LookML explained

LookML: Empowering Data Scientists with Efficient Data Modeling and Analysis

Table of contents

LookML, an acronym for Looker Modeling Language, is a powerful tool that empowers data scientists, AI/ML practitioners, and data analysts to efficiently model and analyze data. LookML serves as the modeling layer of Looker, a widely-used Business Intelligence and data analytics platform. With LookML, users can define data models, explore and analyze data, and create visualizations, enabling them to gain valuable insights and make data-driven decisions.

The Essence of LookML

LookML is a declarative language designed specifically for data modeling. It allows users to define and organize the structure of their data, specify relationships between different data tables, and define calculations and transformations on the data. By abstracting away the complexity of SQL, LookML provides a more user-friendly and intuitive way to work with data.

At its core, LookML is a collection of files written in a human-readable syntax. These files typically have a .lookml extension and can be version-controlled, promoting collaboration and reproducibility. LookML files are organized into projects, each containing one or more models. Models represent specific domains or areas of analysis within an organization, such as sales, marketing, or Finance.

Origins and Evolution

LookML was developed by the team at Looker, a business intelligence platform founded in 2012. Looker aimed to simplify the process of data exploration and analysis, allowing users to leverage the power of SQL without the need for deep expertise in the language. LookML emerged as the solution to bridge the gap between SQL and user-friendly data modeling.

Over the years, LookML has evolved to become a mature and versatile language. It has gained popularity among data teams and organizations of all sizes, thanks to its flexibility, scalability, and ability to handle complex data models. Looker was acquired by Google in 2019, further solidifying LookML's position as a leading data modeling language in the industry.

LookML in Action: Examples and Use Cases

To understand LookML's capabilities, let's explore some examples of its usage:

1. Defining Data Models: LookML allows data scientists to define the structure of their data models using simple and intuitive syntax. For instance, they can specify dimensions (e.g., customer name, order date) and measures (e.g., total sales, average order value) that form the basis of analysis.

dimension: customer_name {
  type: string
  [SQL](/insights/sql-explained/): ${TABLE}.name ;;
}

measure: total_sales {
  type: number
  sql: sum(${TABLE}.sales) ;;
}

2. Creating Relationships: LookML enables users to establish relationships between different data tables, facilitating complex analyses. By defining join conditions, users can combine data from multiple tables into a single coherent view.

view: orders {
  sql_table_name: my_database.orders ;;

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

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

  measure: order_total {
    type: number
    sql: sum(${TABLE}.total) ;;
  }

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

3. Calculations and Transformations: LookML allows users to define calculations and transformations on the data, enabling advanced analysis and reporting. These calculations can range from simple arithmetic operations to complex aggregations and statistical functions.

measure: average_order_value {
  type: number
  sql: ${total_sales} / count_distinct(${order_id}) ;;
  value_format_name: usd
}

4. Data Exploration and Visualization: LookML integrates seamlessly with Looker's visualization tools, allowing users to create interactive dashboards and reports. By leveraging LookML's capabilities, data scientists can explore data, create custom visualizations, and share insights with stakeholders.

Career Aspects and Relevance

Proficiency in LookML can be a valuable asset for data scientists, AI/ML practitioners, and data analysts. Mastery of LookML enables professionals to efficiently model and analyze data, providing them with a competitive edge in the industry.

Organizations across various sectors rely on Looker and LookML to drive data-driven decision-making. LookML's ease of use, scalability, and collaboration features make it a popular choice for teams working with complex data models. By leveraging LookML, data professionals can streamline their workflows, enhance Data governance, and deliver insights to stakeholders in a more efficient and accessible manner.

In terms of career growth, LookML proficiency can open doors to a range of opportunities. Companies actively seek professionals with LookML expertise to leverage their data assets effectively. Positions such as LookML Developer, LookML Analyst, or Business Intelligence Engineer often require LookML skills. Moreover, LookML's underlying principles and concepts align with broader data modeling and analysis practices, making it a transferable skillset.

Best Practices and Standards

To make the most of LookML, it is essential to follow some best practices:

1. Modularization: Break down LookML files into reusable and modular components, promoting code maintainability and reusability.
2. Documentation: Document data models, dimensions, measures, and any customizations made in LookML files to enhance code understandability and facilitate collaboration.
3. Version Control: Utilize version control systems such as Git to track changes, collaborate with teammates, and ensure reproducibility.
4. Performance Optimization: Optimize LookML code for performance by leveraging database-specific optimizations, such as using SQL snippets and aggregating data at the appropriate level.
5. Testing and Validation: Implement robust testing and validation processes to ensure the accuracy and integrity of LookML models and calculations.

By adhering to these best practices, data professionals can create scalable, maintainable, and efficient LookML models.

Conclusion

LookML, the modeling layer of Looker, empowers data scientists and analysts to efficiently model and analyze data. With its declarative syntax, LookML simplifies data modeling, enables complex analyses, and facilitates the creation of interactive visualizations. LookML proficiency is highly sought after in the industry, offering career opportunities in data-driven organizations. By adhering to best practices and leveraging LookML's capabilities, data professionals can unlock the true potential of their data and drive impactful insights.

References:

1. LookML Documentation: https://docs.looker.com/data-modeling/learning-lookml/intro-to-lookml
2. Looker Modeling Language (LookML) - Wikipedia: https://en.wikipedia.org/wiki/Looker_(company)#Looker_Modeling_Language_(LookML)
3. Looker: https://looker.com/