what are the main differences between tableau and looker, and in for use cases each of them is a better choice?
Tableau and Looker are both powerful business intelligence (BI) tools, but they have distinct features, strengths, and use cases that make them suitable for different scenarios. Here are the main differences and the use cases for each:

### Main Differences

1. **Data Model and Architecture**:
   - **Tableau**: Primarily a data visualization tool that connects to various data sources. It allows users to create visualizations and dashboards without needing a specific data model. It supports live connections and extracts.
   - **Looker**: Built around a semantic model (LookML) that allows users to define and manage their data relationships and metrics. Looker requires a more structured approach to data modeling, which can be beneficial for consistency and governance.

2. **User Interface and Ease of Use**:
   - **Tableau**: Known for its intuitive drag-and-drop interface, making it easy for users to create visualizations quickly. It’s user-friendly for analysts and non-technical users.
   - **Looker**: Has a more complex interface due to its emphasis on data modeling. While it provides powerful capabilities, it might require more training for users to fully leverage its features.

3. **Data Governance**:
   - **Tableau**: Offers some governance features, but they may not be as robust as Looker's. Users can create dashboards independently, which can lead to inconsistencies if not managed properly.
   - **Looker**: Strong focus on data governance with
 Tableau and Looker are both powerful business intelligence (BI) and data visualization tools, but they have some key differences in terms of their architecture, features, and use cases. Here are some of the main differences between the two:

1. Architecture: Tableau is a desktop-based tool with a focus on visual analysis and data discovery, while Looker is a web-based tool that is more focused on governed data analysis and reporting. Tableau requires data to be loaded into its own data engine, while Looker is designed to work with existing data warehouses and databases.
2. Data modeling: Looker has a more robust data modeling layer, which allows for more complex data transformations and calculations. This makes Looker a better choice for organizations with complex data architectures or those that require advanced data modeling capabilities.
3. Collaboration and sharing: Tableau has more advanced features for data storytelling and dashboard sharing, including the ability to create interactive dashboards and publish them to the web. Looker is more focused on providing access to data and insights within existing workflows, such as through integrations with other business applications.
4. Pricing: Tableau is generally more expensive than Looker, particularly for larger organizations or those with more complex data needs. Looker's pricing is based on the amount of data usage, which can make it a more cost-effective choice for smaller organizations or those with