Real-time analytics solutions based on event streaming generates several challenges of interactive data at scale. The event-based data processing pattern assists you in moving from point queries against static data. Overall, it's possible to gain insights from data before persisting in the analytics repository.
Enterprises achieve a tremendous advantage of gathering interactive data processing for business challenges along with the capability of archiving the data for long-term storage in stable repositories in order to perform traditional historical data analysis:
Lambda Architecture typically helps in balancing high availability, fault-tolerance, throughput, latency, the reliability of data at scale with batch processing for historical data analytics, computing data in small jobs as well as processing data at an instance in real-time streams to provide interactive data analytics with visualizations. It consists of three main layers: