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.
This article presents a comprehensive overview of data engineering practices for large model training, reviews current model scales and pre‑training data sources, discusses automated evaluation techniques, and explores how knowledge graphs can be integrated throughout the model lifecycle to improve quality and applicability.
This guide explains how data engineers can rapidly adapt to a new workplace by mastering business context, data domains, and system architecture, using structured learning, practical case studies, and continuous reflection to earn trust and deliver value efficiently.
This article explains how Alibaba's user growth platform leveraged RoaringBitmap in Hologres to accelerate massive user profiling, reducing analysis time from several minutes to around ten seconds by redesigning bitmap storage, optimizing data pipelines, and employing efficient SQL and scheduling techniques.
Real-time data processing transforms traditional batch pipelines by delivering fresh, low‑latency data to millions of concurrent users, leveraging event‑driven architectures, streaming engines, and real‑time databases, with use cases ranging from fraud detection to personalized e‑commerce and operational dashboards, and includes reference architectures and tool recommendations.
This article presents a comprehensive overview of data engineering practices, pre‑training data composition, automated model evaluation techniques, and the synergistic use of knowledge graphs within large‑scale AI model research, highlighting pipelines, quality criteria, and practical case studies.
The article details how iQIYI's Magic Mirror platform evolved from a simple single‑table reporting tool to a multi‑engine, self‑service big data analysis system that improves data access speed, reduces operational costs, and supports comprehensive business analytics across the company.
This article introduces user path analysis and the SessionAnalytics open‑source framework, covering business scenarios, data processing techniques, algorithmic mining methods, technical architecture, implementation details, comparisons with event‑based analysis, and a comprehensive Q&A for practical deployment.
The article shares a detailed interview experience for big‑data roles, outlining the proportion of problem‑solving, project, fundamentals, and open‑question segments, and highlights the technical depth expected in areas such as Flink, Hudi, SparkSQL, and OLAP.
This article introduces the Lambda Architecture for billion‑scale real‑time data analysis, explains its three layers—Batch, Speed, and Serving—covers its flexibility, fault tolerance, and scalability, and demonstrates concrete applications such as Twitter hashtag analysis and a smart‑parking recommendation system.
This article discusses the practical implementation of extracting table and field lineage information via the Spline REST API, analyzing API call frequency, server load tolerance, and the strategy of re-parsing lineage only when job versions change to optimize performance.
The article recounts NetEase’s presentation at the inaugural DataOps conference, detailing the evolution from DataOps 1.0 pipeline to a 2.0 integrated data development‑governance model, the challenges faced, practical solutions, and strategic advice for data managers.
This article details how JDQ, Flink, and ClickHouse were integrated to replace Elasticsearch for real‑time reporting, covering table‑engine selection, Flink sink implementation, performance bottlenecks, CPU hot‑spots, query optimization techniques, and strategies for handling high concurrency while ensuring data consistency and system stability.
This article explains why Kuaishou built a data lake, describes its lake architecture based on Apache Hudi and Flink, outlines five major production challenges—including ingestion bottlenecks, snapshot queries, update bottlenecks, merge limitations, and operational reliability—and details the practical solutions and future roadmap.
This article chronicles Nubank’s architectural evolution—detailing how strategic technology choices, cloud‑native platforms, micro‑service design, and data‑engineering practices were leveraged across startup, growth, consolidation, and expansion phases to achieve massive scalability and business agility.
OpenMLDB is an open‑source machine‑learning database that delivers a production‑grade, consistent online‑offline feature platform for real‑time AI applications such as recommendation, risk control and fraud detection, offering millisecond‑level feature computation, dual SQL engines, extensive ecosystem integration, and a roadmap of new capabilities.
This tutorial demonstrates how to accelerate processing of a 2.8‑million‑row CSV dataset by using Python's multiprocessing, joblib, and tqdm libraries, covering serial, parallel, and batch processing techniques, performance measurements, and best‑practice code examples for efficient large‑scale data handling.
This article presents ZhongAn Financial’s end‑to‑end MLOps workflow and real‑time feature platform architecture, detailing team roles, data pipelines, Flink‑based processing, TableStore storage, anti‑fraud feature design, and answers to common implementation questions, offering a comprehensive guide for building scalable, low‑latency ML services in finance.
This article details how the Performance Platform at Baidu speeds up data production and consumption across the company's R&D pipelines by introducing five optimization paths, 18 concrete methods, service tiering, compliance measures, and self‑service analytics for both real‑time memory tables and offline disk tables.
The article details HuoLala’s internally built YunTai BI platform, covering its motivation, system architecture, data source integration, zero‑code modeling, visual report and dashboard creation, performance optimizations, and future plans for stability and code design, illustrating a comprehensive big‑data visualization solution.
Baidu's Performance Platform speeds data production and consumption by adopting a unified stream‑batch architecture with TM and Spark, leveraging the Turing warehouse, introducing tiered service grading, robust governance and compliance measures, and offering self‑service analytics, cutting latency from minutes or days to milliseconds while handling billions of daily records and boosting SLA adherence, data accuracy, and user satisfaction.
The article presents Huya's data‑self‑service product, describing the problems of traditional data services, the principles of a good data service, the MVP implementation, architectural components, project outcomes, and future evolution, while also addressing common Q&A scenarios.
This article details Du Xiaoman's development of the ATLAS automated machine learning platform, covering business scenarios, AI algorithm deployment challenges, the end‑to‑end production workflow, platform components such as annotation, data, training and deployment, as well as optimization techniques like AutoML, meta‑learning, NAS, and large‑scale parallelism, concluding with lessons learned and future directions.
This article details Bilibili's 北极星 user behavior analysis platform, tracing its evolution from early Spark‑Jar models to Flink‑ClickHouse pipelines and Iceberg‑based full aggregation, and explains the technical solutions for event, retention, funnel, path analysis, data ingestion, cluster rebalancing, and performance optimizations that enable massive real‑time analytics on billions of daily events.
This article details Kuaishou’s three‑year evolution of its metric middle platform, covering the data infrastructure, key challenges of data inconsistency and low analysis efficiency, the enterprise‑level OneMetric solution, architectural design, development phases, practical lessons, system implementation, and real‑world applications.
Dolphin Streaming provides Alibaba’s advertising merchants with a DB‑like, SQL‑driven real‑time data platform built on Flink that abstracts storage and compute, enabling non‑engineers to develop, debug, and deploy streaming feature jobs quickly, boosting query volume, QPS, and revenue.
This article explains the Lakehouse concept, why it is needed, the limitations of traditional data warehouses and data lakes, and how Databricks’ unified architecture—through open storage formats, fine‑grained governance, and optimized query engines—delivers high‑quality, low‑latency data for BI, analytics, and machine learning workloads.
This article shares engineering practices for constructing a tag‑profile system, covering core concepts, minimal architecture, technology selection, key modules such as estimation, selection, deployment, and validation, and offers design details and implementation tips for large‑scale marketing scenarios.
This article details Zhuanzhuan's user profile platform, covering its business-driven motivation, tag taxonomy, system architecture, data pipelines using Hive, ClickHouse and Spark, storage design, per‑user insight, segmentation techniques, ID‑mapping, and future plans for real‑time tagging.
The article presents a comprehensive risk insight platform built on ClickHouse, Flink, and intelligent algorithms, detailing its architecture, technical challenges, solutions, real-time data modeling, practical applications in fraud detection and user behavior analysis, and future optimization directions.
This article explains the practical steps to transform a company into a data‑driven organization by establishing a self‑service culture, aligning organizational structures, defining key roles such as analysts, engineers, scientists, and CDOs, and addressing common obstacles and best‑practice tips.
The 2022 Open Source Big Data Heat Report analyzes 102 active projects since 2015, revealing that heat values double every 40 months, highlighting diversification, integration, and cloud‑native trends, and offering guidance for developers, contributors, and project maintainers navigating the evolving big‑data landscape.
The article outlines comprehensive data‑engineering and warehouse‑design principles—covering collection (four Ws and methods like SDK, point‑code, binlog), reporting strategies, source selection, modeling with fact, aggregation, dimension and model tables, quality checks, and governance practices such as standardized SDKs, metric libraries, automated lineage, and cost optimization—to share actionable experience for any organization.
This tutorial walks through creating a simple “Hello World” Apache Airflow DAG by setting up the Python file, importing modules, defining the DAG object, adding a PythonOperator task, writing the callable function, and running the DAG with Airflow’s webserver and scheduler.
Hulu’s data platform team describes how they migrated large‑scale Spark workloads from Yarn to native Spark on Kubernetes, leveraging AWS services such as EKS, S3, and custom operators to achieve dynamic scaling, unified monitoring, cost‑effective resource management, and improved stability for search, recommendation, and advertising pipelines.
This article analyzes the technical difficulties of building a data‑driven full‑link system and describes how the Kai Shu Storytelling app overcame them by adopting DataFinder for automated event tracking, metric management, and growth analysis, offering practical guidance for enterprises and developers.
This article compares Elasticsearch and ClickHouse for log analytics, presents cost analysis, and provides detailed deployment instructions for Zookeeper, Kafka, Filebeat, and ClickHouse to build a private, high‑performance backend data platform for SaaS services.
This article presents KuaiKan's data governance journey, detailing the rapid business expansion challenges, the three‑step planning framework, the logical closed‑loop architecture, practical implementation experiences, cross‑team collaboration techniques, and the evaluation of governance outcomes and future plans.
This article explains DataOps and its role in modern ETL pipelines, outlines the benefits of DataOps for efficiency and reliability, and provides a detailed roadmap and best‑practice guidelines for planning, implementing, and optimizing DataOps‑driven ETL in cloud‑native environments.
The article reviews the shift of big‑data computing from batch to real‑time, outlines the evolution of one‑stop real‑time data warehouses, introduces Alibaba's Hologres solution and its technical advantages, and shares the CCO department’s three‑generation architecture upgrades and practical use cases.
Alibaba Front‑End Intelligent Technology combines PipCook, DataCook, and imgcook to enable data‑driven UI generation, on‑device AI inference via WASM‑Rust‑SIMD and WebGPU, and applications such as code IntelliSense and design‑to‑code, while outlining a roadmap toward unified AI‑powered interfaces for commerce.
The YoDA project tackles the growing entropy of Ant Financial's risk data platform by introducing white‑box visibility, logical abstraction, and integrated heterogeneous fusion, enabling systematic governance, cost reduction, and consistent decision‑making across online, offline, and near‑line environments.
During a pre‑GIAC interview, Shopee data engineer Deng Lin discusses the evolution of data lakes and warehouses, lakehouse integration, big‑data technology choices, real‑time processing with Flink and Kafka, and offers career advice for newcomers to the big‑data field.
Bilibili migrated its massive offline platform from Hive to Spark using an automated SQL rewrite and dual‑run verification, cutting execution time over 40% and resource use 30%, while introducing small‑file merging, shuffle stability, runtime filters, data‑skipping, lineage tracking, auto‑parameter tuning, and metastore federation for robust large‑scale processing.
This article presents OPPO's commercial data system construction practice, covering the data platform strategy, ingestion pipelines, development efficiency toolkits, data validation, visualization aids, UDF principles, warehouse architecture, metric systems, dimensional modeling, ETL optimization, governance metadata, quality management, monitoring, attribution services, analytics reporting, and a Q&A session.
The article outlines five major 2022 data trends— the rise of analytics engineers, the intensifying lake‑house competition, the growth of real‑time streaming pipelines and operational analytics, the expanding cloud marketplaces for data tools, and the push toward unified data‑quality terminology—explaining their origins, market impact, and future outlook.
This article explains how Delta Lake adds reliability to data lakes by offering ACID transactions, scalable metadata, and unified batch‑and‑stream processing, outlines the challenges it solves, details its implementation principles, and demonstrates a practical demo for building an integrated data warehouse.
This article describes how Halodoc’s data engineering team identified limitations of their Redshift‑based platform, evaluated a LakeHouse design, selected Apache Hudi for mutable data handling, and outlined the challenges and benefits of building a scalable, decoupled storage‑compute architecture for their growing healthcare services.
This article presents a comprehensive overview of an intelligent risk control platform, covering its design background, six core characteristics, the "five‑full double‑core" concept, end‑to‑end strategy and model lifecycle management, business architecture atomization, and real‑world anti‑fraud case studies.
This article explains the harms and root causes of data quality problems—such as integrity, latency, accuracy, and consistency issues—then outlines systematic prevention methods, baseline monitoring, and concrete NetEase YouShu platform practices, illustrated with real incidents, code snippets, and tag‑monitoring strategies.
This article presents the design, architecture, and implementation of a unified penalty data center for 58 Group, detailing the challenges of heterogeneous data sources, the selection of Flink for real‑time ETL, the use of a DSL and LRU aggregation, and the adoption of MVEL for feature recognition to achieve standardized, high‑performance penalty data processing.
The 2021 software industry review highlights the rise of hybrid work, the continued dominance of microservices, emerging data engineering and AI/ML practices, ethical and sustainability concerns, multi‑cloud and cloud‑native adoption, and anticipates further developments in these areas throughout 2022.
In 2022 the modern data stack will be driven by the rise of analytics engineers, intensified competition between lakehouse and warehouse solutions, growing demand for real‑time analytics, the explosive growth of cloud marketplaces, and the emergence of unified data‑quality terminology, all reshaping data infrastructure and operational practices.
InfoWorld's 2021 BOSSIE Awards highlight 29 standout open‑source projects—from front‑end frameworks like Svelte to cloud‑native tools such as Minikube, AI platforms like Hugging Face, data‑engineered solutions including Presto and Apache Arrow, and many more—offering developers a curated snapshot of the most influential software of the year.
The article introduces Netease Thunderfire UX's big data team and its multidisciplinary approach to game user experience, covering topics such as mathematics, engineering, product practice, AI, visualization, platform development, data analysis, and tool creation for game data professionals.
To celebrate the 2022 New Year, Meituan’s technology team offers a curated gift of the 22 most‑read and most‑watched 2021 technical articles—spanning logging, knowledge graphs, GraphQL, data warehousing, performance, security, and more—while inviting readers to complete a survey for a chance to win a premium keyboard wrist rest.
This article introduces the Alibaba Cloud Community's big‑data section, outlines its extensive learning resources—including e‑books, Q&A, learning paths, open courses, and activities—explains why the industry has shifted toward cloud‑based platforms, and provides links for deeper exploration, all aimed at helping newcomers advance in big data engineering.
This article details JD Retail's strategic "Nirvana" product‑selection platform, describing the technical challenges of handling billions of items and hundreds of tags, and presenting a dual‑engine solution using ClickHouse and Elasticsearch with Spark‑driven data pipelines to achieve fast filtering, multidimensional analytics, and efficient storage.
This article presents the design, implementation, and operational experience of Tencent's game big‑data analysis platform, covering its background, the offline, online, and real‑time multi‑dimensional analysis engines, practical use cases, performance optimizations, and future roadmap.
This article outlines six practical strategies—identifying bottlenecks, empowering data engineers, adopting distributed management, creating data contracts, embracing diverse perspectives, and streamlining workflows—to help organizations leverage cloud data warehouses more efficiently and drive better business intelligence outcomes.
The article analyzes post‑2022 data development trends, explains why high‑end positions are scarce while entry‑level roles are highly competitive, and provides detailed campus and social recruitment interview advice, including required skills, project experience, and strategies for standing out in a rapidly maturing big‑data industry.
This article outlines a comprehensive seven‑stage workflow for building enterprise user portraits—from goal interpretation and requirement analysis through tag development, scheduling, service‑layer integration, productization, optimization, and finally deployment and performance tracking—highlighting critical outputs and common challenges at each step.
EMR Studio is a one‑stop, open‑source‑compatible big data development platform that integrates Zeppelin, Jupyter, Airflow and a custom Cluster Manager to streamline job creation, scheduling, monitoring, and cluster switching, thereby addressing common usability challenges in Spark, Flink, Hive, and Presto workflows.
The article analyzes recent Q3 financial reports of major internet companies, discusses the uneven development of data engineering talent, examines the challenges of data platforms and middle‑office services, and offers practical advice for developers to broaden technical depth, improve soft skills, and increase resilience in a tightening market.
This article presents Tencent's game big‑data analysis platform, detailing its background, the architecture of the iData engine—including offline multi‑dimensional analysis (TGMars), online portrait analysis (TGFace), and real‑time multi‑dimensional analysis (TGDruid)—application scenarios, performance insights, and future ecosystem and open‑source plans.
This article explores why small and medium enterprises struggle with big‑data adoption, proposes partnership‑based strategies to gain access to large datasets, and offers concrete technical roadmaps—including distributed storage, streaming pipelines, and query stacks—to help engineers practice high‑concurrency big‑data systems.
This article provides a comprehensive guide to designing and implementing a big‑data platform, covering architecture overview, data ingestion with Flume, storage on HDFS/Hive/HBase, processing engines such as Hive, Spark and Flink, scheduling solutions like Azkaban and Airflow, and the construction of self‑service analytics systems.
This guide outlines the fast‑growing data engineering career path, covering essential Linux fundamentals, programming languages, testing, database concepts, data warehouses, processing frameworks, messaging systems, cluster computing, workflow scheduling, monitoring, infrastructure as code, and CI/CD tools.
The article describes how Yixin Group’s product team created an in‑house intelligent data warehouse using Hadoop, Flink/Spark, and standardized data services to transform scattered automotive‑finance data into a secure, scalable platform that supports real‑time analytics and drives business growth.
This article, discovered on the Yunqi community site, provides a system‑architecture perspective overview of current big‑data architecture hotspots, development trajectories, emerging trends, and unresolved challenges, while highlighting the field’s rapid evolution and recommending a curated list of in‑depth resources for further study.
The "God of Big Data" project, launched in 2019, offers a detailed learning roadmap, systematic column resources covering Hadoop, Spark, Kafka, and more, and invites engineers transitioning from backend to big‑data development to follow curated articles, GitHub code, and CSDN tutorials.
Airbnb’s Midas certification establishes a company‑wide, multi‑dimensional golden‑standard for data quality—covering accuracy, consistency, timeliness, cost, and completeness—by requiring collaborative design, automated health checks, and four review stages, ensuring certified data is reliable, well‑documented, and ready for reporting, experimentation, and machine‑learning.
This article details Meitu's adoption of the Presto ad‑hoc ROLAP engine, comparing it with Hive on Spark and Impala, describing enhancements for coordinator high‑availability, and explaining a cross‑cluster scheduling strategy that leverages idle Presto resources to improve overall big‑data workload efficiency.
This article reviews the evolution of big data technologies in the last five years, covering streaming and batch processing frameworks, column‑store NoSQL databases, programming language trends, the cloud‑native multi‑model database Lindorm, and practical Flink/Blink usage with code examples.
The DSU mid‑year reading collection compiles high‑quality technical articles, reflective essays, and internal job referrals across data architecture, big‑data ecosystems, machine learning, data governance, and career development, providing a searchable resource for data professionals.
This article, compiled from a Volcano Engine meetup, explains how ByteDance’s unified traffic platform designs, governs, and processes massive event‑tracking data in real time, covering embedding content solutions, link architecture, dynamic processing engines, and data‑governance practices that support trillions of daily events.
Airbnb’s 2019 Data Quality Improvement Plan reorganized its data‑engineering workforce, introduced a dedicated data‑engineer role, adopted a decentralized Minerva‑based architecture with Spark pipelines, instituted rigorous testing, governance, and certification processes, and established SLAs and monitoring to ensure timely, trustworthy, well‑documented data across the enterprise.
This article details JD Logistics’ challenges in handling petabyte‑level data, outlines their existing data architecture, and explains how they adopted Apache Doris for faster, scalable analytics, covering table management, data import workflows, visualization tools, and future roadmap for data engineering.
This article details the design and multi‑stage optimization of an A/B testing metric system, describing its product architecture, Spark‑based computation engine, ClickHouse OLAP layer, cumulative calculation improvements, and batch processing techniques that reduced processing time from hours to a few minutes for hundreds of experiments and metrics.
SQLFlow is an open‑source platform that lets users build and run machine‑learning and deep‑learning models directly from SQL statements, lowering the barrier for business analysts to apply AI by abstracting complex pipelines into familiar database queries.
This article presents an extensive glossary of more than fifty big‑data concepts—including Apache projects, data‑analysis methods, storage formats, AI‑related terms, and emerging metrics—providing concise English explanations for each term.
At the 2021 QECon conference in Shenzhen, Zhongtong Express senior architect Wu Da detailed the design and evolution of their big data quality assurance framework, covering six key layers and highlighting future trends in predictive analytics and deep business integration.
This article examines the challenges of storing massive DMP ID mappings in Redis—including memory fragmentation, expansion, and latency constraints—and presents eviction, bucket‑hashing, and fragmentation‑reduction techniques to achieve efficient, real‑time, large‑scale key‑value storage.
A unified semantic layer for data development solves metric‑change ripple effects, developer burden, and large‑scale query performance problems by offering consistent metric definitions, multi‑view access, concise auto‑generated SQL, instant propagation of updates, and engine‑driven optimal query selection, thereby bridging business and engineering and cutting maintenance effort.
ClickHouse, born from Yandex’s Metrica and now a top‑50 open‑source analytics engine, achieves exceptional speed through a vectorized compute engine, column‑store architecture, and an active community, powering real‑time workloads at companies like Tencent Music, Sina, Bilibili, and Suning while introducing features such as column merging, projections, and storage‑compute separation for future scalability.
This article details BTC.com’s journey from a legacy batch‑oriented analytics stack to a modern real‑time OLAP architecture using Flink, ClickHouse, Kafka, and Kubernetes, highlighting the business drivers, technical choices, architectural evolution, optimizations, and future directions.
This article details Baixin Bank's construction of a Flink‑driven real‑time computing platform integrated with Hudi as a real‑time data lake, covering background, architecture, data collection, transformation, storage layers, technical challenges, future roadmap, and practical lessons for similar big‑data initiatives.
The inaugural Techo TVP Developer Summit in Shenzhen gathered over 500 developers to explore the latest trends in databases, distributed systems, big data, and cloud‑native technologies, offering expert analyses, real‑world case studies, and career guidance for data professionals.
Meituan’s hotel‑travel platform tackled exploding data‑quality, cost, efficiency, and security issues by establishing a full‑link governance framework—standardized processes, a Data Management Committee, and unified “One Model, One Logic, One Service, One Portal” systems—that cut per‑unit costs by ~40%, boosted engineer productivity over 60%, eliminated major security incidents, and set the stage for autonomous, AI‑driven data governance.
To meet iQIYI video production’s thousands‑QPS, petabyte‑scale, frequently‑updated data and large‑table join requirements, the team built a Spark‑plus‑ClickHouse real‑time warehouse that streams Kafka changes, joins HBase dimensions, and writes to ClickHouse, reducing reporting development time from days to hours while supporting both offline and real‑time analytics.
This article presents an in‑depth technical overview of Kuaishou's massive HDFS deployment, detailing its architecture, petabyte‑scale data and thousands‑of‑node clusters, the key scalability challenges faced, and the custom solutions—including FixedOrder, RBF balancer, observer read, slow‑node mitigation, and tiered protection—implemented to keep the system performant and reliable.
The article explains why shifting batch jobs, reporting, and machine‑learning model training from traditional backend services to a dedicated data platform can simplify development, improve fault tolerance, and scale analytics, using real‑world examples from Spotify and best‑practice guidelines.
The article outlines a three‑dimensional framework—technical, business, and soft‑skill competencies—that algorithm engineers need to master in order to successfully deliver machine‑learning solutions in production environments, offering practical advice on data handling, model evaluation, stakeholder communication, and personal development.
The article examines how data teams can select the appropriate Ops framework—DataOps, MLOps, or AIOps—by comparing their origins, principles, responsibilities, and tooling, and stresses that cultural principles outweigh technology choices for efficient delivery of data and machine‑learning products.
This article details how Beike built a comprehensive Data Management Platform (DMP) that integrates user behavior and business data across multiple apps, outlines its five‑layer architecture, discusses data collection, processing, storage, real‑time profiling, and presents performance results and future optimization directions.
This article, presented by senior data platform engineer Mao Xiangyi of TAL Education, details the design and implementation of the company’s real‑time T‑Streaming platform, covering its three‑layer data architecture, batch‑stream integration techniques, ODS layer real‑timeization, Flink SQL development workflow, hybrid‑cloud deployment, and a case study of K‑12 renewal reporting.
This article presents a comprehensive overview of a recommendation system, covering its business background, purpose, detailed engineering architecture—including data sources, computation, storage, online learning, service and access layers—and discusses key challenges, module design, and practical reflections.
The author recounts a month‑long, four‑round technical interview at Alibaba Mama, detailing phone, on‑site, and HR stages, with deep discussions on Spark, Kafka, Hadoop, platform design, and backend fundamentals, while sharing resource links for big‑data interview preparation.
This article provides an in‑depth overview of Apache Airflow, covering its core concepts, advantages, architecture components, installation steps, example ETL DAG code, common command‑line tools, and practical tips for leveraging Airflow in data engineering workflows.
This article shares Tubi's practical experience in rapidly iterating machine‑learning systems, emphasizing the early importance of simple end‑to‑end A/B testing platforms, clear launch plans, heat‑based and embedding‑based ranking models, and a culture of fast experimentation over complex deep‑learning research.
