How Looker Parameters Revolutionize Data Analytics?

In today’s data-driven world, businesses rely on insightful analytics to make informed decisions and stay ahead of the competition. Looker, a powerful business intelligence platform, empowers users to unlock the full potential of their data through dynamic reporting and analysis. One key feature that sets Looker apart is its robust parameterization capabilities, which allow users to create dynamic and interactive reports tailored to their specific needs using Looker Parameters.

What is the Looker Parameter?

There is a LookML parameter that is actually called “parameter“. The parameter parameter creates a filter-only field that can be used to filter Explores, Looks, and dashboards but that cannot be added to a result set. The value that a user selects for this filter-only field can create interactive query results, labels, URLs, and more when it is used with the {% parameter parameter_name %} and parameter_name._parameter_value Liquid variables. The parameter parameter can also simplify LookML models, as different results can be displayed in a single field.

Understanding Looker Parameters

Parameters in Looker are often used in conjunction with LookML, Looker’s modeling language, to create flexible and interactive reports. There are different types of parameters in Looker, including:

Filter Parameters: These parameters allow users to dynamically filter the data displayed in a report based on certain criteria, such as date ranges, product categories, or geographic regions.
Liquid Parameters: Liquid is Looker’s templating language, and Liquid parameters allow for dynamic text substitution within Looks and dashboards. They can be used to customize the text or labels in a report based on user input or other conditions.
User Attributes: User attributes are parameters that represent specific user characteristics, such as region, department, or role. They can be used to personalize the data displayed for each user based on their attributes.

Here’s a basic example of how you might use a filter parameter in Looker:

Let’s say you have a sales dashboard in Looker, and you want users to be able to dynamically filter the sales data by different product categories. You can create a filter parameter called “Product Category” and define it in LookML like this:

parameter: product_category {
  type: string
  allowed_value: {
    label: "Electronics"
    value: "electronics"
  }
  allowed_value: {
    label: "Clothing"
    value: "clothing"
  }
  allowed_value: {
    label: "Home Goods"
    value: "home_goods"
  }
  default_value: "electronics"
}

In this example, the parameter “product_category” is defined as a string type parameter with three allowed values: “Electronics”, “Clothing”, and “Home Goods”. The default value is set to “Electronics”.

You can then use this parameter in your SQL queries to dynamically filter the data by the selected product category.

For example:

SELECT
  order_date,
  product_name,
  quantity,
  total_sales
FROM
  sales
WHERE
  product_category = {% parameter product_category %}

When users interact with the dashboard, they will be able to select a product category from a dropdown menu, and the data displayed in the report will automatically update based on their selection.

This is just a basic example, and Looker’s parameterization capabilities are quite powerful, allowing for a wide range of customization and interactivity in your reports and dashboards.

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Reference : Looker official doc

About Me:-
I am Om Prakash Singh – Data Analytics Consultant, Looker Consultant, and Solution Architect.
I am a Highly analytical and process-oriented Data Analyst with in-depth knowledge of database types; research methodologies; and big data capture, manipulation, and visualization. Furnish insights, analytics, and business intelligence used to advance opportunity identification.

You’ve got data and lots of it. If you’re like most enterprises, you’re struggling to transform massive information into actionable insights for better decision-making and increased business results.
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How Looker Parameters Revolutionize Data Analytics?