Dataform vs Looker: What are the differences?
Introduction
Dataform and Looker are both powerful tools for data analytics and visualization. While they have some similarities, there are key differences between the two that set them apart. In this Markdown code, I will provide six specific differences between Dataform and Looker.
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Data modeling workflow: Dataform is a data modeling tool that allows you to define and manage your data transformation workflows using SQL and JavaScript. It provides a collaborative and version-controlled environment for developing, testing, and deploying your data models. Looker, on the other hand, is a data exploration and visualization tool that sits on top of your existing data sources. It provides a user-friendly interface for exploring data and creating custom visualizations, but it does not offer the same level of control and flexibility for data modeling as Dataform.
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Data transformation capabilities: Dataform allows you to define complex data transformations using SQL and JavaScript, including aggregations, joins, and advanced calculations. It also supports incremental data loading, which can greatly improve the performance of your data pipelines. Looker, on the other hand, provides a visual interface for creating data transformations using its proprietary LookML language. While LookML offers some flexibility, it may not have the same level of expressiveness and control as SQL and JavaScript in Dataform.
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Data pipeline orchestration: Dataform provides built-in features for orchestrating and managing your data pipelines. You can schedule the execution of your data models, track their dependencies, and automatically handle incremental data loading. Looker, on the other hand, focuses more on the visualization aspect and does not offer the same level of data pipeline management capabilities as Dataform.
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Collaboration and version control: Dataform provides a collaborative environment for data modeling, allowing multiple users to work on the same project simultaneously. It also offers version control capabilities, so you can track changes to your data models and easily revert back to previous versions if needed. Looker, on the other hand, provides a more individual-focused collaboration and does not have the same level of version control features as Dataform.
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Extensibility and integrations: Dataform allows you to extend its functionality using JavaScript, which means you can integrate it with other tools and services to build more complex data pipelines. Looker, on the other hand, offers a wide range of integrations with popular data sources and platforms, but it may not have the same level of extensibility as Dataform.
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Pricing and licensing: Dataform offers a free version with limited features and a paid version with additional features and support. The pricing is based on the number of users and usage. Looker, on the other hand, offers different pricing plans based on the number of users and the level of support required. The pricing model for Looker may be more complex and may vary depending on your specific requirements.
In summary, Dataform provides more control and flexibility for data modeling and pipeline management, while Looker focuses more on data exploration and visualization. Dataform offers advanced data transformation capabilities, collaboration features, and extensibility through JavaScript. Looker, on the other hand, provides a user-friendly interface, ready-made integrations, and a strong focus on data visualization. Ultimately, the choice between Dataform and Looker depends on your specific needs and preferences in terms of data modeling, pipeline management, and visualization capabilities.
