In this talk, a Baidu Waimai engineer explains the motivations, requirements, and architectural choices behind their open‑source ETL platform, covering data flow patterns, logical mappings, storage options, scheduling, metadata management, and quality monitoring to achieve scalable, transparent, and explainable data delivery.
This article introduces Flink’s background, architecture, and ecosystem, then demonstrates a step‑by‑step tutorial on using Flink SQL to clean and transform streaming data from Kafka, highlighting its advantages over Storm for real‑time ETL.
This article provides an extensive English overview of fifty additional big-data terminology and Apache open-source projects, explaining concepts such as Kafka, Hive, Spark, data cleaning, AI, graph databases, and many other tools and techniques essential for modern data engineering and analytics.
58.com’s intelligent recommendation system, evolving from a C++ monolith in 2014 to a Java-based micro‑service platform, integrates multi‑layer data processing, diverse recall and ranking algorithms, and a robust microservice architecture to deliver personalized listings across housing, jobs, cars, and more.
This guide walks beginners through the vast big data landscape, helping them choose the right role, understand essential terminology, plan a learning path, and access curated resources for becoming a data engineer or analyst, all illustrated with clear diagrams.
This guide walks beginners through the essential big‑data ecosystem—from understanding Hadoop’s core components and mastering MapReduce, to using Hive, SparkSQL, Kafka, and real‑time frameworks like Storm, while also covering data ingestion, export, scheduling, and introductory machine‑learning techniques.
This article outlines the critical competencies needed across the data field—from building and maintaining big data platforms and data warehouses to mastering visualization, analysis, mining, and deep learning—offering practical guidance for aspiring data professionals seeking long‑term career growth.
The article outlines the key competencies, responsibilities, and career development advice for data professionals across the entire data stack—from building big‑data platforms and data warehouses to visualization, analysis, algorithm engineering, and deep‑learning applications—emphasizing the importance of creating business value with data.
This article outlines the key competencies and career roadmap for data professionals, covering big‑data infrastructure, data‑warehouse engineering, visualization, analysis, algorithmic mining, and deep‑learning, while emphasizing the importance of business sense, cloud adoption, and continuous learning.
The article recounts the author’s early encounters with big‑data challenges, the shift from relational to NoSQL systems, the development of an immutable‑data batch architecture, and the eventual formulation of the Lambda Architecture, illustrating how simplicity and fault‑tolerance can replace complex incremental designs.
Alibaba senior technologist Luo Jinpeng explains how the company’s Double 11 media big‑screen platform evolved from chaotic early implementations to a robust, real‑time data system, detailing architectural redesigns, model refactoring, resource scheduling, and link reliability strategies that underpin its massive e‑commerce event.
This article outlines five practical commandments that help data scientists and data engineers collaborate more effectively, covering data awareness, tool familiarity, technical limits, mutual respect, and shared responsibility to ensure smooth project delivery.
This article explains what data is, classifies it into structured, semi‑structured and unstructured forms, describes data mining, databases, data warehouses, the full data lifecycle, and surveys the big‑data ecosystem including storage, batch and real‑time processing, resource scheduling, and visualization technologies.
This article details the architectural design and implementation of a scalable big data platform built on AWS services, highlighting the transition from HDFS to S3 for storage, the use of EMR for elastic compute, and a custom Execution Service integrated with Consul and Airflow for automated cluster management and task scheduling.
Airbnb’s engineering VP Mike Curtis explains how the company leverages Amazon Web Services, a Hadoop‑based big‑data platform, and custom tools like Aerosolve, Airflow, and Airpal to power its global marketplace, enabling rapid scaling, dynamic pricing, and personalized search through extensive cloud infrastructure and machine‑learning pipelines.
Explore a comprehensive big data skill map covering processing frameworks like Spark and Hadoop, databases, programming languages, analytics tools, visualization libraries, AI techniques, algorithms, data structures, and cloud computing services, providing a practical reference for building expertise in modern data engineering.
An in‑depth guide outlines technology‑agnostic best‑practice techniques for building high‑performance big data analytics systems, covering data acquisition, storage, processing, visualization, and security, and explains how to address the five V’s of big data to meet demanding operational and performance requirements.
In this expert Q&A, Baidu’s senior big-data specialists reveal common project pitfalls, argue for Java in Hadoop-style systems, discuss MongoDB deployment, outline criteria for choosing open-source versus self-built solutions, and evaluate the viability of NewSQL/Spanner-type startups.
This article explains Meituan's end‑to‑end recommendation system architecture, data processing pipeline, candidate generation strategies, model training and online ranking techniques, illustrating how data, algorithms, and real‑time signals are combined to improve relevance and conversion.
This article chronicles the step‑by‑step evolution of Dazhong Dianping’s data platform from 2012 to 2014, detailing changes in data models, storage and compute architecture, scheduling, monitoring, and data‑driven applications, offering practical insights for teams building early‑stage big‑data infrastructures.
This article explains what Hadoop is, outlines its key features, and presents a curated list of 15 high‑quality tutorials, video courses, and books to help beginners and professionals efficiently learn Hadoop and its MapReduce ecosystem.
