You're tasked with implementing a real-time analytics solution for a global e-commerce company. The system must be able to process both batch and stream data efficiently. What combination of GCP services should you use to meet these requirements?

Cloud Dataflow is a fully managed service for executing Apache Beam pipelines, making it ideal for both batch and stream processing. Pub/Sub is a real-time messaging service that ingests streaming data efficiently, acting as the data source for Dataflow. BigQuery is a highly scalable, serverless data warehouse designed for analytics, perfect for storing and querying the processed data. This combination provides a robust, scalable, and real-time analytics solution. Cloud Dataprep is for visual data preparation, not real-time processing. Data Catalog is for metadata management. Firestore is a NoSQL document database, not primarily for analytical warehousing. Cloud Spanner is a globally distributed relational database, not a stream processing engine. Cloud Functions are serverless event-driven functions, and Cloud Scheduler is for automating tasks, neither of which are core components for a real-time stream processing pipeline.