Implement a batch processing system using Databricks notebooks and scheduled jobs to process transactions in predefined intervals, ensuring compliance through manual audits.

Deploy Apache Kafka for high-throughput data ingestion, utilize Databricks Structured Streaming for real-time processing with built-in fault tolerance, and store the results in a Delta Lake for ACID transactions and regulatory compliance.

Develop a custom Spark Streaming application for data ingestion and processing, and use a NoSQL database for storage to achieve low latency, relying on external tools for compliance monitoring.

Adopt a third-party cloud-based real-time analytics platform for end-to-end data processing and storage, using Databricks for ad-hoc analysis and reporting, with compliance ensured by the platform's certifications.