Apache Flink vs Apache Spark vs Impala

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.