Looker Quiz - Best Practices & Persistence Types

A Liquid code filter that transforms text to uppercase.

It's a simplified interface for users to interact with data, built in Looker.

Reusable views and explores are essential for good data modeling in Looker.

Dimensions categorize and help filter data, enabling focused analysis.

Utilizing caching and persistence helps optimize explore performance in Looker.

Measures in Looker perform calculations on data, bringing out insights.

Filters in Looker narrow down the displayed data, tailoring the view.

Joins in Looker combine data from multiple tables, enriching data analysis.

A Looker best practice that involves avoiding hardcoded values (fixed values directly embedded in code) in explores. Hardcoded values are generally discouraged because they can make your analyses less flexible and less maintainable.

The primary goal of caching in Looker is to speed up the retrieval of commonly accessed data. When a query is executed for the first time, the results are stored in the cache, making subsequent requests for the same information much faster.

Derived tables provide a powerful way to simplify complex calculations in LookML. They work by defining a separate logical table within your LookML project that encapsulates specific computations, which can be accessed by other explores (like a reusable calculation unit).

Row Level Security (RLS) in Looker allows you to control which rows of data users can see based on their attributes. This helps to ensure that users only have access to the data they are authorized to view.

Column Level Security (CLS) in Looker works by hiding specific columns from users based on their attributes or roles. This is especially useful for protecting sensitive information that shouldn't be visible to all users.

Maps in Looker are used to translate values in your data by defining a correspondence (mapping) between the original values and their desired replacements. This can be helpful for standardizing data or converting between different representations.

Liquid filters are special functions within the Looker Liquid templating language that allow you to manipulate and transform data in your reports and dashboards. They offer various operations like text formatting, calculations, and data transformations.

Reusable sets of data created in Looker to simplify data analysis and improve efficiency. They combine specific data from different tables into a single data source.

In Looker, explores are interactive interfaces that allow users to access and manipulate data through simple drag-and-drop functions, making data exploration more user-friendly for non-technical users.

Dimensions are used to categorize and filter data in Looker. They act like labels or groupings for different data points, allowing users to analyze data based on specific characteristics.