You want to migrate an existing on-premises application to Google Cloud. Your application supports semi-structured data ingested from 100,000 sensors, and each sensor sends 10 readings per second from manufacturing plants. You need to make this data available for real-time monitoring and analysis. What should you do?

The scenario describes a classic Internet of Things (IoT) time-series use case with extremely high write throughput (100,000 sensors 10 readings/sec = 1,000,000 writes/sec). Bigtable is a fully managed, scalable NoSQL wide-column database designed for large analytical and operational workloads with very low latency. It excels at ingesting massive volumes of time-series data and serving it for real-time monitoring dashboards that require single-digit millisecond read latency. Its flexible schema is also ideal for the semi-structured nature of sensor data. This makes Bigtable the most appropriate choice to meet all the requirements of high-throughput ingestion, real-time access, and support for semi-structured data.

A. Deploy the database using Cloud SQL. Cloud SQL is a relational database service (OLTP) not designed for the massive write throughput (1M ops/sec) and semi-structured data model required by this IoT use case.
B. Use BigQuery, and load data in batches. Loading data in batches is inherently not real-time and introduces significant latency, which contradicts the "real-time monitoring" requirement. BigQuery is an analytical data warehouse, not an operational database for low-latency point reads.
D. Deploy the database using Cloud Spanner. Cloud Spanner is a globally distributed, strongly consistent relational database. It is not the optimal or most cost-effective solution for this specific high-volume, time-series ingestion workload, which typically does not require Spanner's strong transactional consistency guarantees. ---

1. Google Cloud Documentation - Bigtable Use Cases: "Bigtable is ideal for applications that need to handle large amounts of data with very low latency... Common use cases include... Time series data, such as CPU and memory usage over time for multiple servers... Internet of Things (IoT) data, such as energy consumption reports from smart meters and home appliances." This directly aligns with the question's scenario.

Source: Google Cloud Documentation, "About Bigtable," Use cases section.

2. Google Cloud Documentation - Schema Design for Time Series Data: "Bigtable is a good choice for storing and tracking time-series data... A time-series schema in Bigtable is a design that optimizes for time-series data." This document provides specific guidance on using Bigtable for IoT sensor data, confirming it as the purpose-built solution.

Source: Google Cloud Documentation, "Bigtable Schema Design for Time Series Data."

3. Google Cloud Solutions - Architecture for IoT on Google Cloud: Reference architectures for IoT frequently depict a pattern where streaming data is ingested into Bigtable for real-time operational dashboards and simultaneously into BigQuery for long-term analytics. This highlights Bigtable's primary role in the real-time monitoring component.

Source: Google Cloud Solutions, "Connected factory on Google Cloud," Architecture diagram and component descriptions.

4. Google Cloud Documentation - BigQuery Data Loading: "Batch loading involves loading source data into a BigQuery table in a single batch operation... For time-sensitive data, you can stream data into BigQuery one record at a time." The option explicitly states "load data in batches," which is not a real-time method.

Source: Google Cloud Documentation, "Introduction to loading data from files."