Although data production continues to surge—reaching 52.26 ZB in 2025—the “big data” label is fading because its original narrative of scale as value has run out, exposing a credit‑and‑responsibility gap that forces organizations to demand concrete business impact rather than mere infrastructure.
EMR Serverless Spark now supports scalar‑vector hybrid search via DLF Global Index, allowing a single Spark SQL statement to perform vector similarity and scalar filtering together, eliminating data movement, reducing latency, and boosting performance for scenarios such as autonomous driving, e‑commerce, and knowledge‑base retrieval.
The article analyzes the challenges of AI‑generated SQL in a data‑warehouse environment—context loss, unstable rule enforcement, and token overflow—and presents a five‑layer Harness architecture that persists constraints, injects hooks, uses subagents, and refactors SKILL files, raising SQL compliance from 70‑80% to over 95% while reducing context compacting.
This guide walks through setting up a Spring Boot 3.5 environment, configuring Flink 1.20 and Flink CDC 3.5, preparing MySQL tables, and using both the Flink CDC CLI and SQL client to achieve near‑millisecond synchronization of data from MySQL to Elasticsearch, including custom sink programming and real‑time monitoring via the Flink Web UI.
The article analyzes why data‑governance projects still fail when combined with AI, presents a four‑layer NL2SQL architecture, details agent responsibilities, metadata‑governance methods, anomaly‑diagnosis and permission‑control flows, outlines dataset‑building stages, evaluation metrics, and provides a step‑by‑step rollout roadmap.
Xiaohongshu transformed its data platform from a simple ClickHouse‑based ad‑hoc analysis to a Lambda‑style architecture and finally to a lakehouse with generic incremental compute, cutting architecture complexity, resource and development costs by one‑third while delivering second‑level queries over trillions of rows.
The article details MaxCompute's transformation into a cloud‑native, AI‑centric data warehouse, covering multi‑modal storage, model management, heterogeneous CPU/GPU scheduling, SQL AI functions, the MaxFrame Python framework, and several production case studies that demonstrate performance gains of up to 50% and elastic resource scaling to 160 000 cores.
This article enumerates ten uncommon, compaction‑related problems encountered in large‑scale Paimon deployments, explains their root causes—such as RPC timeouts, snapshot expiration, file corruption, and write conflicts—and provides concrete configuration tweaks and operational steps to resolve each issue.
The article analyzes how AI large models can transform data‑warehouse development through three practical scenarios—automated modeling, intelligent data cleaning, and ops optimization—while exposing five frequent implementation traps and offering concrete best‑practice recommendations to achieve cost reduction, efficiency gains, and quality improvement.
This article details Hisense's journey to create an AI‑ready multimodal data platform, covering the challenges of integrating diverse business systems, the shift from a Hadoop‑based architecture to a cloud‑native data lake, the JuData governance and development platform, and six practical scenarios that demonstrate unified ingestion, metadata management, rule‑based quality control, intelligent asset retrieval, and future AI‑driven DataOps capabilities.
This article examines how Alibaba Cloud’s MaxCompute platform has been transformed for AI workloads, detailing its multi‑layer architecture, multimodal data storage, SQL AI functions, the Python‑based MaxFrame framework, and real‑world deployments in large‑model preprocessing, autonomous driving, and multimodal image labeling.
The article shows that Polars, a query‑compiling DataFrame library, can accelerate ten‑million‑row GroupBy workloads by 6‑10× compared with Pandas, explains the underlying optimizer, Arrow columnar engine and Rust parallelism, provides a 20‑item syntax map, three real migration scenarios, streaming for out‑of‑memory data, and AI‑pipeline use cases, and offers a step‑by‑step migration guide.
This guide compiles the most frequent interview pitfalls for data warehouse roles, covering SQL join and aggregation errors, window function misuse, subquery versus CTE performance myths, dimensional modeling mistakes, SCD implementation traps, layered design issues, data quality handling, ETL traps, Hive and Spark performance questions, real‑time warehousing considerations, and effective interview strategies.
OPPO tackles explosive multimodal data growth by unifying metadata with Gravitino and boosting I/O performance using the open‑source Curvine cache, delivering a four‑layer data‑lake architecture that resolves data islands, metadata chaos, and bandwidth bottlenecks while achieving near‑commercial query speeds.
The article details Xiaohongshu's evolution from a simple ClickHouse‑based analytics layer to a Lambda‑enabled 2.0 stack and finally a Lakehouse‑based 3.0 architecture, showing how each iteration reduced infrastructure complexity, resource consumption and development effort by roughly one‑third while supporting trillions of daily events and AI‑driven use cases.
The article explains how a bitmap‑driven high‑table design (SelectDB) overcomes wide‑table storage bloat and latency to enable millisecond‑level crowd selection for tens of millions of users with hundreds of tag dimensions, while supporting dynamic tag expansion.
Facing a projected 85% of enterprises deploying internal agents within two years, Alibaba Cloud proposes an Agent-Ready big‑data AI infrastructure—comprising a unified data lake, real‑time processing, high‑dimensional vector retrieval, elastic model serving, and comprehensive security governance—that has already cut data‑development cycles from hours to 5‑10 minutes in internal model‑training and Taobao flash‑sale scenarios.
The article explains how Kuaishou partnered with Apache Hudi to overhaul its ODS‑based data lake, addressing latency, storage cost, and complexity for AI and BI workloads, detailing the evolution from mysql‑to‑hive to mysql‑to‑hudi 1.0 and 2.0, the resulting performance gains, cost savings, and future roadmap.
The Streaming Lakehouse Meetup (May 27) explores breaking data silos by unifying structured tables, images, video, audio, and high‑dimensional vectors through StarRocks‑Paimon‑Fluss integration, covering multimodal fusion retrieval, vector search internals, native reader/writer performance gains, and real‑world ANN indexing practices.
This article reviews two SIGMOD 2026 papers—MASC, which redefines multi‑query subgraph matching by maximizing shared computation to achieve up to two orders of magnitude speedup, and PrivHop, which combines 2‑hop labeling with secure multi‑party computation to enable privacy‑preserving shortest‑path queries on million‑node graphs with roughly a million‑fold reduction in runtime and communication.
