about the dashboard in your car. It lets you quickly check things, such as the car speed, fuel level, and engine temperature, as well as reminding you to refuel your car.

You may need to use data from multiple sources when creating a dashboard, such as accessing JSON-formatted data shared via APIs. You can be use BigQuery and CSV files as well.

Pulling all of these sources of data together into a Google spreadsheet before visualizing it in Data Studio allows you to see your raw data and how it is organized. With the help of Coupler.io, you can ensure your data from different sources are sorted and ready to be analyzed.

Coupler.io connects Google Sheets with other apps through a user-friendly interface and powerful API-driven connectors. It has some great features, including:

Please check out the complete list of the Google Sheets integrations that Coupler.io supports.

Now that we’ve covered some basics in the previous chapters, let’s see how we can use Google Data Studio for visualizing online sales data in a dashboard.

To give you an idea about what the finished product will look like, here’s a simple dashboard we’re going to build:

We’ll build a dashboard that will help viewers understand the total sales and its trend over time. Moreover, the dashboard will show a simple indicator to determine which product categories have low or high sales.

For visualization, we’ll be using a scorecard, a line chart, and a table, with the following purposes:

You’ll learn how to format the table using conditional formatting so you can quickly see which values are low in sales, as well as how to add calculated fields.

Open the Google Data Studio home page and follow the steps below:

Note: If you already have an existing report, you can also add a data source directly from it by clicking Add data in the report toolbar. We’ll cover more about the report toolbar later in this article.

Note: Once the setup is completed, you will see a data source list.

Note: If this is your first time using the Google Sheet connector, you’ll need to authorize Data Studio to connect to your Google Sheets by clicking the AUTHORIZE button.

After you have successfully set up the connection, the fields in your data source page will look like this:

Notice that, by default:

You may also notice that some fields with Currency data type in Google Sheets are detected as Number in Data Studio. If you like, you can change them manually to Currency by clicking the triangle icon (⏷) next to the data type:

You can create a report from the home page by using the Create button. But since we’re opening a data source page, let’s continue from this page.

Note: Data Studio creates a random table based on the fields in your data source. We will add a table later so, for now, let’s just delete it.

You may feel that the tools look quite intimidating at first glance. Don’t worry. We will briefly review the main core areas of this interface.

The top section on the right contains high-level functions for your report:

Under the menu, you’ll find the editing toolbox. Many of the menu options are also available when you right-click on the report elements. As of this writing, the toolbar has the following functions, from left to right:

There are two tabs in this configuration panel: Theme and Layout.

In the Theme tab, Data Studio offers a number of themes for your report. Let’s say you like a dark background. Try selecting the Constellation, Lagoon, or Simple Dark theme. You can save time creating a professional look instead of styling your report from scratch.

In the Layout tab, you can do things such as control how your report looks like in View mode, customize your report size and orientation, and change grid settings. We recommend experimenting with each option to see what works best for you.

After understanding the report editor and its features, you’re ready to start adding some components to your dashboard!

A scorecard is a great start as it’s like choosing a headline. To add a scorecard that shows the total sales to your report:

Now you will see the scorecard with the Total Sales label:

Note: In this Style tab, you can also change other appearances of your chart, such as the background color, border color, font, and so on.

By the way, with the help of scorecards, you can build a Looker Studio funnel visualization.

To create a line chart that shows sales over months for different order types, we have two options: a basic line chart or a time series.

Because we want to see how the data changes over time, a time series is best for this case. We can save a few steps using it rather than using a basic line chart.

Now follow the steps below to add a time series:

Note: In this case, we’re changing a field’s data type in a chart. This won’t change the original OrderDate field in your data source. It’s a best practice to always have a full Date or Date & Time field in your data source. Then, if necessary, you can adjust its type in a chart.

To add a table that shows the sales per product category:

Now, what if you want to display the total price before discount? And also display the product categories in uppercase letters? It’s time to create calculated fields.

You can add a calculated field in either the data source or a chart. When you add the field in the data source, it will be available in any report that uses the data source.

Let’s add a new field Total in the data source that multiplies a price by a quantity by following the steps below:

Let’s see another way to add a new calculated field. We’ll add a field Category in the chart to display product categories in uppercase letters. To do this, we’ll use a text function: UPPER.

Follow the steps below:

Now let’s format the OrderTotal cells to red for values under $1,000,000 so that you can quickly see which ones are low in sales.

Follow the steps below:

To share your report, click on the Share dropdown in the header toolbar. This will give you a list of different ways to share it:

Use this option to invite specific people or Google Groups and add them as viewers or collaborators on your report.

In the Add people tab, enter the email addresses of those you want to share your report with. You can allow them to edit it or just view it.

If you want to share more broadly, turn on link sharing in the Manage access tab. This will let anyone view your report, even if they don’t have a Google account.

Use this option to send scheduled reports in PDF format. You can schedule email delivery for yourself and others by setting up an email schedule: every day, weekdays only, etc.

Use this option to generate a short URL for your report, which you can then share with anyone. If you want to change who is able to access the report, click Change sharing settings and adjust them accordingly.

Use this option if you want to add the report to an existing web page.

First, tick Enable embedding. A few options will appear, allowing you to choose Embed mode and sizing.

How do you know which Embed mode to use?

The process of embedding can vary based on what web authoring system is being used. Some work with just a URL, while others require a full code for it to be properly embedded. If you are unsure about how your site will need this information, copy both to a text file.

Use this option to download your report as a PDF file. After that, you can distribute it via email or Slack. Additionally, you can protect the document with a password for added security.

We can’t cover everything in one article but, hopefully, you found this Google Data Studio tutorial to be a helpful starting point. There is so much more that Google Data Studio (Looker Studio) has to offer, and we don’t want you to stop here.

Google AnalyticsGoogle PLXSQLData AnalyticsDashboard DesignBigQueryReporting AnalyticsData IntegrityGoogle AnalyticsGoogle PLX+6. Google Data Studio turns your data into fully customizable informative reports and dashboards that are easy to read and share. My data source is custom a PLX query so may that have something to do with it? I 've tried the same exercise on dashboards using Gsheets and. Querying plx tables from bigquery; Joining tables from other projects; Other tools. Provided to YouTube by Routenote GOOGLE PLX · JDSY Winter Scape ℗ Joseph D.

Dataplex lets you do the following:

Enterprises have data that's distributed across data lakes, data warehouses, and data marts. Using Dataplex, you can do the following:

Dataplex lets you standardize and unify metadata, security policies, governance, classification, and data lifecycle management across this distributed data.

Dataplex manages data in a way that doesn’t require data movement or duplication. As you identify new data sources, Dataplex harvests the metadata for both structured and unstructured data, using built-in data quality checks to enhance integrity.

Dataplex automatically registers all metadata in a unified metastore. You can access data and metadata using various services and tools including the following:

Dataplex abstracts away the underlying data storage systems, by using the following constructs:

This section outlines common use cases for using Dataplex.

With this type of data mesh, data is organized into multiple domains within an enterprise- for example, Sales, Customers, and Products. Ownership of the data can be decentralized. You can subscribe to data from different domains. For example, data scientists and data analysts can pull from different domains to accomplish business objectives like machine learning and business intelligence.

In the following diagram, domains are represented by Dataplex lakes and owned by separate data producers. Data producers own creation, curation, and access control in their domains. Data consumers can then request access to the lakes (domains) or zones (subdomains) for their analysis.

In this case, data stewards need to retain a holistic view of the entire data landscape.

This diagram includes the following elements:

You can extend this scenario by breaking down data that's within zones into raw and curated layers. You can accomplish this approach by creating zones for each permutation of a domain and raw or curated data:

Another common use case is when your data is accessible only to data engineers, and is later refined and made available to data scientists and analysts. In this case, you can set up a lake to have the following: