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The article presents a cloud‑native distributed Data Warebase that unifies relational, NoSQL, search and analytical capabilities into a single horizontally‑scalable system, reducing data‑access barriers and improving developer productivity while addressing consistency, latency, cost and operational complexity.
The article describes how a real‑time data warehouse supporting ad‑delivery metrics adopts both technical and business‑side chaos‑engineering, using red‑blue team drills to inject faults, monitor indicator anomalies, and refine response procedures, thereby enhancing early risk detection, system resilience, and overall data stability for the advertising platform.
The DataFun Summit 2023 Real‑Time Computing Forum showcases a series of expert talks on Apache Flink, stream‑batch integration, cloud‑native streaming databases, and large‑scale real‑time data warehousing, featuring speakers from Alibaba Cloud, Taobao, Didi, Ant Group and RisingWave.
The article explains the fundamental differences between OLTP (online transaction processing) and OLAP (online analytical processing), describes how ETL bridges the two, and provides a detailed side‑by‑side comparison of their characteristics, purposes, and design considerations.
OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are two distinct data processing systems—OLAP optimized for complex, multidimensional analysis and business intelligence, while OLTP handles high‑volume, real‑time transactional workloads—each serving different purposes, and often used together to drive data‑driven decision making.
The article presents a technical Q&A covering the challenges of wide tables and historical data retrieval, the recommendation to separate public and private tracking parameters, methods for validating tracking data using automated rules and manual checks, and announces DataFun's 5‑year anniversary series on big data and AI.
This article presents a detailed overview of Apache Spark's Adaptive Query Execution evolution, its optimization techniques, and performance gains, followed by an in‑depth discussion of Apache Kyuubi's architecture, security integrations, cloud‑native capabilities, and practical Rebalance + Z‑Order strategies that enhance data‑warehouse task efficiency and query performance.
The talk explains how NetEase Cloud Music leverages multi‑dimensional analysis across its social innovation apps, detailing business background, use‑case scenarios, technology choices, data‑platform architecture, and future plans to enable self‑service analytics and improve data efficiency.
This article explains how to construct a real‑time data warehouse by combining Flink CDC for end‑to‑end change data capture with StarRocks' high‑performance OLAP engine, detailing the architectural challenges, optimization techniques, and a practical e‑commerce case study.
This article explains OLAP as a core data‑warehouse component that enables fast, multidimensional analysis for business intelligence, describes the structure of OLAP cubes, outlines basic operations such as drill‑down, roll‑up, slice‑and‑dice and pivot, compares MOLAP, ROLAP and HOLAP, and discusses OLAP’s relationship with OLTP and cloud architectures.
This article explains how data-driven decision making evolves from simple Excel and Python analysis to robust data warehouses, introduces star‑schema dimension modeling, and demonstrates practical integration of Alibaba Cloud MaxCompute with Tableau for interactive OLAP‑style analytics.
Greenplum is an open‑source, massively parallel processing (MPP) database built on PostgreSQL, offering ANSI‑SQL compliance, distributed ACID transactions, linear scalability, polymorphic storage, advanced optimizers, and extensive ecosystem integrations, making it suitable for large‑scale data warehousing, analytics, and big‑data workloads.