Architecting Distributed Data Engineering Systems for Real-Time Retail Sales Analytics
DOI:
https://doi.org/10.15662/IJEETR.2022.0406014Keywords:
Retail analytics, data engineering, stream processing, data pipelines, real-time systems, section_title, Data Ingestion and In-Flight ProcessingAbstract
Real-time retail analytics addresses immediate business inquiries through instantaneous reporting. Responses to emerging questions can be automated using real-time algorithms, but many others require human intervention, visual exploration, and ad-hoc analysis. These must be performed in true real-time, with freshness of the data being more important than the level of detail. Most commonly, responses take the form of dashboards. Real-time dashboards continuously process data streams and auto-refresh related queries. Compared to historical dashboards that depend on a data warehouse, near-real-time dashboards emerge from a data lake and offer noticeable advantages.
Delays often affect time-sensitive dashboards. At the analytical level, the data pipelines may have become blocked due to sudden spikes in traffic; delays may be acceptable in certain tolerable failure modes when data freshness is less crucial than data correctness. Nevertheless, the interactive exploration of data is a near-real-time demand and supportive of decision-makers accountable for a company's results. Late data can even be tolerated in semi-real-time dashboards. Latency continues to be a secondary criterion in the second-highest requirement category: dashboards that answer complex analytical queries with data at a greater logical level of detail.
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