Google Cloud BigQuery is a fully managed, serverless data warehouse that enables scalable and cost-effective data analysis. Here’s an overview of its key features and usage:

Using BigQuery

1. Setting Up: To start using BigQuery, you’ll first need to create a project in the Google Cloud Console. After creating a project, you can enable the BigQuery sandbox to begin loading data and querying datasets. BigQuery uses GoogleSQL for table creation and querying, which is ANSI SQL compliant​​.

2. Storage Optimization: BigQuery’s storage cost is based on the amount of data stored. The first 10 GB per month is free. For data not updated in 90 days, BigQuery automatically applies a 50% discount for long-term storage. To improve query performance and manage data efficiently, you can use BigQuery’s table partitioning and clustering features​​.

3. Data Ingestion: BigQuery allows data import in various formats like CSV, JSON, Avro, Parquet, and ORC. You can use the BigQuery UI, command line interface (CLI), or REST API for data loading. Cloud Dataflow and Cloud Dataproc are also viable options for data pipeline creation. Data ingestion is free, but there are quotas and limitations to consider​​.

4. Querying Data: BigQuery enables querying massive datasets with SQL, without the need for infrastructure management or a database administrator. It offers a pay-as-you-go model, where the first 1TB of data queried each month is free, and subsequent usage is charged at $6.25 per TB​​.

5. Integration with Other Google Cloud Services: BigQuery integrates seamlessly with other Google Cloud services like Google Cloud Storage, Pub/Sub, Dataflow, and BigQuery ML for machine learning. These integrations enhance its capabilities for data analysis and management​​.

6. Cost Management: To manage costs effectively, you should optimize data storage and query performance. Strategies like setting table expiration and early data filtering in queries can help reduce costs​​.

7. Real-World Applications: BigQuery is versatile, supporting various applications like data warehousing, real-time analytics, machine learning, and IoT data analysis. For example, it can analyze retail sales data, monitor website traffic in real-time, and build machine learning models for customer churn prediction​​.

Overall, BigQuery stands out for its ease of use, scalability, and integration with the broader Google Cloud ecosystem, making it a powerful tool for organizations looking to leverage their data for meaningful insights.