Query engines are powerful tools for processing and retrieving data efficiently, but not all are designed to meet the speed and real-time demands of modern apps. A query engine is a core component of a database management system that processes and executes user queries to retrieve and manipulate data efficiently. It consists of four key components: input query parsing, planning/optimization of the query, query execution to interface with the underlying storage to fetch the data, and formatting and returning results. Examples of query engines include PostgreSQL for relational data, MongoDB for document-based queries, Elasticsearch for search and analytics, and Redis for real-time data retrieval, search, and secondary indexing. Query engines offer several benefits, including unlocking actionable insights, simplifying access to complex data, enabling advanced operations, simplifying data exploration, improving developer productivity, optimizing resource efficiency, and delivering low-latency queries, high-speed aggregations, high throughput, scalability, in-memory data processing, real-time analytics, ease of integration, and more. When selecting a query engine, look for systems that deliver real-time performance, not delays. Key features to consider include low-latency queries, high-speed aggregations, high throughput, scalability, in-memory data processing, real-time analytics, ease of integration, and a focus on delivering instant responses even for complex queries. To get the most out of your query engine, size your query environment properly, write efficient queries, optimize query performance, monitor CPU, disk, and memory utilization, implement and enforce data governance, maintain high data quality, test in a dev/test environment prior to production rollout, and consider the intersection with AI applications. Redis Query Engine is particularly powerful for companies building GenAI apps, offering multi-threading capabilities, sub-second response times, and performance that can handle unique demands of AI apps such as real-time retrieval-augmented generation (RAG).