KaptureCX migrated its core analytics from ClickHouse to StarRocks, introduced RisingWave and Kafka for CDC, and achieved millisecond‑level query latency, a reporting cycle cut from weeks to one day, and a solid data foundation for AI‑driven services.
ScopeDB introduces a cloud‑native, real‑time analytics database that combines structured core columns with a flexible JSON column, adaptive indexing, a custom query language (ScopeQL), and true compute‑storage separation, delivering sub‑second query latency, high throughput, and up to 70% cost reduction compared with traditional big‑data stacks.
The whitepaper analyzes the data‑driven transformation of the new‑energy battery sector, outlines four core challenges—massive data streams, fast‑changing R&D demands, long manufacturing cycles, and multi‑dimensional quality standards—and demonstrates how SelectDB’s unified lake‑warehouse architecture delivers million‑level throughput, second‑level latency, up to 30× query speedup, and 60% cost reduction across real‑world case studies.
Traditional databases were built for low‑frequency, human‑driven queries, but AI agents generate dozens of concurrent, sub‑second queries that expose architectural limits, and StarRocks addresses these challenges with self‑healing optimization, real‑time data pipelines, extreme concurrency handling, and seamless lakehouse access.
EventHouse, Alibaba Cloud’s AI‑native data platform, unifies event ingestion, storage, governance and intelligent analysis through a layered architecture that supports real‑time SQL, zero‑ETL federation and Luma Agent‑driven conversational analytics, positioning it as a next‑generation AI data foundation for enterprises seeking to turn event streams into actionable insights.
This article dissects the full advertising funnel, explains why CTR and eCPM are critical, and demonstrates how StarRocks combined with Flink can deliver minute‑level real‑time monitoring, material selection, anomaly alerts, A/B testing, and a successful migration from Druid for massive ad‑tech workloads.
ByteHouse, ByteDance's cloud‑native data warehouse, evolves from a traditional warehouse to a next‑generation AI‑ready platform that handles 800+ PB of data, supports 25,000 nodes, and delivers real‑time, multimodal analytics through a decoupled storage‑compute architecture, AI‑driven query optimization, and native vector search integration.
This article details how Alibaba Cloud EMR Serverless Spark combined with the Paimon lakehouse framework enables Taobao Flash Sale’s retail data team to achieve low‑latency, high‑throughput real‑time analytics, batch processing, and feature generation, outlining architecture evolution, performance gains, and practical Spark tuning techniques.
This article details the evolution of a data warehouse at RenliJia from a MaxCompute‑centric setup to a modern lakehouse using StarRocks, Paimon, Flink, and Fluss, describing design goals, technical evaluations, implementation steps for offline, OLAP, and real‑time workloads, and the challenges and future plans that emerged.
This article explains how Taotian Group unified real‑time and offline data using Paimon as lake storage and StarRocks for high‑performance OLAP, eliminating costly sync pipelines, cutting refresh time by about 80%, saving nearly ten million yuan annually, and detailing the architecture, cluster safeguards, configuration tweaks, monitoring, and future roadmap for large‑scale promotional events.
Veteran data‑warehouse expert explains how the Paimon‑StarRocks stack solves the write‑read split, cuts storage costs, and delivers real‑time analytics, comparing it with Hudi, Iceberg, ClickHouse and Trino, and shows why leading Chinese tech firms are adopting this lakehouse architecture.
StarRocks and Apache Paimon have been progressively integrated across multiple releases, enabling a unified lakehouse architecture that supports multi-source federated analysis, time-travel queries, native readers/writers, distributed planning, and advanced profiling, while delivering performance gains that bring Paimon query speed on par with native StarRocks tables.
This article demonstrates how to integrate Apache Doris, a high‑performance MPP analytical database, into a Spring Boot application as a drop‑in replacement for MySQL, detailing environment setup, Maven dependencies, configuration, entity mapping, repository, service and controller code, and performance testing that shows Doris’s superior real‑time query speed.
Ctrip transformed its traditional T+1 offline warehouse into a near‑real‑time lakehouse by integrating Flink CDC with Apache Paimon, designing a two‑stage CDC ingestion, optimizing performance, implementing dynamic updates, and deploying the solution across multiple business scenarios, achieving minute‑level latency, reduced costs, and faster data‑driven decisions.
The 2025 Flink release introduces a disaggregated state management architecture for cloud‑native elasticity, AI‑driven Flink Agents with LLM, Memory and Tool support, Delta Join and VARIANT type for semi‑structured data, adaptive batch execution, incremental checkpoints, high‑speed network optimizations, and new SQL and Process Table Functions, reshaping real‑time analytics.
This article shares a three‑year journey of building a data‑virtualization‑based, multi‑environment feature management framework for real‑time risk decision platforms, detailing challenges like heterogeneous storage, cold‑start, and operational stability, and presenting a unified architecture that decouples physical storage from business logic.
Li Auto’s data team tackled the explosion of vehicle‑telemetry data—over a trillion rows and millions of signals per second—by redesigning their data foundation with Alibaba Cloud’s Hologres and Flink, achieving sub‑second latency, elastic scaling, high availability, and significant cost reductions across real‑time and offline workloads.
This article details how HuoLala built a high‑performance user profiling platform on Apache Doris, redesigning data models, leveraging bitmap storage, and applying query‑level optimizations to achieve up to 30‑fold speed gains, lower memory usage, and scalable real‑time analytics.
This article details OceanBase's evolution from a distributed OLTP system to a unified HTAP database, covering its cost‑based optimizer, vectorized execution, integrated row‑column storage, bypass import, materialized views, external tables, full‑text search, and real‑world use cases for real‑time analytics.
This article outlines the challenges of high‑quality data for AI, introduces Tencent Cloud’s AI‑Ready data platform with three core capabilities—DIaaS, Setats, and ES‑based knowledge search—covers the end‑to‑end WeData integration, intelligent agents for automation, and showcases ecosystem partnerships driving industry‑wide intelligent transformation.
Zepto transformed its brand‑analytics platform from a Postgres MVP into a production‑grade, sub‑second real‑time analytics solution by adopting StarRocks, redesigning its data pipeline with Databricks, Kafka, and Flink, and choosing a storage‑compute architecture that supports massive joins and rapid insights.
This article explains how the Tianji advertising platform consolidated heterogeneous MySQL, ClickHouse, and TiDB data sources into a single StarRocks data warehouse, addressing data silos, real‑time performance, and query complexity while improving accuracy, latency, and development efficiency.
Facing minute‑level decision demands and billions of marketing events during Taobao's Flash Sale, the Ele.me data team built a real‑time lakehouse with StarRocks and Paimon, leveraging asynchronous materialized views, RoaringBitmap de‑duplication, and resource isolation to achieve sub‑second query latency, lower storage costs, and stable high‑concurrency.
This article summarizes how enterprises adopt Apache Doris for lakehouse integration, near‑real‑time data warehouses, federated queries, and performance optimization, highlighting practical experiences, connector support, scheduling capabilities, and comparisons with Elasticsearch.
This article details how Kuaishou’s content middle platform tackled the massive challenges of real‑time, flexible, and cost‑effective data analysis at trillion‑scale by redesigning its architecture, adopting ClickHouse, splitting wide tables, and implementing a scatter‑gather execution model with pre‑shuffle and bitmap optimizations.
This guide presents a curated list of cutting‑edge lakehouse, real‑time analytics, and big‑data implementations from industry leaders, followed by a QR code that lets you download the full eBook for deeper insights.
This article presents a curated collection of cutting‑edge data lake and lakehouse case studies—including real‑time analytics, cloud‑native architectures, industry implementations from sales platforms to automotive IoT, and the latest advancements in open‑source projects—offering insights into modern big‑data strategies and governance.
This guide presents a series of expert case studies and insights on how Cloud Lakehouse solutions enable real‑time, fully managed multidimensional data analysis, improve user data experiences, balance performance and cost, and power large‑scale IoT and big‑data platforms across industries.
In the era of rapid AI growth, Fluss offers a unified lake‑stream architecture that tackles data quality, timeliness, scale, and multimodal challenges by tightly integrating Flink streaming with a high‑performance data lake, enabling seamless real‑time and batch analytics for AI workloads.
This article presents a curated list of case studies and insights on how Lakehouse technology powers real‑time analytics, simplifies data architecture, and drives digital transformation across industries such as e‑commerce, automotive, and social media.
The Fluss project, an open‑source next‑generation stream storage engine donated by Alibaba, has entered the Apache Software Foundation incubator, offering columnar streaming, real‑time updates, lake‑flow integration, impressive performance metrics, and a growing global developer community.
This guide presents a curated collection of case studies and insights on cloud-native Lakehouse architectures, real‑time analytics, data‑driven user experiences, and data governance, showcasing implementations from companies like SalesEasy, Changan Auto, TikTok, Tencent, JD.com, and more.
This article presents a curated list of sessions covering cloud Lakehouse technology for real-time, multidimensional data analysis, including case studies from SalesEasy, Changan Auto, Tencent, and JD, as well as discussions on data lake adoption, streaming lake Paimon, and the relevance of metadata‑driven data governance in the digital economy.
This article presents a curated list of case studies and insights on cloud Lakehouse technology, covering real-time intelligent analytics, data architecture simplification, IoT big‑data platforms, integrated data platforms, and the evolving role of metadata‑driven data governance in the digital economy.
Suixingfu rebuilt its payment data pipeline by replacing a fragmented Lambda stack with a unified Porter CDC + StarRocks + Elasticsearch architecture, achieving three‑fold query speed, ten‑fold analytics efficiency, 20% storage reduction, and sub‑second data‑capture latency across high‑concurrency, ad‑hoc, and batch workloads.
Learn how to rapidly build an intelligent data analysis agent by integrating multi‑source data through Alibaba Cloud Hologres, leveraging Bailei’s AI model service and the serverless Function AI platform, covering architecture, step‑by‑step deployment, verification, and resource cleanup for cost‑effective, real‑time business insights.
This article examines how Xiaohongshu replaced its costly Lambda architecture with a real‑time lakehouse built on Iceberg, Paimon, Spark, and StarRocks, achieving minute‑level latency, higher data quality, lower resource consumption, and dramatically faster query performance.
The article examines how emerging data‑infrastructure trends, multi‑modal databases like Neon, Supabase, and ClickHouse, and the convergence of OLTP, OLAP, and vector search are reshaping AI workloads, introducing the Data Warebase concept that unifies warehouse and database capabilities to meet modern AI workflow demands.
BiFang is a lake‑stream integrated storage engine that merges Apache Pulsar message‑queue capabilities with Iceberg data‑lake features, providing a single unified data store with full‑incremental queries, sub‑second visibility, exactly‑once semantics, and seamless integration with Flink, Spark, and StarRocks for both real‑time analytics and batch processing.
ByteBrain-LogParser is a cloud‑native log‑parsing framework that transforms unstructured logs into dynamic templates with real‑time precision control, delivering parsing speeds up to two orders of magnitude faster than state‑of‑the‑art methods while maintaining near‑SOTA accuracy and low storage overhead.
At the 2025 DA Data+AI Conference in Shanghai, Tencent Cloud unveiled Setats—a unified stream‑batch‑incremental engine that cuts system costs, delivers second‑level data visibility and real‑time changelog generation, and demonstrates measurable performance gains in automotive IoT analytics while integrating tightly with the WeData platform.
GreptimeDB solves front‑end observability challenges with a distributed architecture (frontend, datanode, flownode, metasrv), transparent two‑level caching, elastic scaling, and an SQL‑based flow engine for real‑time multi‑granularity aggregation and approximate counting, delivering millisecond query latency and cost‑effective storage.
The Apache Hudi Asia technical salon held in Beijing on March 29 gathered over 230 on‑site participants and 16,000 online viewers, featuring expert talks from leading Chinese tech companies that showcased real‑world Hudi implementations, performance optimizations, and future roadmap for data‑lake technologies.
This article analyzes why Elasticsearch struggles with large‑scale, complex real‑time analytics and demonstrates how Apache Doris’s MPP, columnar storage, and native SQL support provide a cost‑effective, high‑performance alternative, illustrated with detailed enterprise case studies.
This article explains the challenges LLMs face in data analysis, introduces the Model Context Protocol (MCP) as a standard bridge, and provides a step‑by‑step guide to integrate Hologres, MCP, and large language models—using Claude Desktop as an example—to create a fast, multi‑source data‑analysis agent.
This article summarizes a talk by Alibaba Cloud senior engineer and Flink Committer Luo Yuxia on the challenges of separating lake and stream storage, introduces the Fluss lake‑stream unified architecture, explains its technical benefits such as second‑level data freshness, unified metadata, efficient changelog generation, and outlines future plans for broader ecosystem integration.
In an era of exploding data volumes, distributed architecture offers unparalleled scalability, fault tolerance, and parallel performance through sharding, replication, batch and stream processing, with real‑world examples from e‑commerce and social media giants illustrating its practical impact.
Flink Forward Asia 2024 highlighted the limitations of Kafka for real‑time analytics—lack of updates, poor data exploration, costly back‑tracking, and high network overhead—while introducing Fluss, a columnar streaming storage that offers low‑latency reads, CDC, lake‑stream integration, and efficient Delta Join for scalable, fast analytics.
The 8th Tongcheng Travel Big Data Technology Salon in Suzhou featured four expert talks covering Tencent Cloud’s Meson Spark engine, near‑line computing for travel itineraries, a Flink‑based real‑time risk control system, and Apache Paimon’s latest lake‑warehouse innovations, followed by a data‑driven business perspective session.
This article presents Tencent's real‑time lakehouse architecture, detailing its three‑layer design of compute, management and storage, and explains the six components of the Intelligent Optimization Service—including Compaction, Index, Clustering, and AutoEngine—along with scenario‑based capabilities, migration strategies, and future optimization directions.
This article presents Bilibili's end‑to‑end exploration of a streaming‑batch unified data pipeline built on Apache Iceberg, detailing the original and iterated architectures for massive user behavior transmission, online AI training, DB synchronization, and dimension‑join, along with performance gains, cost savings, and future plans.
This guide introduces StarRocks, a high‑performance MPP database, explains its architecture and typical use cases, walks through a Docker‑based quickstart, shows how to create databases and tables, load NYC crash and weather datasets via Stream Load, and demonstrates analytical SQL queries that reveal traffic‑accident patterns under different weather conditions.
The paper proposes a business‑observability framework for a content‑production pipeline—illustrated by Bilibili’s workflow—by modeling archives as entities, assigning global AIDs for end‑to‑end tracing, and leveraging a Kafka‑Flink‑ClickHouse event‑streaming platform to monitor real‑time latency, bottlenecks, and safety audits across the entire production line.
The DataFun Summit page presents a collection of technical papers covering graph database parallel queries, next‑generation vector databases, real‑time data warehouse architectures, and best practices in finance and e‑commerce, while also providing instructions for obtaining the e‑book via a public account.
This article presents a comprehensive overview of a log clustering system, detailing its background, architecture based on Filebeat, Kafka, Flink, Elasticsearch, and Grafana, and introduces a continuous clustering algorithm using SimHash and Hamming distance for real‑time log governance and anomaly detection.
This article outlines practical approaches to data warehouse construction, covering dimensional modeling, layered architecture, capability development, real‑time and batch processing with technologies like Hive, Spark, Flink, Iceberg, and discusses governance, security, and future trends toward data value and real‑time metrics.
This article details Haijing Technology's challenges with real‑time telecom data analysis, explains why traditional Hadoop and ClickHouse solutions fell short, and shows how ByConity's unified engine, multi‑table joins, and elastic scaling enable efficient, low‑latency analytics across complex B‑O‑M domains.
This article details how a SaaS CRM provider built a cloud‑native Lakehouse platform to support multi‑tenant real‑time analytics, describing data challenges, metadata‑driven architecture, virtual database design, query optimization, BI integration, AI readiness, migration steps, and the resulting performance and scalability gains.
This article provides a comprehensive overview of real-time stream processing, covering data streams, window types, event and processing time, Flink's operator model, watermark mechanisms, and strategies for handling out-of-order and late data to ensure accurate, timely analytics.
IDC’s 2024 MarketScape report shows Alibaba Cloud topping the Chinese real‑time lakehouse market, highlighting the growing demand for integrated, low‑cost, minute‑level fresh data solutions that combine big‑data, AI, and multi‑engine analytics.
This article examines the growing challenges faced by traditional databases, explains the fundamentals and advantages of in‑memory databases, and details a practical evaluation of the Chinese HaiGuang CPU’s suitability for such workloads, highlighting performance, security, and reliability aspects across various application scenarios.
This article outlines the step‑by‑step evolution of Baidu's Feed data warehouse—from traditional layered modeling to hour‑level core tables, then real‑time wide tables, and finally a flow‑batch integrated multi‑version wide‑table architecture—highlighting the motivations, design choices, challenges, and resulting benefits.
This article provides a practical deep‑dive into StarRocks and Apache Paimon, covering data‑lake fundamentals, the technical advantages of both platforms, performance gains over traditional engines, step‑by‑step migration strategies, deployment options on Alibaba Cloud EMR, and future roadmap plans.
This article examines Rockset’s cloud‑native real‑time analytics database, detailing its document‑oriented data model, RocksDB‑Cloud storage engine, compute‑storage separation, sharding, converged indexing, query processing pipeline, and the implications of OpenAI’s recent acquisition for the broader database ecosystem.
Baidu’s log platform processes billions of daily page‑view events and, to monitor them accurately with minute‑level latency, implements a downstream streaming‑task architecture that maps limited custom dimensions, uses watermarks for completeness, trims raw data, aggregates into 5‑minute windows, and outputs concise metrics to Elasticsearch, achieving high accuracy, configurability, and low cost.
This article explains Baidu's log platform architecture, the UBC event‑tracking protocol, monitoring requirements, and the low‑cost, high‑accuracy solutions—including dimension mapping, watermark handling, data trimming, and time‑window aggregation—that enable real‑time, customizable monitoring of petabyte‑scale log streams.
This article summarizes Wang Feng's presentation on the next‑generation Lakehouse architecture, explaining how Apache Paimon provides a unified, real‑time data lake format that bridges batch and streaming workloads, enabling low‑latency analytics and AI integration for modern big‑data applications.
StarRocks combines extreme query speed and a unified architecture to deliver a lakehouse solution that separates storage and compute, supports multi‑warehouse resource isolation, offers Trino compatibility, materialized‑view acceleration, and cost‑effective scaling, making it suitable for real‑time analytics, data‑lake queries, and traditional OLAP workloads.
The article explains how China Unicom transformed its 5G fully‑connected factory data pipeline from a complex Lambda architecture into a streamlined, real‑time and offline‑integrated solution built on Apache Doris, detailing system requirements, architectural redesign, performance gains, and future plans.
This article explains how CloudLakehouse’s Multi‑Cluster elastic architecture enables high‑concurrency, low‑latency real‑time analytics on data lakes by addressing storage‑compute separation, dynamic caching, and automated scaling, providing a cost‑effective solution for customer‑facing data products.
This article analyzes the challenges of building real‑time and batch risk‑control features, compares Lambda and Kappa architectures, evaluates storage‑unified and compute‑unified solutions, and details how StarRocks was selected, validated, and deployed to achieve high‑performance, low‑latency feature serving in a financial context.
This article explains the evolution of message middleware, introduces core concepts of stream processing, and details RocketMQ 5.0's native lightweight stream engine RStreams and its stream database RSQLDB, showing how they simplify real‑time data integration, computation, and scaling in cloud‑native environments.
JD’s Mini‑Program Data Center integrates data collection, storage, and real‑time analysis using Flink, ClickHouse, and Elasticsearch to provide comprehensive monitoring, user behavior insights, and scalable analytics for mini‑programs across JD’s ecosystem, enabling precise operations and future AI‑driven enhancements.
This article introduces Dongchedi's near real‑time metric system architecture, covering business background, technical challenges, the unified storage‑compute and query service design using the Las lakehouse built on Apache Hudi, solutions to consistency issues, achieved results, and future plans for further real‑time improvements.
This article details Didi's evolution from a multi‑engine OLAP stack to a unified StarRocks solution, explains the design of global dictionaries and materialized views for real‑time dashboard acceleration, and shares performance results, challenges, and future optimization directions.
This article reviews the evolution of data analysis and business intelligence, highlights current trends such as precision, agility, and real‑time needs, discusses common challenges, and presents the design and implementation of a unified semantic layer and indicator platform to enable agile, accurate, and real‑time analytics.
This article explains how ByteDance (Douyin) transforms its data infrastructure from isolated workshops to a unified middle platform and finally to a data flywheel, detailing the three development stages, the Data BP organizational model, real‑time analytics, A/B testing, and the resulting business benefits for large‑scale event projects.
This guide walks you through a 5‑minute challenge that shows how to enable Hologres real‑time lakehouse capabilities, configure OSS, DLF, and Hologres services, create external and internal tables, run TPCH Q11 queries, and submit results for prizes.
This article details ByteHouse's evolution from a ClickHouse‑based OLAP engine to a cloud‑native, massively parallel data warehouse, highlighting its distributed and cloud‑native architectures, enhanced table engines, HaKafka and Materialized MySQL extensions, and real‑world use cases in short‑video, marketing and gaming analytics.
The Flink Forward Asia 2023 conference showcased major updates to Apache Flink (versions 1.17 and 1.18), introduced the Apache Paimon lakehouse project, announced Flink CDC 3.0, and highlighted community growth, cloud‑native deployments, and real‑time data‑warehouse use cases across industry leaders.
This article introduces Apache Doris, a real‑time analytical database built on an MPP architecture, explains its suitability for massive data workloads and online high‑concurrency reporting scenarios, and details the core technologies—storage models, vectorized query engine, materialized views, partitioning, indexing, row‑store and prepared statements—that enable sub‑second query latency and high QPS, while also showing a real‑world case study and how to join the Doris community.
The article describes how NetEase News' recommendation team replaced a slow, batch‑oriented data‑warehouse pipeline with a Flink‑based, Apache Paimon real‑time wide‑table solution that supports partial updates, reduces latency from hours to minutes, and lowers processing costs while handling both deduplication and non‑deduplication recommendation scenarios.
This article details Qunar Travel's multi‑year development of a Customer Data Platform (CDP), covering its business motivations, architectural design, tag‑based data processing, real‑time and offline pipelines, user segmentation, marketing automation, performance optimizations, and future directions for model‑driven personalization.
The Apache Doris Summit Asia 2023 showcased the milestone 2.0 release, impressive performance gains, rapid community growth, and diverse industry deployments, while outlining future cloud‑native and unified analytics directions that position Doris as a leading real‑time data warehouse solution.
This article provides a comprehensive technical overview of LakeSoul, an open‑source, cloud‑native lakehouse framework, covering its design philosophy, core features, architecture, performance benchmarks, real‑time ingestion, incremental computation, multi‑stream joining, security, community progress, and future roadmap.
This article traces the step‑by‑step evolution of an AB testing platform—from its initial 1.0 version with basic traffic splitting, through the 2.0 era that introduced multi‑layer orthogonal traffic models and real‑time metric pipelines, to the 3.0 era focused on usability, stability, and advanced analysis—while sharing concrete design decisions, implementation details, and lessons learned.
The article details the JD Mini Program platform, its data‑center development milestones, comprehensive business panorama, technical architecture, data collection, storage, and analysis pipelines—including Flink‑based real‑time monitoring, ClickHouse custom analytics, and Elasticsearch user‑behavior insights—while outlining current challenges and future AI‑driven enhancements.
This article presents a comprehensive overview of a batch‑stream unified storage solution built on Hudi and the Lakehouse Analysis Service (LAS), covering background challenges, architectural design, data organization, read/write mechanisms, BTS architecture, real‑world deployment scenarios, and future development plans.
This article details how Xingyun Retail Credit transitioned from traditional data warehouses to an Apache Doris‑based OLAP solution, covering data demand generation, OLAP engine selection challenges, multi‑stage implementation, performance optimizations, data‑warehouse construction, real‑world use cases, and future roadmap.
This article analyzes the rise of lake‑house architecture in the Hadoop ecosystem, compares the technical capabilities of Hudi, Iceberg and Delta Lake, details implementation enhancements such as MOR and multi‑writer support, showcases Flink integration, presents a real‑time marketing use case, and outlines future development directions.
The article examines Doris‑based real‑time data warehousing, outlining why teams choose this approach, comparing its low‑threshold development and operational simplicity to Flink’s high‑cost streaming, and highlighting latency, scale limits, and the strict monitoring required for production use.
This article examines the massive‑scale data requirements of an activity‑tracking platform, compares MySQL, Elasticsearch and HBase, and explains why ClickHouse—with its columnar storage, MergeTree engine, vectorized execution, and distributed architecture—offers the best combination of storage capacity, write performance, real‑time analysis, and query speed for billions of records.
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.
Apache Doris 2.0-beta, released on July 3, 2023, introduces a new Cascades‑based query optimizer, adaptive pipeline execution engine, workload‑aware resource isolation, enhanced memory management, partial column updates, multi‑catalog support, and numerous performance gains across real‑time analytics, ETL, and high‑concurrency point queries.
By replacing the costly Lambda architecture with a unified data‑lake built on Iceberg and Flink CDC, the advertising team achieved minute‑level latency, strong consistency, and lower storage expenses, cutting end‑to‑end processing times from hours to a few minutes across budgeting, warehousing, OLAP and ETL workloads.
This article details Alibaba Cloud MaxCompute’s lakehouse evolution, describing its unified storage‑metadata‑compute design, the Transactional Table 2.0 format, near‑real‑time incremental ingestion, clustering and compaction services, transaction handling, TimeTravel and incremental queries, and future roadmap for big‑data workloads.
This article examines the challenges and value of log analysis, introduces the concepts of schema‑on‑read versus schema‑on‑write, and details how the Honghu platform implements real‑time, one‑stop heterogeneous data analytics with flexible storage, indexing, and SQL‑based query engines.
This article details Tongcheng Travel’s production deployment of the StarRocks OLAP database, covering background, business scenarios, technical evaluation against ClickHouse and Greenplum, implementation with Flink SQL, real‑time analytics, offline reporting, CDP use cases, performance optimizations, and future cloud‑native plans.
This article details Waterdrop's transition from ClickHouse and TiDB to a unified StarRocks‑based OLAP platform on Alibaba Cloud EMR, covering architecture evolution, performance benchmarks, best‑practice recommendations, and future plans for large‑scale real‑time analytics.
This article details the migration from a traditional Hive‑based data warehouse to a lakehouse architecture using Apache Hudi, covering the original Lambda setup, its pain points, lake‑vs‑warehouse differences, Hudi features, integration challenges, practical solutions, and future roadmap.
