This article explains Flink's state concepts, the distinction between keyed and operator state, available state backends, TTL configuration, the mechanics of checkpoints and savepoints, and the two‑phase commit protocol for ensuring exactly‑once processing in streaming applications.
This article reviews the evolution of RocketMQ from its early MetaQ roots through the 4.x releases, explains the motivations behind RocketMQ 5.0, and details its cloud‑native architecture, lightweight SDK, storage‑compute separation, POP consumption model, elastic scaling, and the upcoming RocketMQ Streams framework.
The article explains various dimension‑table join approaches in Apache Flink, including preloading tables into memory, using distributed cache, leveraging hot storage with async I/O, broadcasting state, and temporal table function joins, and compares their trade‑offs for different data volumes and update frequencies.
This article shares practical insights on designing and operating a real‑time clickstream data warehouse using Flink for streaming processing and ClickHouse for near‑real‑time OLAP, covering dimensional modeling, layered architecture, Flink‑ClickHouse sink implementation, and data rebalancing strategies.
This article provides a detailed comparison of three popular open‑source change data capture tools—Debezium, Flink CDC, and Canal—covering their underlying principles, architecture, deployment options, performance characteristics, and suitability for real‑time data synchronization in big‑data environments.
This article explains a lightweight, loosely‑coupled solution for real‑time monitoring of HLS video playback in vehicle IoT, detailing how to embed device and session identifiers in HLS requests, modify M3U8 files, and collect statistics with NGINX Lua scripts.
This comprehensive guide walks you through creating a gRPC user service in Go and PHP, from defining protobuf messages and generating code, implementing server and client stubs, adding client, server, and bidirectional streaming, securing communication with TLS certificates, and managing request deadlines with timeout controls.
The article examines Apache Kafka's strengths and shortcomings, explores the operational complexities of managing a Kafka deployment, and encourages organizations to reassess its value versus emerging alternatives by weighing maturity, scalability, and total cost of ownership.
This article explores the evolution of data lakes, compares major cloud providers' lake architectures, introduces the emerging lakehouse concept, and provides a step‑by‑step Flink‑Iceberg implementation—including dependencies, catalog setup, table creation, checkpointing, and Kafka ingestion—demonstrating practical big‑data streaming solutions.
Apache Kafka 3.0 introduces major enhancements including KRaft consensus, deprecation of Java 8 and Scala 2.12 support, stronger producer guarantees, updated APIs, improved Kafka Connect, MirrorMaker 2 flexibility, and numerous KIP-driven feature upgrades, marking a significant step forward for the distributed streaming platform.
This article provides a comprehensive overview of Apache Flink's major releases from version 1.7 to 1.14, detailing new APIs, state management improvements, Kubernetes integration, SQL and Table API enhancements, checkpointing advances, and performance optimizations that together illustrate the platform's evolution for both streaming and batch processing workloads.
This article details the design and implementation of a real‑time, TB‑scale bill‑detail query platform that leverages Kafka for streaming, Debezium and Confluent Platform for change capture, Kudu for low‑latency storage, and Presto/Kylin for fast OLAP queries, while outlining deployment, integration, and future enhancements.
This article details Bilibili's comprehensive optimization of Flink real‑time computing, covering connector stability improvements, SQL interval‑join enhancements, runtime state and checkpoint refinements, a diagnostic tool, and future directions for high‑throughput streaming workloads.
This article details Douyu's virtual avatar tool built on Unity and Live2D, covering project background, core features, layered architecture, key technologies such as Unity rendering, FairyGUI UI, TCP-based IPC, model dressing and recoloring, face‑data processing, and future development plans.
Apache Kafka 3.0.0, released on September 21, 2021, introduces major changes such as deprecating Java 8 and Scala 2.12, adding Raft‑based metadata quorum, stronger producer delivery guarantees, removal of old message formats, numerous performance optimizations, extensive bug fixes, and a large set of new and updated JIRA issues across features, improvements, bugs, tasks, tests, and subtasks.
Apache Kafka 3.0.0 introduces a host of enhancements—including deprecated Java 8/Scala 2.12 support, Raft metadata snapshots, stronger producer guarantees, MirrorMaker 2 upgrades, and Kafka Streams improvements—while continuing to serve real‑time data pipelines and streaming applications.
Apache Kafka 3.0.0 introduces numerous enhancements including deprecation of Java 8 and Scala 2.12 support, KRaft metadata snapshots, stronger default producer delivery guarantees, expanded Connect and Streams APIs, updated MirrorMaker 2 configuration, and many KIP-driven feature and API changes for improved streaming and event processing.
This article provides a comprehensive guide to Apache Flink's Table API and SQL, covering required dependencies, the differences between old and Blink planners, program structure, table environment creation, catalog registration, query execution, conversion between DataStream and Table, update modes, and time attribute handling, with Scala code examples throughout.
This article explains Flink's memory architecture, covering on‑heap and off‑heap memory concepts, garbage collection, allocation strategies, memory segments, buffers, the memory manager, and how network transmission and back‑pressure are handled to achieve efficient streaming processing.
This article provides an in-depth overview of data lake concepts, definitions, and essential features, followed by detailed case studies of enterprise data lake implementations and comparative analysis of leading data lake table formats—Iceberg, Hudi, and Delta Lake—highlighting their architectures, capabilities, and trade‑offs.
This article provides a comprehensive beginner-friendly guide to Apache Kafka, covering its background, core concepts, producer and consumer settings with code examples, underlying architecture, operational monitoring, integration with Spark and Flink, and an introduction to Kafka Streams.
VR video clarity suffers because the required pixels‑per‑degree far exceed what 4K or 8K spherical streams can deliver, but tile‑based encoding that decodes only the viewport, combined with low motion‑to‑photon latency, distortion control, advanced codecs and AI‑driven projection, promises sharper, lower‑bitrate 6DoF experiences.
This article explains the motivation, component types, naming conventions, runtime mechanism, streaming protocol, design goals, and practical considerations of React Server Components, illustrating how they reduce client bundle size and enable progressive server‑side rendering with code examples.
This article examines the architecture of enterprise-level big data platforms at leading Chinese tech firms—Taobao, Meituan, and Didi—detailing their data sources, synchronization components, batch and streaming processing layers, scheduling systems, and common design patterns, while highlighting shared principles across these implementations.
Apache Pulsar is a cloud‑native distributed messaging platform that combines messaging, storage, and lightweight compute, featuring multi‑tenant support, geo‑replication, and high throughput, and this article introduces its core concepts, architecture components such as brokers, BookKeeper, ZooKeeper, and key design features.
This article describes how SmartNews integrated Flink into its Airflow‑driven Hive batch pipeline to cut the actions table generation latency from three hours to about thirty‑four minutes, detailing the technical challenges, design decisions, and production results.
This article summarizes frequent problems encountered when using Flink CDC with MySQL—including Kafka version conflicts, checkpoint timeouts, permission errors, global lock issues, and DDL parsing failures—and provides practical configuration tweaks and code examples to resolve them.
This article provides a clear, visual guide to Kafka’s core concepts—including producers, consumers, topics, partitions, consumer groups, message ordering, and the underlying ZooKeeper‑managed cluster architecture—helping readers grasp how Kafka enables reliable, scalable stream processing.
This article explains why Kafka is used, its buffering, decoupling, redundancy and robustness benefits, details the ack reliability levels, storage design, replica synchronization, ISR handling, consumer partition assignment strategies, transaction support, exactly‑once semantics, and why read‑write separation is not provided.
This article explains Kafka's high‑availability architecture, including multi‑replica design, ISR synchronization, leader election, acks configuration, and how misconfigured __consumer_offset replication can cause consumer outages, offering practical steps to ensure reliable message delivery.
This article explains the fundamental message exchange patterns—including publish‑subscribe, fan‑out, unidirectional and bidirectional streaming—as well as routing models such as unicast, broadcast, multicast, and anycast, illustrating each with common technology examples.
This article provides a comprehensive overview of Apache Flink SQL 1.13, detailing new Window TVF support, cumulate windows, performance optimizations, time‑zone handling, enhanced Hive compatibility, SQL client upgrades, DataStream‑Table conversion improvements, and outlines the roadmap for the upcoming 1.14 release.
ULTRON is a one‑stop real‑time data‑warehouse development platform built on FlinkSQL that unifies data integration, asset management, cluster deployment, modeling, ETL, OLAP analysis and governance, addressing the limitations of traditional batch‑oriented warehouses and simplifying streaming data workflows for developers.
This comprehensive guide introduces multimedia front‑end development, explains W3C media standards and HTML elements, explores media APIs, outlines playback scenarios and solutions, and details both consumer‑facing live video systems and production‑side tools such as streaming and video‑editing, while sharing Alibaba’s roadmap for the field.
This article explains how to implement both offline (T+1) and real‑time UV counting using Hologres with RoaringBitmap for high‑cardinality aggregation, and demonstrates a complete Flink‑Hologres pipeline—including table creation, streaming joins, windowed aggregation, and query examples—for fine‑grained user metric analysis.
This article explains the fundamental messaging exchange architectures such as Pub‑Sub, Fanout, Unidirectional and Bidirectional streaming, and the routing patterns including Unicast, Broadcast, Multicast and Anycast, illustrating how they are used in systems like Redis, Kafka, RabbitMQ and IBM MQ to simplify communication between producers and consumers.
This article explains the concept of data lakes, outlines a four‑layer open‑source architecture, presents several classic Flink‑Iceberg use cases, details why Iceberg was chosen, and describes the design of Flink’s streaming sink and upcoming community roadmap.
This article compares Apache Pulsar and Apache Kafka across performance, architecture, features, and real‑world use cases, highlighting Pulsar’s multi‑layer design, scalability, client language support, ecosystem integrations, and operational advantages while providing detailed analysis of storage, messaging models, and community resources.
This article walks through a complete Flink‑based recommendation system, detailing its v2.0 architecture, recommendation algorithms, front‑end and back‑end components, and step‑by‑step Docker deployment of MySQL, Redis, HBase, and Kafka services.
Flink SQL's idle state retention time feature prevents state explosion by automatically cleaning up state for keys that remain inactive beyond a configurable time window, requiring both minimum and maximum retention settings, with implementation details involving CleanupState, timers, and KeyedProcessFunctionWithCleanupState.
This article explains the end‑to‑end live‑streaming workflow, covering how a broadcaster pushes video and audio to a server, the various streaming protocols (RTMP, HTTP‑FLV, HLS, RTP), encoding formats, FFmpeg‑based decoding, hardware vs software decoding, and audio‑video synchronization techniques.
This article presents NetEase Game's Flink‑based streaming ETL system, detailing business background, log classifications, specialized and generic ETL services, Python UDF integration, runtime optimizations, HDFS write tuning, SLA metrics, fault‑tolerance mechanisms, and future roadmap for unified data lakes and PyFlink support.
The article explains how Kafka 2.8 removes its dependency on Zookeeper, detailing Kafka's core concepts, the role of Zookeeper in broker registration, load balancing, and controller election, the operational drawbacks of this coupling, and how KIP‑500 with a Quorum Controller modernizes the architecture.
This article extends the concepts introduced in Streaming 101 by deeply exploring data processing patterns for unbounded data, covering windowing, watermarks, triggers, accumulation modes, and their practical implications for building robust low‑latency streaming pipelines.
The article explains RTMP’s multiplexed, packetized streaming over TCP, detailing its chunk structure, message types, handshake, and connection workflow, then demonstrates livego’s publishing and pulling processes, discusses typical latency sources, and offers mitigation strategies and reference resources for developers.
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.
This article provides a clear, visual walkthrough of Kafka’s fundamental architecture, explaining how producers and consumers interact, the role of topics and partitions, consumer groups, and ZooKeeper’s coordination, helping readers grasp message flow, storage, ordering, and fault‑tolerance in a distributed streaming system.
This article presents Kuaishou's comprehensive engineering practices for improving Flink's stability, task startup latency, and SQL performance, including high‑availability Kafka connectors, fault‑recovery mechanisms, I/O reductions, asynchronous job upgrades, aggregation optimizations, and future resource‑utilization plans.
Kafka 2.8.0, released on April 19, 2021, introduces the groundbreaking Raft Metadata mode that eliminates the need for ZooKeeper, alongside numerous new features, bug fixes, and enhancements such as API controls for stream threads, SASL_SSL mutual TLS, and IP rate limiting.
This article walks through downloading the required Flink and Hudi components, building Hudi for Scala 2.12, and demonstrates step‑by‑step how to create, populate, query, and update Hudi tables in both batch and streaming modes using Flink SQL, complete with code snippets and result screenshots.
This article details Zhihu’s transition from a Sqoop‑driven data integration system to a Flink‑centric platform, covering business scenarios, historical architecture, design goals, technology choices, performance optimizations, and future plans for unified streaming‑batch processing across diverse storage systems.
This article demonstrates how to configure Flink 1.12 to read data from HBase, covering the required environment components, HBase table creation, Maven dependencies, Java POJO and Flink‑SQL code, and showing the query results with and without printing the TableResult.
Apache Kafka 2.8.0 introduces several significant enhancements, including a new group API, mutual TLS authentication for SASL_SSL listeners, JSON request/response logging, broker connection rate limiting, topic identifiers, self‑managed quorum replacing ZooKeeper, and numerous improvements to Streams and Connect APIs for more reliable real‑time data pipelines.
The Apache Pulsar Meetup in Shenzhen on April 17, 2021, co‑hosted by Tencent Middleware and StreamNative, will showcase Pulsar’s cloud‑native messaging, streaming, and storage capabilities through sessions on KoP migration, big‑data and IoT use cases, cloud‑native deployments, and the StreamNative Cloud Pulsar‑as‑a‑Service offering.
This article explains the principles of streaming server‑side rendering (SSR) in React, compares it with traditional client‑side rendering, and demonstrates how lazy loading and Suspense can be used together with streaming SSR to parallelize data and JavaScript delivery for faster first‑paint and improved user experience.
This comprehensive guide introduces Kafka as a high‑throughput, distributed, publish‑subscribe messaging system, detailing its core concepts, architecture, features, replication, log management, reliability guarantees, and typical use cases such as log collection, real‑time analytics, and cross‑cluster mirroring.
The article details how Netflix collects massive real‑time device logs, streams them through Kafka into Apache Druid, and uses this high‑performance analytical database to monitor, query, and continuously improve user experience at a scale of over two million events per second.
This article details common Flink streaming problems such as data skew causing task back‑pressure, oversized Kafka messages, high‑throughput ack settings, slot removal errors, checkpoint timeouts, and resource constraints, and provides concrete configuration changes and architectural adjustments to resolve them.
This article explains the upsert‑kafka connector in Flink, its configuration parameters, step‑by‑step usage with SQL examples, and demonstrates a complete pipeline that reads Kafka streams, aggregates page view metrics, and synchronizes the results to TiDB in real time.
This article shares ten practical pitfalls encountered while moving hour‑level Spark session processing jobs to Apache Flink, covering parallelism skew, state TTL, checkpoint handling, logging, debugging, state migration, Reduce vs Process, input validation, event‑time handling, and the trade‑offs of storing data inside Flink.
The article provides a comprehensive technical guide on Flink's FileSystem, JDBC, and Kafka connectors, detailing their source and sink implementations, core code logic, checkpoint handling, partition commit strategies, and complete SQL usage examples for streaming applications.
This article explains how Flink SQL can be used to construct a unified stream‑batch ETL pipeline for data warehouses and data lakes, covering data integration, CDC support, streaming writes to Hive and Iceberg, and various join techniques such as regular, interval, and temporal joins.
This article explains how to boost the efficiency of massive regular‑expression matching by using Intel's Hyperscan library, integrating it with Apache Flink for streaming processing, and providing deployment guidelines for both private and internal environments.
This article introduces Kafka Connect, explaining its purpose as a scalable and reliable tool for moving data between Apache Kafka and external systems, detailing its core concepts, architecture, deployment modes, configuration files, and a step‑by‑step example that streams data from a file source to a file sink.
Apache Beam 2.28.0 introduces extensive Parquet support, new hash functions in BeamSQL and ZetaSQL, ApproximateDistinct via HLL, enhanced I/O connectors including SpannerIO for Numeric fields, ParquetIO schema support, KafkaTableProvider thrift, HadoopFormatIO key/value cloning skip, and various other improvements.
This article explains how NetEase games collect heterogeneous logs, design a Flink‑driven streaming ETL pipeline, handle schema‑free sources, implement Python UDFs with Jython, optimize HDFS writes, manage real‑time and offline warehouses, and share practical tuning and fault‑tolerance techniques.
This article explores practical methods for optimizing Flink real‑time task resources on Kubernetes, focusing on memory usage analysis via GC logs and message‑processing capacity assessment, proposing automated detection of over‑provisioned memory and CPU, and outlining a workflow for resource adjustment to reduce costs.
This article details JD Search's journey from a Storm‑based pipeline to a Flink‑driven architecture backed by Apache Doris, covering business requirements, technical challenges, design trade‑offs, performance optimizations for massive traffic spikes, and future plans for their real‑time OLAP data warehouse.
This article demonstrates how to initialize, broadcast, and dynamically update rule sets in an Apache Flink fraud detection pipeline, using BroadcastProcessFunction and MapState to achieve runtime data partitioning without recompiling, and explains the underlying data exchange patterns such as forward, hash, rebalance, and broadcast.
The article details Apache Iceberg 0.11.0's core enhancements—including partition changes, SortOrder, extensive Flink and Spark integrations, CDC/Upsert support, hash‑based write distribution to reduce small files, and upcoming 0.12.0 roadmap—while providing practical SQL and API examples for data‑lake practitioners.
This article presents Meituan's use of Flink to enable incremental data warehouse production, covering the warehouse architecture, streaming data integration evolution, real-time OLAP applications, platform design, and future directions for unified stream‑batch processing.
This guide walks through setting up Apache Zeppelin as a low‑cost, SQL‑centric development platform for Flink, covering environment preparation, installation, interpreter configuration, execution modes, verification, common pitfalls, dimension‑table joins, custom UDFs, Redis integration, and dual‑stream join techniques.
The article reviews Flink 1.12's major enhancements, including comprehensive SQL capabilities, deep integration with Hive via catalog and streaming support, and a practical code example that demonstrates how to write streaming data into Hive tables while handling partition commits and small‑file merging.
This article provides a comprehensive introduction to Apache Kafka, covering its distributed publish‑subscribe architecture, its key components such as brokers, topics, partitions, producers, consumers, and ZooKeeper, as well as its advantages, drawbacks, storage mechanisms, partition assignment strategies, and reliability guarantees for high‑throughput big‑data streaming.
This article presents Tencent's experience of constructing a real‑time data warehouse by integrating Apache Flink with Apache Iceberg, covering background pain points, Iceberg's table format and capabilities, Flink‑Iceberg streaming and batch processing, practical implementations, and future roadmap for data‑lake acceleration.
iQIYI becomes the first video platform to deliver content meeting the China Ultra‑High‑Definition Video Industry Alliance (CUUVA) HDR standard, enabling devices like Xiaomi 10 Pro, Huawei P30 Pro, iPhone 11 Pro and XS Max to display richer colors, higher contrast, and deeper visual depth, while fostering an open, industry‑wide ecosystem and planning future CUVA HDR live‑streaming support.
Hulu’s Generation Stream study, conducted with Culture Co‑Op and industry experts, surveyed 2,500 U.S. viewers aged 13‑54, revealing that 90% watch TV via streaming, identifying three audience segments—‘Stream Most’, ‘Stream Only’, and ‘Stream Also’—and highlighting how streaming reshapes viewing habits and content expectations.
This article introduces Kafka’s fundamental role as a messaging system, explains topics, partitions, producers, consumers, replicas, consumer groups, and the controller, and explores its cluster architecture, performance optimizations like sequential writes and zero-copy, providing a comprehensive overview for building scalable data pipelines.
This article describes how LinkedIn’s Who Viewed Your Profile feature was originally built on a Lambda architecture, the operational challenges it caused, and the step‑by‑step migration to a streamlined, Samza‑driven, Lambda‑less design that improves performance, reduces maintenance overhead, and retains essential batch capabilities.
This article explains the concepts of streaming output and reactive programming, describes the underlying HTTP chunked transfer, SSE, WebSocket and RSocket protocols, provides code examples, and outlines practical scenarios where end‑to‑end streaming improves performance and user experience.
This guide explains how to integrate Flink 1.12 with Hive using HiveCatalog, covering required dependencies, Blink planner configuration, SQL dialect switching, Hive UDF support, temporal table joins, and provides complete code snippets for a streaming‑batch unified data warehouse solution.
This article presents a detailed collection of 2021 Flink interview questions covering checkpoint mechanisms, watermarks, state backends, join types, fault tolerance, resource configuration, and recent Flink 1.10 features, providing concise explanations and code examples for each topic.
This guide demonstrates how to build a Docker‑based Spark standalone environment, configure Apache Zeppelin to connect to it, and perform data analysis on local CSV files, HDFS, and streaming sources such as Twitter and Kafka, with complete code examples.
This article describes how a fast‑growing company built a layered real‑time data warehouse on Flink, detailing the evolution from a simple 1.0 pipeline to a 2.0 architecture with ODS, DWD and ADS layers, dimension joins, exactly‑once sinks, HDFS partitioning, monitoring, and future improvements.
This article provides a step‑by‑step guide on configuring the MySQL CDC connector for Flink, covering Maven and SQL client dependencies, MySQL user setup, connector options, table creation via SQL and Stream API, key features, common issues, and practical troubleshooting tips.
This article details how to migrate Spark Streaming jobs to Flink Streaming by creating custom SourceFunction and SinkFunction implementations, including a RocketMQ source connector and an HBase sink, with code examples, configuration tips, and discussion of checkpointing and watermark handling.
The MSU World Video Codec Competition highlighted iQIYI’s QAV1 encoder achieving faster speeds and superior compression compared to H.265, while the expanding AV1 ecosystem—bolstered by widespread hardware decoding, platform adoption, and recent QAV1 enhancements such as 8K, HDR, and improved rate‑distortion optimization—promises higher quality video at lower bandwidth costs.
This tutorial introduces Apache Beam’s unified programming model for batch and streaming, explains its core concepts and terminology, compares it with other runners, and walks through a complete Java word‑count example—including dependencies, pipeline construction, transforms, and execution with DirectRunner.
This article explains how Flink enables end‑to‑end machine‑learning workflows through AI Flow, covering the background of Lambda architecture, AI task stages, the advantages of Flink, AI Flow components, AI Graph concepts, integration with Python and TensorFlow, and a real‑world advertising recommendation use case.
This article explains how Apache Flink integrates with data lake architectures, especially using Apache Iceberg as a table format, to enable real‑time streaming ingestion, CDC processing, near‑real‑time lambda architectures, and future enhancements like automatic file merging and row‑level deletes.
This article explains how Flink enables end‑to‑end AI workflows through the AI Flow platform, covering the Lambda architecture background, AI task pipeline stages, the reasons for choosing Flink, AI Flow’s graph model, core services, integration with ML pipelines, and real‑world advertising recommendation use cases.
This article examines Apache Pulsar, an open‑source messaging platform created by Yahoo, compares it with Kafka by outlining Kafka’s common pain points, highlights Pulsar’s multi‑tenant architecture, layered storage, built‑in functions, and security features, and discusses the trade‑offs of each solution.
This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, explains its core algorithms, performance comparison with Spark ML, version‑wise feature evolution, and provides practical quick‑start instructions for both Java (Maven) and Python (PyAlink) users, including data source handling, type conversion components, unified file‑system operations, and an overview of its FM algorithm implementation.
This article explains why Spark Streaming introduced backpressure, how the dynamic rate‑control mechanism works, and provides a detailed walkthrough of the relevant source code, including the RateController class, its registration, and the execution flow that adjusts ingestion rates to match processing capacity.
This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, detailing its core algorithms, performance advantages over Spark ML, version evolution, Maven and PyAlink installation steps, data‑source integrations, FM algorithm support, and unified file‑system operations for both batch and streaming workloads.
This article examines how Netflix migrated its video‑streaming platform to AWS, adopted a microservices architecture, and built a global CDN, detailing the system’s components, design goals such as high availability, low latency and scalability, and the trade‑offs and resilience techniques employed.
Flink SQL, the high‑level SQL interface for Apache Flink, offers language‑independent, dependency‑free, easy‑to‑use stream processing with advanced features such as DDL, UDFs, time semantics, windowing, pattern matching, and built‑in connectors, supporting data synchronization, batch‑stream fusion, Hive integration, and various product enhancements.
The article explains how Flink 1.11 deepens its integration with Hive, covering background, new connector features, simplified dependency management, enhanced Hive dialect, streaming writes and reads, temporal table joins, and how these capabilities enable a unified batch‑streaming data warehouse.
This guide walks you through using the Python httpx library to perform various HTTP methods—including GET, POST, PUT, DELETE, HEAD, and OPTIONS—handle query parameters, decode responses, work with JSON, custom headers, form data, file uploads, streaming, cookies, redirects, and authentication, all with clear code examples.
Lyft transformed its legacy data pipeline by designing a cloud‑native, Flink‑based near real‑time analytics platform that ingests billions of events, writes Parquet files to S3, leverages Presto for interactive queries, and implements multi‑stage non‑blocking ETL, fault‑tolerant back‑fill, and extensive performance optimizations.
