If you need timely, near real-time data but your data integration architecture prevents you from employing stream processing, micro batching is a good option to consider. Micro batching splits your data into groups and ingests them in very small increments, simulating real-time streaming. Apache Spark Streaming is actually a micro-batch processing extension of the Spark API.
2. Real-Time Processing
In real-time processing, also known as stream processing, streaming pipelines move data continuously in real-time from source to target. Instead of loading data in batches, each piece of data is collected and transferred from source systems as soon as it is recognized by the ingestion layer.
A key benefit of stream processing is that you can analyze or report on your complete dataset, including real-time data, without having to wait for IT to extract, transform and load more data. You can also trigger alerts and events in other applications such as a content publishing system to make personalized recommendations or a stock trading app to buy or sell equities. Plus, modern, cloud-based platforms offer a lower cost and lower maintenance approach than batch-oriented pipelines.
For example, Apache Kafka is an open-source data store optimized for ingesting and transforming real-time streaming data. It’s fast because it decouples data streams, which results in low latency, and it’s scalable because it allows data to be distributed across multiple servers. Learn more about Apache Kafka.
Real-time data ingestion framework: