Building Analytics Pipelines You Can Trust
Keywords:
Data engineering, analytical workflows, lakehouse architecture, data modeling, data ingestion, data storage, data cataloging.Abstract
Data-analytical workloads are critical to operational and strategic decision-making, yet failures remain common and often unrecoverable, leaving reliability below industry norms. To address this, we propose a modern Lakehouse Engineering Blueprint that leverages native cloud capabilities to achieve the production readiness and observability required for reliable Lakehouse Pipelines. The Blueprint offers pragmatic solutions for each Lakehouse Engineering component, enabling management teams, architects, and developers to collaboratively make informed decisions that strengthen Business Intelligence, Self-Service Analytics, and Advanced Analytics capabilities.
The Blueprint’s relevance rests on two core contributions: it automates governance, quality, reliability, and observability to mitigate production issues, and it eases the architectural trade-off between schema flexibility and governing-metadata performance. Together, these bring lakehouse reliability and performance in line with industry standards and establish the prerequisites for production-ready pipelines. The Blueprint primarily supports analytics teams in operationalizing business-critical processes for Business Intelligence and Self-Service Analytics, while also enabling the models and pipelines underlying Advanced Analytics. Its scope extends beyond lakehouses to data lakes and warehouses, from cloud to hybrid environments, and across both academic and industrial Lakehouse Engineering contexts.
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