Looker for visualization in GCP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Looker in Google Cloud Platform?

Looker is a tool in Google Cloud that helps you turn data into easy-to-understand charts and reports. It lets you explore data and create visual stories without needing to write complex code.

beginner

How does Looker connect to your data?

Looker connects directly to your databases or data warehouses, like BigQuery, to fetch data in real-time. It does not copy data but queries it when you ask for reports or dashboards.

intermediate

What is a LookML model in Looker?

LookML is a simple language Looker uses to describe your data structure. It helps define how data tables relate and what calculations to use, so Looker can build accurate visualizations.

beginner

Why use dashboards in Looker?

Dashboards in Looker gather many charts and reports in one place. This helps you see the big picture quickly and share insights with your team easily.

intermediate

How does Looker help with data security?

Looker respects your data permissions. It only shows data you are allowed to see, based on roles and rules set by your organization, keeping data safe and private.

What type of data connection does Looker use?

A Uses only Google Sheets as data source

B Copies all data into Looker storage

C Uploads data manually via files

D Direct connection to databases without copying data

Looker connects directly to your databases or data warehouses and queries data live without copying it.

What is LookML used for in Looker?

A To create user accounts

B To write SQL queries manually

C To define data structure and relationships

D To design dashboard layouts

LookML is a language to describe data models and relationships so Looker can build visualizations correctly.

Which Google Cloud service is commonly used with Looker for data storage?

A Cloud Functions

B BigQuery

C Cloud Storage

D App Engine

BigQuery is a popular data warehouse service that Looker connects to for querying large datasets.

What is the main benefit of Looker dashboards?

A They combine multiple visual reports in one view

B They store data permanently

C They replace databases

D They automate data entry

Dashboards let you see many charts and reports together for quick insights.

How does Looker ensure data security?

A By showing data based on user permissions

B By storing data locally on user devices

C By encrypting all data in transit only

D By disabling data access for all users

Looker enforces access controls so users only see data they are allowed to view.

Explain how Looker connects to data and why this is beneficial.

Think about how Looker gets data fresh without storing it itself.

Describe the role of LookML in creating visualizations in Looker.

LookML is like a map for your data inside Looker.

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