The ability to process data instantaneously and send back insights to users as soon as possible is the key for a successful deployment of a real time data streaming architecture. Real time data streaming applications are considered good, only if they have very low latency.

Some of the common challenges of real time data processing are as follows:

In-memory data processing

Real time applications require most of the processing to be done in memory. The in-memory processing should handle the bare minimum tasks, which are absolutely necessary to perform the action, and any other tasks like audit entries or transactional commits should be performed asynchronously.

Caching of external data

Caching of external data is essential to perform various actions to reduce the latency. It is necessary to cache data on demand, and keep refreshing the cache at appropriate time intervals. If the cache data size is huge, it is recommended to partition the streaming process.

Managing peak volumes

Distribution of data on multiple nodes should be managed dynamically, such that during peak volumes, the number of nodes automatically increase, thereby keeping the latency constant. The logic to distribute data should be handled dynamically, such that if one distribution factor is overtly skewed, it can shift automatically to other factors.

Managing out-of-sequence events

It is necessary to include logic to identify an out-of-sequence event, and take appropriate actions to handle the scenario. This can include separating the out-of-sequence events and handle them with special memory configurations.