This article details a banking‑focused real‑time data‑warehouse solution that leverages Flink Table Store to handle both incremental fact‑table updates and full‑table dimension calculations, compares three traditional approaches, and explains data ingestion, query modes, export options, and future streaming‑warehouse directions.
Flink 1.16 introduces adaptive batch scheduling, speculative execution, hybrid shuffle, dynamic partition pruning, improved Hive SQL compatibility, advanced checkpoint mechanisms including changelog backend, and integrates CDC with Kafka and Table Store, offering faster, more stable, and easier-to-use stream‑batch processing capabilities.
Flink Table Store, an Apache Flink subproject, provides a unified stream‑batch storage layer with SQL‑based table APIs, addressing real‑time and offline data needs, detailing its design goals, usage patterns, architectural layers, implementation choices, and upcoming roadmap.
This article introduces Flink Table Store v0.2, explains its four primary application scenarios—offline warehouse acceleration, partial update, pre‑aggregation rollup, and real‑time warehouse enhancement—details the core lake‑storage architecture, bucket management, append‑only mode, and outlines the project’s future roadmap and trade‑off considerations.
This article examines the core trade‑offs of data warehouses—freshness, query latency, and cost—compares offline and real‑time architectures, introduces the concept of a streaming data warehouse, and details how Apache Flink Table Store aims to provide a unified, low‑cost solution.
This article explains the architecture, data model, and multi‑tenant load‑balancing strategies of Alibaba Cloud Table Store, detailing the challenges of distributed NoSQL systems and presenting practical solutions for resource quantification, fairness, trigger timing, and SLA‑driven automation.
