An Introduction to LookML

LookML, Looker's proprietary modeling language, is specifically designed to define the relationships, transformations, and business metrics within data, making it an integral part of constructing a semantic layer within the Looker platform. Its unique features and capabilities offer a blend of simplicity and power, enabling a more streamlined and accurate approach to data analytics. Let's explore LookML in more detail.

Key LookML Objects

Model

• Entry point for queries

• Defines set of explores

Explore

• Represents a queryable view or table

• Base for user queries

View

• Maps to a database table

• Contains dimensions and measures

Dimension

• Represents a column in the table

• Queryable field

Measure

• Defines a calculation on table data

• Used for aggregations
  

Defining Data Relationships

One of the core functionalities of LookML is its ability to define complex data relationships. This is crucial in a semantic layer, as it allows the representation of how various data entities interact with each other. For example, LookML can specify how a customer entity relates to orders, or how products relate to categories, using straightforward, human-readable syntax. This clarity ensures that everyone in the organization understands these relationships in the same way, reducing ambiguity.

Example of defining a view:

view: orders {
  sql_table_name: public.orders;;

  dimension: id {
    type: number
    sql: ${TABLE}.id ;;
  }

  dimension: order_date {
    type: date
    sql: ${TABLE}.order_date ;;
  }

  dimension: customer_id {
    type: number
    sql: ${TABLE}.customer_id ;;
  }
}

Data Transformations

LookML enables developers to specify transformations directly within the data model. These transformations can range from simple calculations, like summing up sales figures, to more complex aggregations and manipulations, such as calculating moving averages or converting currencies. By defining these transformations within the semantic layer, LookML ensures that they are consistently applied across all reports and analyses, leading to more reliable insights.

An example of creating a join:

explore: orders {
  join: customers {
    sql_on: ${orders.customer_id} = ${customers.id} ;;
    relationship: many_to_one
  }
}

Defining Business Metrics

Business metrics are key performance indicators that organizations monitor to assess their performance. With LookML, these metrics are defined once, at the semantic layer, and then reused across various reports and dashboards. This approach not only saves time but also ensures that metrics are consistent across the organization. Whether it's revenue, customer acquisition cost, or churn rate, LookML ensures that everyone in the organization calculates these metrics in the same way.

An example of defining a metric:

measure: total_sales {
  type: sum
  sql: ${sale_price} * ${quantity} ;;
}

Streamlining Reporting Processes

By using LookML to construct the semantic layer, organizations can streamline their reporting processes. Since all data definitions, relationships, and transformations are centralized within the Looker platform, creating new reports or dashboards becomes a matter of leveraging these pre-defined elements. This significantly reduces the time and effort required to generate new insights, enabling a more agile response to business questions.

Single Source of Truth

One of the most significant advantages of using LookML as the foundation of your semantic layer is the establishment of a single source of truth for your data. With all data relationships, transformations, and metrics defined in one place, there's no ambiguity about which data or calculations to use. This not only enhances data integrity but also boosts trust in the data across the organization.

LookML's structured approach to defining data models ensures high data integrity and consistency. Since every report and dashboard relies on the same underlying definitions, inconsistencies are minimized, leading to more accurate and reliable analytics. This consistency is vital for making informed decisions based on data, as it ensures that all stakeholders are working from the same, accurate set of information.