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DataFunSummit2024, co-hosted by Bilibili, convenes industry experts, scholars, and enterprise leaders across six forums to discuss next‑generation data architecture, showcasing Bilibili’s Iceberg‑based stream‑batch innovations, AI‑BI analytics, NoETL practices, and emerging alternatives to Lambda architecture.
This article explains the Lambda architecture—a three‑layer model combining batch and real‑time processing for large‑scale data, outlines its components, advantages, disadvantages, common tools, and compares it with the Kappa alternative while providing practical insights for data engineers.
This article presents a comprehensive overview of Xiaomi's sales data warehouse, detailing its development history, dimensional modeling theory, multi‑layer architecture, Lambda design with batch and streaming processing, capability layers, security measures, and answers to common technical questions.
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This article presents a comprehensive overview of the evolution from traditional Lambda‑based real‑time data warehouse solutions to a data‑lake‑integrated architecture, detailing the shortcomings of legacy designs, the iterative improvements made at JD Technology, and the technical and operational challenges encountered during implementation.
This article details Xiaomi's sales data warehouse development, covering its history, architecture, dimensional modeling, layer design, streaming‑batch integration, governance, security, and future directions, while also addressing practical Q&A on implementation challenges and best practices.
This article presents a comprehensive overview of JD Tech's real‑time data warehouse evolution, detailing the legacy Lambda‑based design, its shortcomings, the transition to a data‑lake‑integrated architecture, iterative improvements, encountered technical and non‑technical issues, and future outlooks.
Airbnb’s Riverbed framework unifies streaming CDC events and batch Spark jobs behind a GraphQL‑based declarative API to automatically build and maintain distributed materialized views, using Kafka‑partitioned ordering and version control to deliver billions of daily updates with low‑latency reads for features such as payments and search.
This article presents Xiaomi's sales data warehouse practice, detailing its evolution, positioning, dimensional modeling, layered architecture, Lambda design, Iceberg integration, capability building, security governance, and future directions toward data value and real‑time metrics.
This article presents a comprehensive overview of Xiaomi's sales data warehouse, covering its evolution, dimensional modeling and layer theory, Lambda architecture with batch and streaming processing, capability layers, security measures, and future trends toward real‑time metricization and data value creation.
The article explains why unified data architecture is essential to avoid duplication and inefficiency, discusses differing performance trade‑offs among batch, streaming, and interactive analytics, introduces an incremental computation model that unifies these modes, and invites readers to a Dec 19, 2023 technical sharing event.
This article introduces Xiaomi's sales data warehouse practices, covering its development history, positioning, architecture, dimensional modeling, layer theory, capability building, real‑time and batch processing using Lambda architecture, Iceberg, Flink, and Hologres, and discusses future trends and Q&A.
This article introduces the Lambda Architecture for billion‑scale real‑time data analysis, explains its three layers—Batch, Speed, and Serving—covers its flexibility, fault tolerance, and scalability, and demonstrates concrete applications such as Twitter hashtag analysis and a smart‑parking recommendation system.
This article explains the Lambda Architecture—a three‑layer big‑data processing model combining batch and speed layers to deliver accurate, low‑latency analytics, and illustrates its use with Twitter hashtag tracking and a smart‑parking recommendation system.
This article presents Xiaohongshu's design and implementation of a unified streaming‑batch storage system that integrates Lambda architecture, Kafka, Flink, Iceberg, and modern OLAP engines to solve real‑time data warehouse pain points and enable consistent, exactly‑once analytics across streaming and batch workloads.
The article explains how enterprises can build a comprehensive big data analytics platform—covering data collection, storage, computation, and decision layers—by clarifying business scenarios, choosing appropriate on‑premise or cloud deployment, selecting suitable architectures such as Lambda/Kappa, and addressing component choices and emerging technical trends.
This article explains the evolution of data warehouses, the need for real‑time processing, the classic ODS‑DW‑APP layering, compares offline, Lambda, Kappa, and the newer Omega architectures, and discusses how cloud‑native databases enable a unified real‑time lake‑warehouse solution.
The article details Zhihu's data empowerment team's design and implementation of a low‑cost, high‑response real‑time data platform built on Apache Doris, covering real‑time business metrics, algorithm features, and user profiling, and explains the challenges, architectural choices, tooling, performance gains, and future directions.
The article explains why enterprises need real-time data warehouses, compares Lambda and Kappa architectures, describes Ctrip Hotel's Lambda‑plus‑OLAP variant built with Flink and StarRocks, and details practical solutions for ordering, wide‑table generation, and data validation that enable billion‑row, low‑latency analytics.
This article presents a comprehensive overview of evolving big‑data architectures—from classic offline warehouses to Lambda and Kappa models—and details a streaming‑batch integrated solution that addresses latency, data freshness, and multi‑table join challenges to achieve minute‑level real‑time multi‑dimensional analytics.