Tencent's SuperSQL achieved a 6.54 × 10⁸ TPC‑DS score—nearly ten times the previous best—while cutting unit performance cost to 11.04 yuan, about one‑sixth of earlier results, making it the global leader in both speed and price‑performance for 100 TB data analytics.
The article details how a live‑streaming data team built an AI‑assisted, metric‑driven system that lets users describe a real‑time data task in under 200 characters, automatically generates DSL and Flink SQL, and supports safe incremental adjustments via a Hook protocol, cutting development time from days to minutes.
China's high-quality dataset count hit 120,000 totaling 1,565 PB with 60% quarterly growth, exposing four systemic gaps in traditional BI-oriented lakehouse architectures — storage, governance, compute performance, and security — and driving an AI-native reference architecture built on Paimon, StarRocks, tiered storage, operator-level lineage, and multi-modal retrieval.
The article explains how Google’s new BigQuery Graph with Measures extends the traditional semantic layer—once focused on unified metric definitions—into a business‑context‑aware infrastructure that lets agents trace why metrics change through explicit entity relationships.
The article outlines the strategic need for a unified data lake in a digital‑focused enterprise, details functional and non‑functional requirements such as linear scalability, real‑time and batch processing, multi‑tenant support, security and governance, and presents a comprehensive architecture design that integrates storage, compute, and management components.
This article deeply compares two StarRocks real-time wide table architectures—direct primary key table writes versus Paimon lakehouse external tables—across seven dimensions including query performance, write latency, data reuse, resource isolation, operational complexity, storage cost, and evolution potential, providing banking scenario selection criteria and a recommended Flink+Fluss+Paimon+StarRocks streaming lakehouse architecture.
This article presents an eight-step methodology for ontology-driven data governance that connects business, data, application, and technology architectures (4A) by starting from high-value business scenarios, establishing semantic kernels, mapping data evidence, linking application actions, referencing technical constraints, enforcing semantic quality, publishing usable semantic products, and closing the loop with operational feedback.
The article explains how lance‑spark enables Lance's Blob V2 storage by marking a column with blob encoding and setting file_format_version ≥ 2.2, describes the metadata‑driven descriptor schema, the write path that still accepts Spark BINARY, and the size‑based placement of binary data into inline, packed, dedicated or external blob files.
The article analyzes how StarRocks, Fluss, and Paimon can replace the traditional Lambda architecture with a unified lakehouse solution that delivers second‑level data freshness, consolidates storage and compute, and provides native read/write paths for high‑performance real‑time analytics and large‑scale ETL.
Apache Fluss graduates to a top-level project, introducing the Lakestream architecture that unifies real-time streaming and historical lakehouse storage through tiered hot/cold storage, unified metadata, and Union Read, eliminating Lambda architecture complexity and enabling sub-second analytics on a single logical table.
The article examines the traditional ODS‑DWD‑DWS‑ADS architecture, explains why it falls short for AI‑driven analytics, and proposes adding semantic, knowledge, context, and AI serving layers while positioning FineDataLink as the foundational data integration engine.
As enterprises add more systems, data volumes explode while accessing it becomes harder; this article defines data architecture, explains how databases, data warehouses, data lakes, and data middle platforms each solve distinct layers, and outlines the key questions and challenges for building a unified, reusable data ecosystem.
This article details migrating from a legacy Canal+Kafka+Flink+Hive stack to a modern Flink CDC + Iceberg real-time data lake, covering Spring Boot orchestration, chunk-based full/incremental sync, Interval Join for wide tables, automated schema evolution, hidden partitioning, Exactly-Once checkpoint tuning, and Kubernetes deployment practices.
The article compares Elasticsearch and ClickHouse for log storage, analyzes cost and performance, and provides a step‑by‑step guide to deploying a Zookeeper‑Kafka‑Filebeat‑ClickHouse pipeline, including common pitfalls and their solutions, showing how the combo reduces server costs by up to half.
The article explains how scattered, hard‑coded metadata in large‑scale systems leads to unknown impact of changes, and walks through building a unified metadata center with a single source of truth, automatic collection, lineage, impact analysis, asset discovery, active metadata, and governance, while highlighting practical trade‑offs and real‑world tools.
This article shares production lessons from integrating Spring Boot with Apache Doris for real-time data warehousing, covering connection pool tuning, data model selection, ingestion methods, materialized views, Colocate Join optimization, resource isolation, compaction tuning, BI tool integration, and cluster operations.
A head‑to‑head benchmark on a 2.3 GB CSV (~1.2 million rows) shows Pandas exhausting memory, Polars completing the pipeline in 8.7 seconds with modest RAM, and DuckDB answering the same query in just 12 milliseconds, highlighting distinct trade‑offs for Python data processing.
In July 2024 Apache Fluss received unanimous approval from the Apache Incubator IPMC and ASF board, graduating to a Top‑Level Project; the article details its community growth, core Lakestream architecture, real‑time streaming storage capabilities, adoption by major enterprises, and how it enables AI agents with fresh, unified data for real‑time decisions.
The article details Ant Group’s Lampara, a next‑generation real‑time data processing platform that embeds end‑to‑end SLA guarantees, active disaster‑recovery, scenario‑driven development and enhanced operators, showing how it improves reliability, efficiency and cost while supporting large‑scale business scenarios such as flash sales, AI agents and marketing campaigns.
At AICon 2026, Ma Jin explained how Lance’s layered lakehouse format—spanning file, table, index and catalog layers—addresses AI workloads by supporting wide columns, cheap schema evolution, mixed storage and low‑latency random reads, enabling unified handling of training data, RAG retrieval and Agent memory.
