You can choose any tools for a system of any size, e.g., 1TB, AMISO. Define the requirements, justify the tools you would select, and explain your reasoning.

When selecting tools for a system of arbitrary size, start by defining key requirements: scalability, performance, reliability, cost, operational complexity, and compliance. For a 1TB data set, a relational database like PostgreSQL may suffice for transactional workloads, but if you need horizontal scaling or high write throughput, consider a distributed SQL engine such as CockroachDB or a NoSQL store like Cassandra. For batch processing, Spark or Flink provide scalable compute; for real‑time ingestion, Kafka or Pulsar offer durable, fault‑tolerant streams. Orchestration can be handled by Airflow or Prefect to schedule jobs and manage dependencies. Justify each choice by mapping it to the requirements: e.g., Kafka for decoupled ingestion, Spark for large‑scale transformations, PostgreSQL for ACID guarantees, and Airflow for visibility and retry logic. Also consider operational factors: open‑source vs. managed services, vendor lock‑in, and team expertise. This structured approach ensures the toolset aligns with both functional and non‑functional needs.