10 Most common Looker interview questions

Here are 10 of the most common Looker interview questions, along with explanations to help you understand and prepare your answers:

 1. What is Looker, and how is it used in data analytics?

Explanation:

Looker is a modern business intelligence (BI) and data analytics platform that enables organizations to explore, analyze, and share real-time data insights. It connects directly to databases and uses LookML (a modeling language) to create data models, allowing users to build interactive dashboards, reports, and visualizations without needing to know complex SQL queries.

 2. What is LookML, and why is it important in Looker?

Explanation:

LookML is Looker's proprietary modeling language used to describe dimensions, measures, calculations, and data relationships. It allows data analysts to create reusable and scalable data models that make data exploration easier for end-users. LookML abstracts the complexity of SQL, enabling consistent and efficient data querying across the organization.

 3. Explain the difference between a dimension and a measure in Looker.

Explanation:

- Dimension: A dimension is an attribute or a qualitative value that represents descriptive data, such as dates, product names, or customer IDs. Dimensions are used to group and categorize data.

- Measure: A measure is a quantitative value that represents calculations or aggregations, such as sums, averages, counts, or other metrics. Measures are typically based on columns in the database and involve numerical analysis.

 4. How does Looker connect to a database, and what types of databases does it support?

Explanation:

Looker connects to databases using JDBC (Java Database Connectivity) or ODBC (Open Database Connectivity) drivers. It supports a wide range of SQL-based databases, including but not limited to:

- BigQuery

- Snowflake

- Redshift

- MySQL

- PostgreSQL

- SQL Server

Looker works as a "live connection," meaning it queries the database in real-time, ensuring that data is always up to date.

 5. How do you create a primary key in LookML, and why is it important?

Explanation:

To create a primary key in LookML, you need to define a field with the `primary_key: yes` attribute in your view file. The primary key uniquely identifies each row in a table or view, which is essential for establishing relationships between different views or tables. It enables Looker to perform joins accurately when combining data from multiple sources.

Example:

lookml

dimension: order_id {

 primary_key: yes

 sql: ${TABLE}.order_id ;;

}

 6. What is a persistent derived table (PDT) in Looker, and when would you use it?

Explanation:

A Persistent Derived Table (PDT) is a temporary table created by Looker that stores the results of a complex query in the database for a specified period. PDTs are used to improve performance when dealing with resource-intensive calculations, large datasets, or queries that need to be reused frequently. Since PDTs are stored in the database, they reduce query execution time and load on the database.

 7. How do you create a join in LookML, and what types of joins are available?

Explanation:

In LookML, you create joins by defining a `join` block within an `explore` block. You specify the type of join (`inner`, `left_outer`, `full_outer`, `right_outer`) and the conditions that define how the tables should be linked.

Example:

lookml

explore: orders {

 join: customers {

   type: left_outer

   sql_on: ${orders.customer_id} = ${customers.id} ;;

 }

}

This example creates a left outer join between the `orders` and `customers` tables.

 8. How can you handle time-based data in Looker, and what are some common ways to create time-based dimensions?

Explanation:

Looker provides built-in time-based functions and LookML syntax to handle time-based data. You can create time-based dimensions using `type: time` in LookML, allowing you to break down date fields into granular parts like year, quarter, month, week, or day.

Example:

lookml

dimension_group: order_date {

 type: time

 timeframes: [raw, date, week, month, quarter, year]

 sql: ${TABLE}.order_date ;;

}

This dimension group creates multiple time-based dimensions, making it easier to analyze data by different time periods.

 9. How do you share reports or dashboards with others in Looker?

Explanation:

You can share reports or dashboards in Looker in the following ways:

- Sharing Links: Generate and share direct links to reports or dashboards.

- Scheduling and Sending: Use Looker's scheduling feature to send reports or dashboards via email at specific intervals (e.g., daily, weekly, monthly).

- Embedding: Embed Looker dashboards into external applications, websites, or intranet pages.

- Exporting: Export reports as CSV, Excel, PDF, or image files.

These features make it easy to distribute insights to different stakeholders.

 10. How do you handle row-level security in Looker?

Explanation:

Row-level security in Looker is implemented by using access filters or Liquid filters. This allows you to restrict data access based on user attributes, ensuring that users only see data relevant to them.

For example, if you want to restrict data by user role, you can set up a filter in LookML:

lookml

access_filter: {

 field: users.department

 user_attribute: department

}

This configuration ensures that users only see data for their respective departments, maintaining data confidentiality and compliance.

These questions cover a mix of fundamental concepts and practical applications of Looker, making them suitable for preparing for interviews focusing on Looker skills and knowledge.