Quickstart using BI Engine SQL interface with Tableau

Preview

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BigQuery BI Engine lets you perform fast, low-latency analysis services and interactive analytics with reports and dashboards backed by BigQuery.

This introductory tutorial is intended for data analysts and business analysts who use the business intelligence (BI) tool Tableau to build reports and dashboards.

Objectives

In this tutorial, you complete the following tasks:

• Create a BI reservation and add capacity using the Google Cloud Console.
• Use Tableau to connect to a BigQuery table that's managed by BI Engine.
• Create dashboards using Tableau.

Costs

There is no cost to using the BigQuery BI Engine SQL interface during the first 60 days of the preview phase.

After the first 60 days, BI Engine will follow the pricing model that's described on the BI Engine pricing page.

Before you begin

Before you begin, ensure that you have a project to use, that you have enabled billing for that project, and that you have enabled the BigQuery API.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.

4. The BigQuery API is automatically enabled in new projects. To activate the BigQuery API in an existing project, go to Enable the BigQuery API.

   Enable the API

Create a BigQuery dataset

The first step is to create a BI Engine dataset to store your BI Engine-managed table. To create your dataset, follow these steps:

1. In the Cloud Console, go to the BigQuery page.

   Go to BigQuery

2. In the navigation panel, in the Explorer panel, click your project name.

   Note: The default experience is the Preview Cloud Console. If you clicked Hide preview features to go to the Generally Available Cloud Console, then perform the following step instead:
   
   In the navigation panel, in the Resources section, select the project.

3. In the details panel, click Create dataset.

4. On the Create dataset page, do the following:

   • For Dataset ID, enter biengine_tutorial.

   • For Data location, choose United States (US). The public datasets are stored in the US multi-region location. For simplicity, we recommend that you place your dataset in the same location.

     

5. Leave all of the other default settings in place and click Create dataset.

Create a table by copying data from a public dataset

This tutorial uses a dataset available through the Google Cloud Public Dataset Program. Public datasets are datasets that BI Engine hosts for you to access and integrate into your applications.

In this section, you create a table by copying data from the San Francisco 311 service requests dataset. You can explore the dataset by using the Cloud Console.

Create your table

To create your table, follow these steps:

1. In the Cloud Console, open the SF 311 dataset.

   Go to the SF 311 dataset

2. In the navigation pane, expand san_francisco_311 and click the 311_service_requests table.

3. Click Copy table.

4. In the Copy table dialog, in the Destination section, do the following:

   • For Project name, choose your project.
   • For Dataset name, verify that biengine_tutorial is selected.

   • For Table name, enter 311_service_requests_copy.

     

   • Click Copy.

5. When the copy job is complete, you can verify the table contents by expanding PROJECT_NAME > biengine_tutorial and clicking on 311_service_requests_copy > Preview.

Create your BI Engine reservation

1. In the BigQuery Admin Console, go to the BI Engine page.

   Go to the BigQuery Admin Console

   Note: If prompted to enable BigQuery Reservation API, click Enable.

2. Click Create reservation.

3. On the Create reservation page, for Step 1:

   • Verify your project name.
   • Choose your location. The location should match the location of the datasets you are querying.

   • Adjust the slider to the amount of memory capacity you're reserving. The following example sets the capacity to 2 GB. The current maximum is 100 GB.

     

4. Click Next.

5. For Step 2, review your reservation details and then click Next.

6. For Step 3, review the agreement and then click Create.

7. After you confirm your reservation, the details are displayed on the Reservations page.

   

Connect to a dataset from Tableau

To connect to a dataset from Tableau, you need to take some steps in Tableau and then some steps in BI Engine.

Steps to take in Tableau

1. Start Tableau Desktop.
2. Under Connect, select Google BigQuery.
3. In the tab that opens, select the account that has the BigQuery data that you want to access.
4. If you're not already signed in, enter your email or phone, select Next, and enter your password.
5. Select Accept.

Tableau can now access your BigQuery data.

Steps to take in BI Engine

In the Cloud Console, on the Data Source page:

1. From the Billing Project drop-down, select the billing project where you created the reservation.
2. From the Project drop-down, select your project.
3. From the Dataset drop-down, select the dataset biengine_tutorial.
4. Under Table, select the table 311_service_requests_copy.

Creating a chart

Once you have added the data source to the report, the next step is to create a visualization.

Create a chart that displays the top complaints by neighborhood:

1. In the Cloud Console, click on New worksheet.
2. Set the Dimension to Complaint Type.
3. Filter based on the dimension called neighborhood.
4. Under Measures, select Number of Records.
5. Right-click on the Neighborhood filter and click Edit Filter.
6. Add a filter to exclude null: select Null.
7. Click OK.

For more information, see the Tableau documentation.

Cleaning up

To avoid incurring charges to your Google Cloud account for the resources used in this quickstart, you can delete the project, delete the BI Engine reservation, or both.

Deleting the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Cloud Console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

Deleting the reservation

Alternatively, if you intend to keep the project, then you can avoid additional BI Engine costs by deleting your capacity reservation.

To delete your reservation, follow these steps:

1. In the BigQuery Admin Console, go to the BI Engine page.

   Go to the BigQuery Admin Console

   Note: If prompted to enable BigQuery Reservation API, click Enable.

2. In the Reservations section, locate your reservation.

3. In the Actions column, click the icon to the right of your reservation and choose Delete.

4. In the Confirm reservation removal dialog, enter REMOVE and then click Proceed.

What's next

• For an overview of the BI Engine SQL interface, see BI Engine SQL interface.