Analyze data with BI Engine and Looker

Looker is an enterprise platform for business intelligence, data applications, and embedded analytics. Looker helps you explore, share, and visualize your company's data so that you can make better business decisions.

How Looker works

Looker lets data experts at each organization describe their data using a lightweight modeling language called LookML. LookML tells Looker how to query data, so everyone in the organization can create easy-to-read reports and dashboards to explore patterns of data. Looker offers additional features for creating custom data applications and experiences.

Looker's platform works with transactional databases like Oracle and MySQL as well as analytical datastores like BigQuery, Snowflake, Redshift, and more. Looker lets you create consistent data models on top of all your data with speed and accuracy. Looker offers a unified surface to access all of an organization's data.

Looker integration with BigQuery

Looker supports hosting in Google Cloud. Because Looker is platform independent, it connects to data in BigQuery as well as other public clouds.

You don't need Looker to use BigQuery. However, if your BigQuery use case includes business intelligence, data applications, or embedded analytics you might want to review Looker as a provider of these services.

If you already have a Looker instance running, see the instructions for connecting Looker to BigQuery.

Get started with Looker and BigQuery

The BI Engine seamlessly integrates with any business intelligence (BI) tools, including Looker. For more information, see BI Engine overview.

Create a BigQuery dataset

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

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. In the left pane, click Explorer:

    Highlighted button for the Explorer pane.

    If you don't see the left pane, click Expand left pane to open the pane.

  3. In the Explorer pane, click your project.

  4. In the details pane, click View actions, and then click Create dataset.

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

    • For Dataset ID, enter biengine_tutorial.

    • For Data location, choose us (multiple regions in United States), the multi-region location where public datasets are stored.

    • For this tutorial, you can select Enable table expiration, and then specify the number of days before the table expires.

      Create dataset page

  6. 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 BigQuery 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 Google Cloud console.

Create your table

To create your table, follow these steps:

  1. In the Google Cloud console, go to the BigQuery page.