Here are ten key aspects that differentiate Google Cloud BigQuery from other data analytics and database solutions:

1. Serverless architecture: BigQuery operates on a serverless architecture, which means users don't need to manage infrastructure, servers, or clusters. This allows for easy scalability and eliminates the need for resource provisioning.

2. Scalability and performance: BigQuery can handle petabytes of data and can scale seamlessly to accommodate growth. It offers high-performance querying, even on massive datasets, thanks to its distributed architecture and columnar storage.

3. SQL-based querying: BigQuery uses a familiar SQL-like syntax for querying data, making it easy for users with SQL knowledge to work with the platform. It supports a wide range of SQL functions and operators.

4. Real-time streaming ingestion: BigQuery allows for real-time streaming ingestion of data through its streaming API. This enables users to analyze data as it arrives, without the need for batch processing.

5. Integration with Google Cloud ecosystem: BigQuery seamlessly integrates with other Google Cloud services, such as Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow, enabling users to build end-to-end data pipelines and leverage the broader Google Cloud ecosystem.

6. Machine learning capabilities: BigQuery has built-in machine learning capabilities through BigQuery ML, which allows users to create and execute machine learning models directly within BigQuery using SQL-like syntax, without the need for specialized machine learning expertise.

7. Data sharing and collaboration: BigQuery enables easy data sharing and collaboration through its dataset and table-level access controls, as well as its ability to create authorized views and share query results with others.

8. Cost-effectiveness: BigQuery offers a pay-per-use pricing model, where users are charged based on the amount of data processed by their queries. This can be more cost-effective compared to solutions that require upfront infrastructure investments.

9. Security and compliance: BigQuery provides robust security features, including encryption of data at rest and in transit, identity and access management (IAM), and integration with Google Cloud's security tools. It also complies with various industry standards and regulations.

10. Geospatial capabilities: BigQuery has native support for geospatial data types and functions, allowing users to analyze and visualize geospatial data directly within the platform.

These aspects demonstrate how BigQuery differentiates itself as a highly scalable, serverless, and SQL-based data warehouse solution that integrates well with the Google Cloud ecosystem and offers advanced capabilities such as machine learning and geospatial analysis.