This article presents a comprehensive design for a layered storage and unified scheduling platform that archives MySQL historical data, reduces storage costs, ensures high‑performance queries, and enables efficient data analysis through tiered hot, warm, and cold storage using big‑data technologies.
This article explains Ray Data’s execution pipeline, detailing the LogicalOptimizer’s architecture, core abstractions, rule‑based optimization process, and both logical and physical rule sets, with concrete code examples and practical illustrations of each optimization technique.
This article details Ant Group’s Flex vectorized engine built on Velox, covering the current state of vectorization, Flex’s architecture (Flink + Velox), core feature development, correctness guarantees, large‑scale deployment results, and future directions for full‑link vectorization and broader hardware support.
This comprehensive guide explains Ray Data's Dataset core type, its distributed pipeline design, lazy execution model, API groups, transformation methods, column operations, I/O integrations, metadata utilities, and execution workflow, providing clear code examples and practical usage tips.
This guide explains why data warehouses need a four‑layer architecture, defines the exact responsibilities and processing rules for ODS, DWD, DWS, and ADS, provides an end‑to‑end e‑commerce example, and lists common pitfalls with practical solutions.
This guide walks data engineers through the entire process of creating clear, production‑ready data architecture diagrams—from identifying the diagram type and defining layers, to selecting tools, drawing step‑by‑step components, applying visual standards, avoiding common pitfalls, and validating the final design for stakeholders.
Python’s reputation for slowness can be overcome by selecting the right tools—Numba, PyPy, CuPy, JAX, Ray, Joblib, async I/O, memory profilers, and big‑data frameworks—delivering speedups from 6× to over 50× with minimal or no code modifications.
This article explains how to extend a Flink CDC job that already syncs an entire MySQL database to Doris so that newly created tables are automatically created in Doris in real time, using the CdcTools utility, side‑output streams, and asynchronous I/O.
This article provides a comprehensive, business‑driven analysis of the Data Warehouse Service (DWS) layer, covering its core positioning, design goals, modeling and aggregation tactics, storage optimizations, typical challenges with practical solutions, and best‑practice recommendations for building efficient, cost‑effective data services.
This article details the deep integration of StarRocks with Apache Paimon, describing the unified architecture, version evolution, performance enhancements, time‑travel queries, native readers/writers, distributed planning, and future roadmap for achieving lakehouse‑native analytics at scale.
This article presents a production‑grade Flink ClickHouse sink that solves common pain points such as lack of size‑based batching, static table schemas, and distributed‑table latency by introducing data‑size batching, dynamic table routing, local‑table writes, load‑balanced node discovery, back‑pressure queues, dual‑trigger flush, and recursive retry with node exclusion, all integrated with Flink checkpoint semantics for at‑least‑once guarantees.
The article explains how Kuaishou modernized its data lake by partnering with Apache Hudi to address latency, storage cost, and consistency issues in both AI and BI pipelines, detailing architectural changes, new ingestion tools, partitioning strategies, compaction mechanisms, performance gains and future plans.
This article explains Apache Flink’s stream processing fundamentals, introduces the open‑source Flink Agents framework for building event‑driven AI agents, details Alibaba Cloud’s Flink AI Function for real‑time LLM inference, and showcases demos, architecture, integration patterns, and practical use cases such as VOC analysis, live‑stream analytics, and intelligent operations.
During Double‑11 mega‑sales, Taobao Group faced exploding OLAP query traffic, costly data sync pipelines, and slow near‑real‑time analytics, so they unified real‑time and batch data in Paimon, leveraged StarRocks for high‑performance lake queries, tuned cluster settings, and saved nearly ten‑million yuan annually while cutting refresh latency by 80%.
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.
ByteDance’s StreamShield delivers a three‑layer resiliency framework—engine self‑healing, hybrid replication at the cluster level, and chaos‑tested releases—that enables over 70,000 concurrent Flink jobs on 11 million CPU cores to meet strict SLAs with second‑level startup and robust fault tolerance.
This article analyzes the architecture, sync modes, latency, scalability, usability, and deployment aspects of four popular data synchronization solutions—Sqoop, DataX, Flink CDC, and Airbyte—and provides a practical decision tree to avoid common misuse pitfalls in enterprise data pipelines.
This guide walks you through designing and deploying a highly available HDFS architecture with dual NameNodes, ZooKeeper‑based failover, and a tuned YARN resource scheduler, covering detailed configuration files, failover testing, performance tuning, monitoring, automated health checks, capacity planning, and best‑practice checklists for production‑grade big‑data platforms.
Uber tackled the challenge of replicating over 350 PB of data across on‑premise and cloud lakes by redesigning Hadoop Distcp, moving intensive tasks to the Application Master, parallelising copy‑listing and commit phases, and leveraging Uber‑mapper jobs to dramatically cut latency and improve resource efficiency.
