Looker for visualization in GCP - Commands & Configuration

Introduction

Looker helps you turn data into easy-to-understand charts and reports. It solves the problem of making complex data clear and useful for decision-making.

When you want to create dashboards to track sales performance over time.

When you need to share interactive reports with your team without coding.

When you want to explore data from Google BigQuery without writing SQL.

When you want to schedule automatic email reports for stakeholders.

When you want to combine data from multiple sources into one view.

Commands

This command logs you into your Google Cloud account so you can access Looker and other GCP services.

Terminal

gcloud auth login

Expected Output

Your browser has been opened to visit:

https://accounts.google.com/o/oauth2/auth?...

You are now logged in as [[e-mail]].

Lists all your Google Cloud projects so you can confirm the project where Looker is set up.

Terminal

gcloud projects list

Expected Output

PROJECT_ID           NAME              PROJECT_NUMBER

my-looker-project    Looker Project    123456789012

Creates a new Looker instance in your Google Cloud project to start building visualizations.

Terminal

gcloud looker instances create my-looker-instance --project=my-looker-project --location=us-central1

Expected Output

Create request issued for: projects/my-looker-project/locations/us-central1/instances/my-looker-instance

Waiting for operation to complete...

Operation completed successfully.

→

--project - Specifies the Google Cloud project to use

→

--location - Sets the region where Looker instance will run

Checks the status and details of your Looker instance to make sure it is ready.

Terminal

gcloud looker instances describe my-looker-instance --project=my-looker-project --location=us-central1

Expected Output

name: projects/my-looker-project/locations/us-central1/instances/my-looker-instance

state: RUNNING

createTime: '2024-06-01T12:00:00Z'

endpoint: https://my-looker-instance.looker.cloud

→

--project - Specifies the Google Cloud project

→

--location - Specifies the region of the Looker instance

Key Concept

If you remember nothing else from this pattern, remember: Looker connects your data to easy visual reports without needing to write code.

Common Mistakes

Trying to create a Looker instance without setting the correct project or location flags.

The command will fail or create the instance in the wrong place, causing confusion or errors.

Always specify the --project and --location flags with the correct values when creating or managing Looker instances.

Not logging in with gcloud auth login before running Looker commands.

Commands will fail because you are not authenticated to access Google Cloud resources.

Run gcloud auth login first to authenticate your session.

Summary

Use gcloud auth login to access your Google Cloud account.

Create a Looker instance with gcloud looker instances create including project and location flags.

Check your Looker instance status with gcloud looker instances describe.

These commands set up Looker so you can start building visual data reports.

Practice

1. What is the main purpose of Looker in data analysis?

easy

A. To write complex SQL queries manually

B. To create visual reports from raw data without coding

C. To store large amounts of raw data

D. To replace databases with spreadsheets

Solution

1. Step 1: Understand Looker's role

   Looker is designed to turn raw data into visual reports easily.

2. Step 2: Compare options

   Only To create visual reports from raw data without coding describes Looker's main function correctly; others describe unrelated tasks.

3. Final Answer:

   To create visual reports from raw data without coding -> Option B

4. Quick Check:

   Looker = Visual reports without coding [OK]

Hint: Looker = easy visuals from data, no coding needed [OK]

Common Mistakes:

• Thinking Looker stores data like a database
• Confusing Looker with manual SQL writing
• Assuming Looker replaces spreadsheets

2. Which syntax correctly defines a measure in LookML?

easy

A. measure total_sales { sum: sales }

B. measure total_sales = sum(${TABLE}.sales)

C. measure: total_sales { type: sum; sql: ${TABLE}.sales ;; }

D. measure: total_sales = sum(sales)

Solution

1. Step 1: Recall LookML measure syntax

   Measures use the format: measure: name { type: ...; sql: ... ;; }

2. Step 2: Check each option

   measure: total_sales { type: sum; sql: ${TABLE}.sales ;; } matches correct LookML syntax with colons and braces; others do not.

3. Final Answer:

   measure: total_sales { type: sum; sql: ${TABLE}.sales ;; } -> Option C

4. Quick Check:

   LookML measure syntax uses colons and braces [OK]

Hint: LookML uses colons and braces for measures [OK]

Common Mistakes:

• Using equal signs instead of colons
• Omitting braces {} around measure definition
• Incorrect SQL syntax inside measure

3. Given this LookML snippet, what will the measure average_price calculate?

measure: average_price {
  type: average
  sql: ${TABLE}.price ;; }

medium

A. Sum of all prices

B. Count of price entries

C. Maximum price value

D. Average of the price column

Solution

1. Step 1: Identify measure type

   The measure type is 'average', which calculates the mean value.

2. Step 2: Understand SQL reference

   The SQL references the price column, so it averages all prices.

3. Final Answer:

   Average of the price column -> Option D

4. Quick Check:

   Measure type 'average' = mean value [OK]

Hint: Measure type 'average' means mean value [OK]

Common Mistakes:

• Confusing average with sum or count
• Ignoring the measure type keyword
• Assuming it returns max value

4. You wrote this LookML measure but get an error:

measure: total_revenue {
  type: sum
  sql: ${revenue} ;; }

What is the likely cause?

medium

A. Missing ${TABLE} prefix before revenue

B. Incorrect measure type 'sum'

C. Extra semicolon after sql statement

D. Measure name cannot have underscores

Solution

1. Step 1: Check SQL reference format

   LookML requires ${TABLE}.column to reference table columns properly.

2. Step 2: Identify missing prefix

   The sql references ${revenue} without ${TABLE}., causing error.

3. Final Answer:

   Missing ${TABLE} prefix before revenue -> Option A

4. Quick Check:

   Column refs need ${TABLE}. prefix [OK]

Hint: Always prefix columns with ${TABLE}. in LookML SQL [OK]

Common Mistakes:

• Omitting ${TABLE}. before column names
• Thinking measure type 'sum' is invalid
• Assuming underscores are not allowed in names

5. You want to create a dashboard showing total sales and average order value per region. Which LookML approach is best?

hard

A. Define measures for total sales and average order value, then create an explore with region dimension

B. Write raw SQL queries for each metric and paste results manually into dashboard

C. Create separate dashboards for sales and orders without linking regions

D. Use only dimensions without measures to keep dashboard simple

Solution

1. Step 1: Identify needed metrics and grouping

   You need total sales and average order value as measures, grouped by region dimension.

2. Step 2: Use LookML best practice

   Defining measures and an explore with region dimension allows dynamic, reusable dashboards.

3. Step 3: Evaluate other options

   The other options do not provide integrated, maintainable visualization.

4. Final Answer:

   Define measures for total sales and average order value, then create an explore with region dimension -> Option A

5. Quick Check:

   Measures + dimension + explore = best dashboard [OK]

Hint: Use measures + dimension + explore for dashboards [OK]

Common Mistakes:

• Manually copying SQL results instead of using LookML
• Separating dashboards without common grouping
• Ignoring measures and using only dimensions