Technically integrated data fields with identical names can represent different business events, leading to misleading analytics unless semantic metadata aligns the meaning behind each value.
This article details how Taobao's instant retail team replaced their Kafka-Flink-Paimon stack with Apache Fluss to achieve second-level real-time decisions, using Delta Join for stateless dual-stream joins, Partial Update for decoupled wide-table writes, and Auto-Tiering to eliminate sync jobs, while contrasting Ele.me's Paimon+StarRocks evolutionary path.
The article defines high-quality datasets in the AI era by six dimensions—accuracy, completeness, consistency, timeliness, understandability, and traceability—explains why AI amplifies data quality requirements, and outlines a five-step engineering pipeline (aggregation, cleaning, unification, validation, traceability) to transform raw data into reliable AI-ready datasets, emphasizing that continuous data engineering, not data hoarding, enables AI success.
Alibaba Mama evolved its advertising real-time data warehouse from a message queue-based architecture to a Flink + Paimon streaming lakehouse, solving structural defects like lack of deduplication, aggregation layer support, and schema enforcement by leveraging Paimon's primary key upsert and changelog capabilities, while sharing production optimization practices and comparing storage choices for advertising vs. risk control scenarios.
This article analyzes two financial risk control lakehouse architectures: DiDi uses StarRocks to unify real-time and batch feature serving with primary-key tables and bucket pruning, while Ant Group replaces HBase and Elasticsearch with Apache Paimon's first-row, partial-update, and sort-compaction mechanisms for long-window deduplication, fast reconciliation, and historical lookback.
Google's BigQuery Graph with Measures integrates governed metrics with property graphs to give AI agents both accurate calculations and traversable business relationships, enabling root-cause analysis beyond simple metric queries, with zero-ETL mapping from existing tables and Looker integration for unified semantic models.
The article analyzes two online travel case studies: Tongcheng Travel migrating from Hudi to Paimon to solve slow large-table real-time writes using LSM-tree storage, and Trip.com adopting StarRocks with materialized views to unify lake queries and eliminate data warehouse copies, detailing migration methodologies, architectural reasoning, and production troubleshooting.
This article explains the three-layer metric hierarchy—atomic, derived, and composite metrics—with e-commerce examples, shows how to define each layer, highlights four common pitfalls like mismatched granularities and ratio averaging, and outlines a five-step process to build a governed metric system from scratch.
This article outlines the eight core modules of enterprise data architecture—data source, integration, storage, processing, modeling, governance, service, and operations—explaining how each layer ensures data flows reliably from source systems to business consumption while maintaining quality, consistency, and operational stability.
This article outlines a phased roadmap for building lakehouse platforms — from 0 to 1 validation, 1 to N isolation, to storage-compute separation and cost governance — emphasizing that architecture must match the current business contradiction, not chase industry trends.
This article presents a complete methodology for lakehouse performance stress testing and capacity planning, covering TPC-DS benchmark adaptation, end-to-end bottleneck identification across CPU, memory, disk, and network, a quantitative capacity derivation framework, and vertical versus horizontal scaling decision criteria for components like Kafka, Flink, Paimon, and StarRocks.
This article provides a deep technical analysis of Apache Flink's checkpoint mechanism, covering barrier alignment and unaligned snapshots, heap vs RocksDB state backends with incremental checkpoints, savepoint vs checkpoint differences, failure recovery flows, and practical troubleshooting for checkpoint timeouts, state bloat, and recovery slowness.
This article deep-dives into Apache Paimon's kernel source code, explaining the LSM write path from memory buffer to Sorted Runs, compaction scheduling trade-offs, four changelog production modes, and two-phase commit with Flink checkpoints, providing troubleshooting guidance for compaction backlog, changelog latency, and snapshot bloat.
This article presents a comprehensive methodology for implementing real-time lakehouse architectures, covering requirements quantification, architecture selection, capacity planning, canary deployment, rollback strategies, and SLA decomposition with a six-stage pipeline, e-commerce case study, troubleshooting guides, and interview frameworks.
Alibaba Cloud unveiled a strategic upgrade to its ODPS big data platform at the 2026 Yunqi Conference, introducing AI-native, multimodal capabilities across MaxCompute, Hologres, and DataWorks to support Agentic AI workloads with vector search, heterogeneous compute, data sandboxes, and semantic knowledge graphs.
At Yunqi 2026, Alibaba Cloud unveiled its Agentic Lake + Agentic Streaming architecture, combining DLF, Flink, EMR Ray/Daft, StarRocks, and Milvus to provide a unified multimodal data infrastructure for AI agents in autonomous driving, embodied intelligence, and enterprise operations.
This article explores how Dataphin applies Spec-Driven Development (SDD) through a Plan mode to address the unreliability of vibe coding in production data development, detailing SDD principles, industry tool comparisons, and a structured workflow for requirement understanding, intent clarification, solution planning, task decomposition, and verified execution.
StarRocks now fully supports Paimon 2.0, integrating columnar analytics with vector, full-text, and scalar indexes into a single SQL execution framework for multimodal lakehouse workloads, enabling unified data management, retrieval, and relational analysis on object storage without data movement.
At Yunqi 2026, Alibaba Cloud unveiled a strategic upgrade to its ODPS big data platform, introducing agent-native architecture and multimodal computing across MaxCompute, Hologres, and DataWorks to support large model data refinement, embodied intelligence, and autonomous data operations with SQL AI, vector search, data sandboxes, and semantic knowledge graphs.
The article presents a unified data development platform where Forge Agent acts as an AI facade, Sindri handles deterministic planning via DSL, ADF manages execution and scheduling, and quality systems ensure trustworthiness, moving beyond chat interfaces to controlled, automated data production.
