The article examines why a single data engineer can run an end‑to‑end data team, outlines the essential abilities—semantic ownership, building an agentic data stack, and leveraging historical context—while discussing ChatBI’s limits, validation loops, and the open‑source Datus 0.3 harness for practical implementation.
With CI/CD pipelines maturing, automated test coverage is no longer the bottleneck; the real constraint has shifted to producing accurate, fast, and secure test data, prompting teams to upgrade from simple data mocking to full‑stack data engineering, AI‑driven synthesis, and verifiable data contracts.
The article analyzes why data development engineers are becoming more valuable in the AI era, outlining four core reasons—including data‑driven AI limits, the rise of RAG architectures, heightened data compliance, and a talent shortage—while offering concrete advice on mastering real‑time pipelines, unstructured data, and AI infrastructure.
This article analyzes how the Daft‑Ray‑Lance stack tackles the challenges of multimodal AI workloads by offering a high‑performance Rust engine, adaptive back‑pressure, seamless Ray‑based distributed scheduling, and a storage format optimized for random access, vector indexing, and zero‑copy schema evolution, complete with benchmark comparisons and practical deployment guidance.
In the 2026 spring hiring season, many big‑data job seekers see their resumes disappear because they still focus on offline batch processing, while employers now demand real‑time streaming, AI‑driven data pipelines, and cloud‑native deployment skills such as Flink, vector databases, and Kubernetes.
The article predicts that by 2030 the traditional data‑engineer role and modern data‑stack components will collapse into a few unified, HTAP‑capable databases, semantic layers, and AI agents, reshaping pipelines, warehouses, and even edge computing while urging engineers to pivot toward semantic modeling and AI orchestration.
This article explains how DataWorks integrates data quality specifications directly into the SQL development workflow using Data Contracts, addressing governance lag, versioning gaps, and trust issues while providing a unified, version‑controlled, and automated quality assurance process for offline data pipelines.
This roundup compiles the latest releases across major data platforms—including ClickHouse, Apache Fluss, Delta Lake, StarRocks, Apache Pulsar and DolphinScheduler—highlighting version numbers, key feature additions, security fixes, and emerging trends shaping the big‑data ecosystem.
FireRed-Image-Edit, an open‑source instruction‑driven diffusion model, combines massive high‑quality data, a dual‑stream multimodal architecture, progressive training, and a comprehensive multi‑dimensional benchmark to achieve unprecedented pixel‑level control and human‑like editing performance across diverse visual tasks.
In a deep‑dive roundtable, three data‑engineering veterans discuss the rise of AI agents, the importance of data context, memory mechanisms, workflow versus agent trade‑offs, and the future of database intelligence, offering practical strategies and architectural philosophies for building smarter data pipelines.
The article explains why mastering large‑model training is crucial for professionals, researchers, and enterprises, outlines the end‑to‑end pipeline—from data preparation and pre‑training to instruction fine‑tuning and RLHF alignment—compares training with RAG, and presents a structured learning roadmap.
This guide presents a comprehensive big‑data development roadmap, detailing industry opportunities, a six‑module technology stack, four progressive learning stages, hands‑on project ideas, interview question strategies, common pitfalls, and curated resources, helping aspiring engineers become proficient and interview‑ready while avoiding common mistakes.
This guide outlines a concise, 1.5‑2‑minute self‑introduction for data engineering interviews, highlighting essential personal details, technical stack, project achievements, business impact, and common pitfalls to avoid, with a concrete example and actionable tips.
This article details the design and implementation of Matra, an AI‑driven data assistant for a large e‑commerce platform, covering the challenges of legacy data assets, knowledge‑base construction, GraphRAG integration, multi‑stage agent frameworks, practical results, and future plans for continuous improvement.
This article explains how StarRocks mitigates the hidden risks of massive one‑time data imports in a storage‑compute separated architecture by redesigning the write path to spill to local disk, merge centrally, and write to object storage, resulting in fewer small files, higher write throughput, and more stable query performance.
The article explains that the real difficulty of RLHF lies in designing and curating high‑quality preference data, building robust reward models through bad‑case rewriting, human‑in‑the‑loop labeling, and inference‑based reward modeling, while algorithmic details like PPO are secondary concerns.
This article details BMW's digital transformation journey using Sensors Data, covering the background of rapid app growth, the cross‑regional data collection challenges, the systematic solution architecture—including mapping, preprocessing, and historical data migration—and the resulting business impact and future AI‑driven roadmap.
The article explains why CSV becomes a bottleneck for large‑scale data, demonstrates how Parquet’s columnar, typed, and compressed format dramatically reduces storage, speeds up reads, and improves data safety, and provides step‑by‑step Python code for migrating and benchmarking the switch.
This guide outlines a step‑by‑step framework for creating AI products, covering problem discovery, user‑centric motivation analysis, compliance and ethics, defining a Minimum Viable Intelligent Product, assembling multidisciplinary teams, leveraging data and model selection, designing trustworthy UX, go‑to‑market tactics, moat building, and continuous monitoring for improvement.
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.
This document outlines the background, design, architecture, workflow, and large‑model integration of an AI‑driven Experiment Analysis Agent, detailing how it consolidates data, automates analysis via modular pipelines, leverages DeepSeek models, and enhances user experience through unified front‑end forms and intelligent messaging.
Alibaba’s Taobao Starry Image Editing model tackles the e‑commerce challenge of maintaining visual consistency by introducing a high‑fidelity, plug‑in architecture, a million‑scale consistency dataset, and multi‑stage multilingual training, enabling precise, controllable edits without altering product layout or background.
The article outlines the five essential engineering domains—application, model, compute, knowledge, and data—in the era of large models, and details concrete scale, efficiency, service, value, quality, and security metrics that enterprises should track to drive intelligent outcomes.
Over a year after completing a five‑month, cross‑cloud migration of Huolala’s 40 PB offline big‑data platform—spanning storage, compute, services, and infrastructure—the team details the architecture, verification methods, high‑throughput migration tools, network isolation strategies, and lessons learned to guide similar large‑scale data migrations.
In an interview, Di Xingxing of Autohome details their AI+Data framework—unified lake‑warehouse, intelligent engine, and agent services—that breaks data silos, blends traditional models with LLMs, leverages causal inference and RAG knowledge bases, and uses continuous feedback to build explainable, evolving data agents for accurate sales forecasting, competitive analysis, and end‑to‑end business automation in the automotive industry.
An individual participant recounts their journey in the 2025 China University Computer Competition Big Data Challenge, detailing data cleaning, feature engineering, model building on 300‑stock historical prices, and insights gained from solo competition experience, highlighting challenges, lessons, and future directions in financial AI.
This article explores how large language models break the natural‑language‑to‑SQL barrier, outlines the challenges of NLP‑driven data retrieval, compares Text2SQL and Text2DSL approaches, and proposes a unified data service and metric platform to power enterprise‑grade ChatBI solutions.
This article examines JD.com's migration of its massive traffic data processing from a dual Lambda architecture to an integrated lakehouse solution, detailing the challenges, innovative optimizations with Flink and Hudi, performance gains, cost reductions, and future directions for real‑time data handling.
This interview with Yunqi Technology CTO Guan Tao explains how the traditional Lambda architecture’s triple‑system complexity drives costs and operational pain, and why the company’s General Incremental Computing (GIC) approach offers a unified, cost‑effective Kappa‑style solution for real‑time, batch, and interactive analytics.
Alibaba International’s senior data science expert explains a systematic five‑strategy solution—data acquisition, augmentation, quality optimization, engineering pipeline, and evaluation loop—to overcome data scarcity, high annotation cost, and processing challenges for low‑resource languages in multilingual large language models.
This article examines how Hainan Shuzhao Technology leverages ChatGPT‑4 and other large language models to enhance DataOps, address traditional data management challenges, improve Text2SQL accuracy, and outline future directions for agile, AI‑driven data pipelines.
Despite the hype around Flink and AI models, Spark 4.0’s release brings a lightweight Python client, Spark Connect GA, enhanced SQL optimization, vectorized execution, and AI integration, reaffirming its leading position in the big‑data ecosystem while hinting at future challenges and innovations.
This semi‑annual report analyzes over 120 interview experiences to reveal current hiring trends, required skills, and strategic advice for data professionals targeting mid‑to‑senior positions in large‑scale companies during the first half of 2025.
The article explores how JD’s engineers across retail, logistics, and AI divisions use model distillation, data selection, intelligent routing, and advanced voice recognition to improve the 618 shopping festival experience, highlighting real‑world technical challenges, solutions, and the company’s talent development programs.
This article provides a comprehensive comparison of Apache Airflow and Argo Workflows, covering their core features, architectures, use cases, code examples, and recommendations for selecting the appropriate distributed workflow engine in data engineering, big‑data, and AI pipelines.
This article explains why Kafka is widely adopted by top companies, outlines its high throughput, scalability, and durability, and describes key real‑time data pipeline, stream processing, and big‑data integration scenarios, concluding that mastering Kafka is essential for modern backend and data engineering roles.
This comprehensive guide compares Apache Airflow and Argo Workflows—two leading cloud‑native distributed task schedulers—by examining their core features, architectures, DAG handling, performance, language support, big‑data and AI integrations, and provides practical selection advice for data engineers and DevOps teams.
The article dissects why many B2B GenAI projects fail to scale despite heavy investment, highlighting overlooked challenges in data preparation, model specialization, product integration, user experience, and organizational culture, and proposes concrete ways to bridge these gaps.
This guide outlines a comprehensive data development roadmap, covering infrastructure setup, governance frameworks, automated pipelines, BI and analytics tools, AI/ML integration, cultural adoption, and continuous performance monitoring to enable intelligent business transformation.
The author recounts attending PyCon LT 2025 Data Day, summarizing talks on building a simple static code analyzer with AST, challenges of data warehouses versus data lakes, cloud cost‑scraping pipelines, A/B testing libraries, privacy‑enhancing data processing, and tools like Panel and Dagster, while noting the inspiring presence of female speakers.
This guide breaks down the main positions in a data team— from data development and analysis engineers to product managers and operations specialists—detailing each role’s key responsibilities, essential skill sets, and the typical technology stack they rely on.
Yingjiao Network transformed its game data platform by adopting Alibaba Cloud EMR Serverless Spark, addressing previous architecture pain points, enhancing data collection, offline scheduling, and online analytics, which led to higher development speed, 50% faster compute, and improved stability for global game operations.
The first Apache Hudi Asia Summit in Beijing attracted over 230 attendees, featuring technical discussions on data lake optimization and case studies from companies like Fastly and Meituan.
The article provides a detailed technical overview of the Turing Data Finder (TDF) platform, describing its background, core components, data schema, ingestion workflow, and a suite of growth‑analysis features such as event, retention, funnel, path, component, distribution, and attribution analysis, while also outlining performance‑optimisation techniques and future development directions.
The article analyzes how the rapid rise of open‑source large‑model AI in 2025 is reshaping the data development profession, urging developers to transition from specialized data‑engineer roles to full‑stack AI data engineering skills such as distributed computing, lake‑house architectures, and model tuning.
Many companies deploy fragmented monitoring and observability tools yet still struggle to pinpoint incidents; this article analyzes the root causes—under‑utilized tools and scenario‑agnostic data—and offers practical steps to organize metrics, build layered insights, and improve fault‑resolution efficiency.
This article shows how AI tools like DeepSeek can dramatically speed up data‑engineering tasks—such as fixing long‑running SQL queries, building real‑time data pipelines with Flink, and deciphering legacy stored procedures—while offering concrete prompts, real‑world case studies, and five time‑saving techniques.
This presentation details engineering‑focused approaches to building, mixing, and filtering data for large language models, covering data preparation, pre‑training mix strategies such as DoReMi, DoGE and online sampling, post‑training data quality selection methods, and practical Q&A on scaling laws and PDF processing.
The article compares Java, Scala, SQL, and Python for data‑engineering tasks, arguing that Python’s versatility, rich ecosystem, and ease of use make it the preferred language for both small‑scale and massive Spark workloads despite its performance trade‑offs.
This article shares a data‑engineering student’s personal experience—from a misaligned operations role to mastering big‑data technologies, building a portfolio, crafting a targeted resume, and navigating multi‑stage interviews—offering concrete advice and a structured learning roadmap for aspiring data professionals.
PAI‑DSW leverages the Fluid project to provide a cloud‑native AI development platform where data scientists can dynamically mount and unmount OSS datasets on running Kubernetes pods without restarting, improving workflow efficiency and addressing the challenges of heterogeneous data source management in AI engineering.
The article shares practical methods and code examples for making SQL both readable and high‑performing in large‑scale data platforms, covering predicate push‑down with subqueries, deduplication strategies, bucket utilization, and Python‑driven job parameter handling.
The article details Bilibili's comprehensive redesign of its tag system—including background challenges, architectural layers, technical upgrades like Iceberg integration and shard‑based ClickHouse writes, crowd selection methods, online service guarantees, performance metrics, and future plans—showcasing a data‑driven solution that boosts stability, speed, and business coverage.
This article introduces 24 highly useful Python libraries that cover the entire data‑science lifecycle—from data collection and cleaning to visualization, modeling, interpretation, and deployment—helping readers build a comprehensive and visually appealing data‑analysis pipeline.
In this interview, Xiaohongshu data engineer Jianchen recounts his evolution from a computer‑science student discovering open‑source through MIT6.824 to contributing to SOFAJRaft and Apache RocketMQ, detailing his OSPP projects, the decision to join Xiaohongshu, and his work on a cloud‑native Kafka engine that cut storage and compute usage by half.
This article details Ping An Financial Services' journey in building a comprehensive big‑data platform, addressing fragmentation, low data timeliness, processing limits, and governance challenges through a four‑stage technical evolution, modular tool development, and a systematic data‑governance framework to support its digital transformation.
This article explains how Douyin Group tackles massive data volume, quality, and efficiency issues by building a four‑layer intelligent platform, standardizing metric management, automating metric decomposition, and creating reusable metric services that boost agility, stability, and cross‑team collaboration.
DataFunSummit2024, co-hosted by Bilibili, convenes industry experts, scholars, and enterprise leaders across six forums to discuss next‑generation data architecture, showcasing Bilibili’s Iceberg‑based stream‑batch innovations, AI‑BI analytics, NoETL practices, and emerging alternatives to Lambda architecture.
Kuaishou’s data‑lake initiative tackled exploding offline warehouse costs, redundant model proliferation, and data‑consistency complexities by adopting Apache Hudi’s schema‑evolution capabilities and real‑time lake ingestion, improving cross‑team collaboration and narrowing the real‑time‑offline data gap.
This article provides a comprehensive, experience‑driven guide to supervised fine‑tuning (SFT) of large language models, covering special tokens, latency considerations, data diversity and production, training frameworks and hyper‑parameters, over‑/under‑fitting diagnostics, and evaluation metrics such as helpfulness, honesty, and harmlessness.
DB‑GPT is an open‑source AI‑native data‑application framework that provides multi‑model management, Text‑to‑SQL optimization, RAG, multi‑agent collaboration, and intelligent workflow orchestration, enabling developers to build scalable large‑model database applications, with proven enterprise adoption, community growth, and academic publications.
The article narrates the author’s 30‑year programming career and shares technical reflections on building business‑agnostic, configurable data platforms, covering batch, streaming, interactive computing, big‑data sharding, Spark, Flink, cloud migration, and the philosophy of software architecture.
The talk reviews the rapid evolution of data technologies—from early database foundations and big‑data breakthroughs to the rise of generative AI—highlighting how Ant Group’s data platform is shifting from a cost‑efficiency focus to a value‑centric, multimodal, AI‑driven ecosystem.
The article reviews the rapid advances in generative AI and big‑data technologies, traces the historical development of data infrastructure, and argues that modern data systems are shifting from a cost‑efficiency focus to a value‑centric paradigm driven by multimodal, non‑structured data, vector search and machine‑oriented services.
This article reviews the 70‑year evolution of databases, explains how vector databases and Retrieval‑Augmented Generation (RAG) are reshaping AI applications, and details Baidu Intelligent Cloud's VectorDB architecture, performance advantages, real‑world use cases, and future trends in data engineering.
This tutorial walks you through the fundamentals of Apache Iceberg, its architecture and key features, explains why it’s advantageous for lakehouse workloads, and provides a step‑by‑step Docker‑Compose setup to integrate Iceberg with StarRocks for fast, ACID‑compliant analytics on real‑world taxi data.
This article explains the Lambda architecture—a three‑layer model combining batch and real‑time processing for large‑scale data, outlines its components, advantages, disadvantages, common tools, and compares it with the Kappa alternative while providing practical insights for data engineers.
This guide provides a comprehensive overview of Apache Paimon’s architecture, key features, and advantages, explains how to integrate it with StarRocks for real‑time lakehouse analytics, and walks through a complete quick‑start setup including component installation, Flink and Kafka deployment, data ingestion, table creation, and query execution with time‑travel support.
This article analyzes why data engineers face endless data‑extraction requests, identifies common missteps in data‑construction practices, and proposes a comprehensive solution based on dimensional modeling, OLAP multidimensional reporting, self‑service tools, and knowledge empowerment to dramatically improve efficiency and scalability.
This article explains how Alibaba Cloud's SLS data processing resolves the tension between simple log collection and the need for structured, analyzable data by introducing a unified SPL syntax, delivering over tenfold performance gains, reducing costs to one‑third, and providing powerful debugging tools for cloud‑native log analytics.
This article presents a comprehensive overview of constructing and deploying a user and item profiling tag system at Qunar, covering tag taxonomy, integration challenges, technical architectures, algorithmic methods such as classification, recommendation, knowledge‑graph and causal inference, as well as real‑time streaming, ID‑mapping, and practical applications in marketing, attribution and A/B testing.
This article explores agile data engineering, advocating code‑as‑infrastructure practices such as code‑everything, data and code reuse, and ETL‑level continuous integration, while discussing the trade‑offs between data‑centric and code‑centric reuse, cost considerations, and practical implementation tips for modern data projects.
This article presents a comprehensive guide to data engineering for research‑focused large language models, covering domain‑adaptive pre‑training, supervised fine‑tuning, retrieval‑augmented generation, dataset construction, data cleaning pipelines, token‑izer adaptation, and prompt engineering best practices to boost model performance in specialized tasks.
This article reviews the rising importance of data quality for large models, explores data‑centric AI, large‑model pre‑training data engineering, and presents recent Fudan University research on using large models to improve data governance across multiple domains such as attribute normalization, geographic cleaning, compliance checking, and multimodal retrieval.
This article examines why data departments get overwhelmed by massive data‑retrieval requests, identifies root causes such as mindset, requirement handling, and lack of tools, and presents a technical solution centered on dimensional modeling and OLAP multi‑dimensional reporting to streamline data access and empower teams.
Tencent Games tackled the low accuracy of AI‑generated SQL in production by combining large language models with a StarRocks lake‑warehouse, introducing a semantic layer, async materialized views, and an agent‑based multi‑intelligence framework, ultimately raising one‑shot SQL correctness to 89% and cutting delivery time from 2 hours to 0.33 hours.
This article details Tencent Video’s data business, describing the design and implementation of its metrics middle platform and lake‑warehouse integration, covering architecture, governance, consistency, timeliness, usability, cost optimization, and future plans, with insights into technology choices such as Iceberg, StarRocks, and MQL.
This article explains how a third‑generation metric platform replaces traditional ETL‑heavy data‑warehouse pipelines with a semantic‑driven NoETL approach, reducing cost, improving quality and efficiency, and delivering automated, self‑service analytics for both IT and business users.
This article explains the value of user profiling platforms, outlines their core functions, presents a layered architecture with open‑source options, and details engineering optimizations—from wide‑table design to BitMap caching and task‑mode execution—while also discussing current industry trends.
This article presents a comprehensive case study of Cao Cao Mobility's transition from a traditional Lambda architecture to an enterprise‑grade real‑time data warehouse built on Hologres and Flink, detailing business background, pain points, architectural design, performance optimizations, metadata management, and future development directions.
This article presents Kuaishou's end‑to‑end analytics platform, detailing the evolution from the early tool‑based stage through Service 1.0 and 2.0 to the unified Service 3.0 architecture, its unified analysis and query engines, data acceleration techniques, performance gains, and future intelligent analytics roadmap.
This article details how Alipay's data engineering team applied Elon Musk's five‑step work method to completely refactor a decade‑old merchant billing system, reducing overall complexity by over 60%, improving timeliness by an hour, cutting storage and compute costs by a third, and dramatically lowering operational and maintenance burdens.
This presentation shares the speaker’s experience and practical methods for creating an effective data platform, covering the transition from technical roles to product management, deep understanding of data workers' needs, Tencent Oura asset‑factory practices, a product‑management methodology, and a Q&A session that addresses governance, performance, and engineering challenges.
This article analyzes why data engineers face endless data‑retrieval requests, identifies common missteps in data‑construction such as demand‑driven development, lack of modeling and OLAP concepts, and proposes a dimension‑model‑based data warehouse with OLAP reporting, tooling, and knowledge‑empowerment to break the cycle.
This article outlines the importance of event tracking for data, describes iQIYI's five‑stage tracking system evolution, analyzes the challenges of the self‑service phase, presents the middle‑platform improvements, explains the migration strategy, and details the downstream data lake, real‑time stream, and data‑warehouse processing workflows.
Facing exploding data volumes and rising cluster costs, HuoLala adopted Apache Tez’s Remote Shuffle Service built on Apache Uniffle, redesigning the Tez client to operate without source modifications, detailing architecture, implementation challenges, testing, stability measures, and future plans to enhance big‑data shuffle performance and cost efficiency.
This article presents NewsBreak's practical deployment of Alluxio Local Cache with Presto on S3, detailing the system architecture, cache design considerations, implementation steps, performance metrics, and future optimization directions to reduce query latency and storage costs.
Airbnb’s Riverbed framework unifies streaming CDC events and batch Spark jobs behind a GraphQL‑based declarative API to automatically build and maintain distributed materialized views, using Kafka‑partitioned ordering and version control to deliver billions of daily updates with low‑latency reads for features such as payments and search.
This article details Bilibili's tag system from its 2021 inception through successive redesigns, describing the three‑layer architecture, data flow pipelines using Hive, Iceberg, Spark and ClickHouse, crowd selection DSL, online services with Redis, performance optimizations, and upcoming governance and quality initiatives.
This article describes how Ctrip's hotel recommendation team built a serverless, configuration‑driven data‑filling engine based on UnifiedPB protobuf schemas to improve development efficiency, reduce cost, ensure data quality, and achieve unified three‑region data delivery across more than twenty recommendation scenarios.
Gaode Technology’s 2023 roundup showcases fifteen of its most-read articles, spanning AI infrastructure evolution, cloud‑native data optimization, BEV‑based perception, real‑time crowdsourced mapping, ETA prediction, lane‑level navigation, AR HUD, architecture design, low‑code platforms, and high‑performance Android testing.
The article reviews how data development accelerated in 2023—with mature offline computing, rapid adoption of real‑time and lake‑warehouse solutions, and a clearer technical layering—while offering practical insights and future directions for professionals entering 2024.
Bilibili migrated its live‑guild analytics from MySQL to ClickHouse, creating a custom ReplicatedUniqueMergeTree engine that uses delete‑on‑insert, min‑max and hash‑bucketed indexes with delete bitmaps to achieve 10‑20× faster queries and scalable near‑real‑time reporting despite higher write latency.
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 article presents a comprehensive overview of financial large language models, covering their unique characteristics, construction methods, layered technical architecture, evaluation strategies, and real‑world use cases such as quality inspection, AIGC‑driven material generation, sales‑lead mining, and knowledge‑graph‑enhanced intelligent Q&A.
Senior Tencent product manager He Zhichao shares his experience and methodology for creating a high‑quality data platform, covering the transition from technical roles to product, understanding data users’ needs, the Euler asset‑factory implementation, product‑manager best practices, and solutions to common data‑engineering challenges.
This article presents a comprehensive overview of Xiaomi’s data production platform, detailing the full data lifecycle, the technical‑driven product design methodology, the platform’s architecture and core capabilities, as well as real‑world case studies and a Q&A session that illustrate how the system improves data collection, storage, processing, and usage across the organization.
In this interview, OPPO’s senior manager Lai Hongke explains how the company tackles the challenges of sparse, cross‑scenario data in app distribution by deploying a unified modeling framework, MMOE sharing, and the oCPX capability to simultaneously cut costs, improve recommendation performance, and preserve user value across its software store and game center.
This article outlines typical interview questions about handling a growing number of business metrics, structuring and managing metric definitions, providing unified services, and performing anomaly detection and attribution analysis, offering practical guidance for data‑focused roles.
The article teaches developers essential MaxCompute data‑processing skills—from creating and naming tables, handling strings and dates, and writing basic SELECTs, joins, and aggregations, to employing advanced techniques such as temporary tables, CTEs, partitioning, and map‑join hints for efficient complex queries.
