The urgency for IT leaders to bring real-time analytics to their organizations is stronger than ever, as the ability to start with fresh data and combine streaming, transactional, and analytical workloads in a single system can revolutionize operations. Data architects must carefully consider streaming semantics when moving from batch to real time, weighing factors such as bandwidth conservation and duplicate record prevention. Different types of streaming semantics, including at most once, at least once, and exactly-once, offer varying trade-offs between data reliability and efficiency. Achieving exactly-once semantics is ideal for operational applications, while SingleStore Pipelines can simplify the process by ensuring real-time data ingestion at scale from message brokers like Apache Kafka.