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Batch processing extracts the data at a specific time and then applies transformations on it. The differences in schemas of incoming data sources during processing aren’t that significant and can be resolved in a data pipeline. Batch data pipeline travels between various data teams and collaboration becomes difficult. The tools available for batch processing are hard to learn and deployment is difficult as well.

In contrast stream processing systems have more than one data source each having its own schema different from others and its own requirements. Data is transformed and analyzed for each source in parallel.There are multiple target systems that request data simultaneously and it’s hard to troubleshoot if something goes wrong.

However, by resolving the challenges associated with stream processing an efficient data pipeline can be designed. One of the platforms that provides low-code solutions for stream processing is Memphis.dev. Memphis.dev is the only low-code real-time data processing platform that offers a full ecosystem for in-app streaming use cases using a produce-consume paradigm that supports modern in-app streaming pipelines and async communication by removing frictions of management, cost, resources, language barriers, and time for data-oriented teams, in contrast to other message brokers and queues that require extensive coding and time.

It provides support for maintaining and defining schemas, collecting data from multiple sources and taking actions based on events. It integrates with a variety of other third party tools as well. Memphis.dev gives stream processing an advantage over batch processing.ה