This article provides a comprehensive introduction to Apache Kudu, covering its origins, cluster architecture with Raft consensus, schema‑based table and partition design, and the intricate storage engine that combines in‑memory and on‑disk structures to deliver fast OLTP and OLAP capabilities on fast data.
This article explains how massive online data can be captured, structured, and analyzed in real time using a Lambda‑style architecture, then introduces a simplified Lambda‑Plus design built on Alibaba Cloud's Tablestore and Blink to meet both batch and streaming requirements while reducing operational complexity.
This article explains how Apache Flink manages state, uses checkpointing for fault-tolerant recovery, and achieves exactly-once semantics when consuming Kafka streams by persisting offsets, describing the checkpoint mechanism, recovery process, and practical considerations for production deployments.
This article explains how Apache Flink builds its RPC communication layer on top of Akka by detailing the Actor model, actor system creation, message passing patterns, key RPC interfaces such as RpcGateway and RpcEndpoint, and the internal workflow of request handling and execution.
This article explains the principles, use cases, and performance considerations of Spark SQL's three join implementations—Broadcast Hash Join, Shuffle Hash Join, and Sort‑Merge Join—illustrating how table size and distribution affect the choice of algorithm for efficient large‑scale data processing.
Didi’s platform handles over 700 billion daily ETA requests using AI‑driven real‑time calculations, while its 6,000‑plus engineers rely on open‑source big‑data, cloud and AI frameworks, contribute 23 projects that have earned more than 36,000 stars, and provide anonymized traffic data to academia for transportation and urban‑planning research.
This article explains how leveraging HBase's SnapshotScanMR feature to create a custom hbase2hiveBySnapshot task dramatically reduces region server pressure, halves ETL execution time, and improves cluster stability for large‑scale data back‑fill operations.
This article explains how Apache Pulsar and Apache Flink can be combined to provide a unified, scalable, and fault‑tolerant data processing platform, covering Pulsar's architecture, its differences from other messaging systems, various integration patterns, and concrete code examples for stream and batch workloads.
This article outlines practical techniques for achieving high system availability, covering multi‑level caching, dynamic group switching, database and service isolation across data centers, concurrency control, gray‑release deployment, comprehensive monitoring, graceful degradation, and data consistency models, with insights on leveraging big‑data pipelines for intelligent logistics.
This article explains how Ctrip’s hotel data intelligence platform handles over ten billion daily data updates and nearly a million queries by adopting ClickHouse, detailing the system's background, the reasons for choosing ClickHouse over other solutions, the data ingestion pipelines, monitoring strategies, operational practices, and performance outcomes.
The article traces the evolution of autonomous driving from early concepts to modern implementations, highlighting Pony.ai’s technical innovations in sensor fusion, high‑definition mapping, simulation, data processing, software iteration, and the challenges of scaling vehicle fleets for commercial deployment.
Magic Mirror is a big‑data‑based visual analytics platform that lowers the barrier of machine‑learning for non‑experts while accelerating expert workflows through visual UI, modular algorithms, distributed feature generation, and automated binary‑classification modeling.
This article examines the technical challenges of large‑scale data processing, compares the classic Lambda and Kappa architectures, introduces the unified stream‑batch Lambda+ design built on Tablestore and Blink, and outlines suitable scenarios and practical solutions for modern big‑data systems.
This article presents a carefully organized catalogue of over a hundred technical posts covering Flink source‑code analysis, fundamental and advanced big‑data structures, Hadoop ecosystem components, real‑time streaming with Spark and Kafka, as well as system design guidelines and miscellaneous insights, each linked to its original publication for easy reference.
On June 29, the Apache Flink Beijing Meetup presented a comprehensive analysis of Flink 1.9’s major architectural changes, new Table API & SQL capabilities, runtime and core enhancements, and future roadmap, with slides and resources made available for download.
The 2019 Beijing Software and Information Service Industry Development Report reveals that the sector’s scale exceeded one trillion yuan, with double‑digit growth in cloud computing, big data, AI and cybersecurity, while talent, investment, and regional collaboration propelled the city to a leading national position.
This article provides a step‑by‑step tutorial on configuring Hadoop high availability, covering HDFS HA architecture, Quorum Journal Manager synchronization, NameNode failover, YARN HA, required pre‑conditions, cluster planning, configuration files, service startup, and verification procedures.
This article provides a comprehensive guide to improving Hive query performance by covering column and partition pruning, predicate pushdown, replacing ORDER BY with SORT BY, using GROUP BY instead of DISTINCT, fine‑tuning join operations, and optimizing MapReduce parameters such as mapper/reducer counts, file merging, compression, JVM reuse, parallel execution, strict mode, and storage formats.
The article examines Hadoop’s RPC framework—detailing its client‑server workflow, core classes (RPC, Client, Server), dynamic proxy handling, NIO‑based server threading, configurable concurrency controls such as FairCallQueue, and a practical HDFS mkdir command example, illustrating high‑performance distributed communication.
Kafka achieves its ultra‑high throughput and low latency by writing data to the OS page cache, performing sequential disk writes, and employing zero‑copy techniques that eliminate unnecessary data copies during consumption, enabling tens of thousands to millions of messages per second.
This article provides an in‑depth overview of Flink SQL, covering its origins, the latest 1.7.0 and 1.8.0 enhancements, the underlying programming model, common operators and built‑in functions, and a complete end‑to‑end example that analyzes NBA scoring‑leader data using Flink SQL.
Suning’s senior tech leader explains how the company leveraged AI, big data, cloud computing and IoT to drive a digital‑first retail ecosystem, illustrating the broader shift toward intelligent, data‑driven retail operations in a rapidly changing market.
This article explains the design and implementation of Spark Structured Streaming's StateStore module, covering its distributed architecture, state sharding, versioning, batch read/write, migration, update/query APIs, maintenance compaction, and fault‑tolerance mechanisms that enable incremental continuous queries with exactly‑once guarantees.
The article explores the rapid rise of data middle platforms in China, tracing their historical roots, explaining their core purpose of unifying data across legacy and new systems, showcasing Shulan Technology’s real‑world implementations, and analyzing market dynamics and future opportunities for enterprises and startups alike.
This article explains core data‑warehouse concepts—including DB, DW, ODS, OLTP, OLAP, BI, and the differing meanings of DM—as well as their relationships, integration examples, and why OLAP cannot replace data mining, providing a concise reference for beginners in data analytics.
The article breaks down artificial intelligence into four essential components—massive data, powerful compute, effective algorithms, and real‑world scenarios—explaining each element with concrete analogies, hardware benchmarks, algorithm classifications, and a list of typical AI applications.
This guide walks you through installing JDK, Logstash, Ruby, and required plugins, configuring Logstash to pull data from a MySQL table, and sending it to Elasticsearch, including code snippets, configuration files, and troubleshooting tips for a smooth data synchronization.
Xianyu IFTTT is a scalable real-time user-relationship platform that enriches metadata, enables bidirectional buyer-seller interactions, integrates quickly via SLS logs, uses a chain-of-responsibility for customizable lists, processes push actions with fatigue filtering, and stores TB-scale data in Lindorm, delivering billions of daily records and more than double the click-through rate of offline pushes.
An in‑depth personal account details how the author prepared for ByteDance’s (TouTiao) recruitment, outlining a month‑by‑month study schedule covering Java, big‑data technologies, algorithms, and system fundamentals, describing each interview round, sharing successful test strategies, and offering practical advice for landing offers at top tech firms.
This article provides a detailed introduction to FlinkCEP, covering how to add the library, define simple and composite patterns, use quantifiers and conditions, handle skip strategies, time constraints, and select results, with complete Java and Scala code examples for complex event processing.
This article presents a comprehensive case study of the Dada‑JD Daojia big data platform, detailing its evolution from a MySQL‑based warehouse to a multi‑layered One Data, One Platform, One Service, Many Apps architecture, the technical challenges faced, and the strategic approaches adopted to ensure coverage, accuracy, stability, and scalability.
This article explains how a high‑traffic messaging platform with tens of millions of users and billions of daily records can be optimized using MySQL partitioning, sharding (both client‑side and proxy‑side), vertical database splitting, and practical migration scripts to maintain performance and availability.
This article explains the evolution of Spark SQL from Shark, describes its key features such as SchemaRDD and in‑memory columnar storage, compares row‑based and column‑based storage, and provides practical Scala code examples for creating DataFrames and loading data from various sources.
In this interview, 360’s Big Data Center Technical Director Xu Hao discusses the critical data security challenges faced by enterprises, outlines regulatory, system‑level, and managerial risks, and shares practical strategies for building robust security governance, platform architecture, permission controls, and cloud‑based data protection.
This article analyzes three Spark shuffle bottlenecks—oversized partitions that exceed Netty's 2 GB limit, excessive retry latency caused by dead executors, and insufficient data‑corruption checks—and presents concrete configuration changes, new block identifiers, executor‑liveness checks, and CRC‑32 verification to improve fetchability, efficiency, and reliability at scale.
This article chronicles Doug Cutting's journey from his humble beginnings at Stanford through his pioneering work on Lucene, Nutch, and Hadoop, highlighting how his innovations in search and distributed computing reshaped the big data landscape and led to the rise of Cloudera.
This article explores the design of a large‑scale, real‑time sentiment analysis system, detailing the data ingestion, processing, and storage requirements, comparing Lambda and Kappa architectures, and presenting an Alibaba Cloud solution that combines Tablestore and Blink for unified batch‑and‑stream processing.
This article provides a detailed roadmap for autumn campus recruitment, covering the significance of the hiring season, tailored preparation strategies for different skill levels, multiple case studies, extensive interview question collections across Java, JVM, big data, and system fundamentals, as well as practical tips for resume polishing and interview mindset.
From June 4‑5, 2019, the China‑hosted Big Data Industry Summit gathered more than 4,000 attendees and 60,000 online viewers to present award winners, release multiple whitepapers and standards, and hold six thematic forums and two roundtables that examined data platforms, asset management, security, law, and emerging technologies, outlining current opportunities and future challenges for big‑data growth.
This article introduces Kafka, explains its core concepts and design principles, outlines typical use cases, and discusses common operational problems and troubleshooting tips for this high‑throughput distributed messaging system.
This article presents a complete solution for real‑time advertising click counting using Spark Structured Streaming combined with Redis Streams, detailing the business scenario, data flow, input/output formats, and step‑by‑step implementation including data extraction, processing, storage, and query via Spark‑SQL.
Based on data transmission and reliability metrics, this article compares Apache Storm and Apache Flink in stream processing, presenting benchmark designs, test environments, results for synthetic and Kafka data, and offers practical recommendations such as operator chaining, object reuse, and checkpoint strategies to maximize Flink performance.
This article presents a comprehensive overview of Taobao's cloud computing architecture, detailing system components, various data synchronization methods such as TimeTunnel, Dbsync, and DataX, the scheduling system design, and metadata-driven analysis platforms for performance optimization and monitoring.
This article presents an in‑depth interview with Alibaba Cloud senior expert Sun Tingtao, detailing the architecture, core features, design challenges, and operational strategies of the Alibaba Cloud Log Service that handles over 10 PB of daily log data for massive, diverse production workloads.
This article presents a detailed performance benchmark comparing Apache Storm and Apache Flink in stream processing, focusing on data transmission methods, reliability mechanisms, operator chaining, and both self‑generated and Kafka‑sourced workloads, and provides practical optimization recommendations based on the results.
The article presents Tencent's evolution of real‑time stream processing using Flink, the design of the Oceanus one‑stop visual platform, and a series of deep extensions and optimizations—including UI redesign, JobManager failover, checkpoint handling, enhanced windows, LocalKeyBy, idle detection, and log isolation—aimed at supporting petabyte‑scale data workloads.
This article provides a detailed roadmap for candidates preparing for the autumn recruitment season, covering interview experience sharing, systematic learning routes, project preparation, essential Java and big‑data technologies, core algorithms, and practical interview question collections to help readers avoid common pitfalls and succeed in securing offers.
This article explains Spark's executor memory architecture, covering on‑heap and off‑heap memory planning, static and unified memory managers, storage and execution memory allocation, RDD persistence, eviction policies, and shuffle memory usage, providing practical guidance for performance tuning.
The article explains how excessive HBase partitions can cause frequent flushes, compaction storms, high memory usage, long master assignment times, and reduced MapReduce concurrency, and provides formulas and guidelines for calculating a reasonable number of regions per RegionServer.
Meltwater details how it processes millions of daily media posts using a custom‑tuned Elasticsearch 1.7.6 cluster of over 400 nodes on AWS, covering data volume, query complexity, node configuration, indexing strategy, performance optimizations, and lessons learned for large‑scale search deployments.
This article explains the phenomenon, causes, detection methods, and a comprehensive set of solutions—including Hive preprocessing, key filtering, shuffle parallelism, two‑stage aggregation, map‑join, sampling, random prefixing, and combined strategies—to mitigate data skew in Spark jobs and improve performance.
The article presents Alibaba Cloud's real-time computing solution based on Flink and HBase, covering market competition, open‑source ecosystem, containerized architecture on Kubernetes, and typical applications such as online education video analysis, city‑brain traffic management, and fraud detection.
In a DTCC 2019 keynote, Zhao Qun, director of big‑data platform at Percent Point, outlines the challenges of trillion‑scale real‑time analytics and presents a transparent, fine‑grained architecture built on Kafka, Spark Streaming, ClickHouse, HBase, Ceph and Elasticsearch, detailing design principles, component sizing, multi‑center deployment, performance testing and operational safeguards.
This article provides a comprehensive technical guide to Kafka, covering its distributed architecture, producer and consumer workflows, partition and leader management, message delivery semantics, exactly‑once guarantees, transaction handling, file organization, and key configuration parameters.
The article explains how Flink's shuffle pipeline—from upstream data serialization to downstream consumption—is optimized through a credit‑based flow‑control mechanism, zero‑copy network buffers, broadcast serialization reduction, external shuffle service, and a plugin‑based shuffle manager, resulting in significant performance gains for both streaming and batch jobs.
This article demonstrates how to download the Google 1‑gram dataset, load and filter billions of rows with the PyTubes library, compute yearly word frequencies using NumPy, and reproduce the classic Python usage trend chart while discussing performance considerations and future improvements.
This article describes how Didi adopted Apache Flink for its real‑time data streams, detailing the platformized architecture, production use cases such as ETL, monitoring and CEP, the evolution of StreamSQL, and the engineering improvements made to support large‑scale, low‑latency processing.
This article details Meituan's R&D team's systematic PDCA‑based approach to resource cost optimization, covering methodology definition, planning, execution, checking, and iterative improvement across infrastructure, big‑data, and shared services, ultimately saving tens of millions of yuan.
This article explains why Elasticsearch can be slow on massive datasets, then details practical techniques—leveraging filesystem cache, pre‑heating hot data, separating hot and cold indices, designing lean document models, and avoiding deep pagination—to achieve sub‑second query performance at billions‑scale.
This article explains how Apache Flink’s TwoPhaseCommitSinkFunction, introduced in version 1.4, enables end-to-end exactly-once semantics when integrated with Apache Kafka, detailing the checkpoint mechanism and the two-phase commit protocol that ensures reliable data processing.
This article describes the challenges of scaling Qunar's Hadoop NameNode, introduces HDFS Federation and the FastCopy tool, presents performance tests comparing FastCopy with DistCp, and details the development and evaluation of an optimized qFastCopy solution that reduces multi‑petabyte migration time from hours to a few.
This article examines the performance challenges encountered after upgrading a large‑scale HDFS cluster at VIP.com, explains the root causes of NameNode RPC latency, and presents concrete solutions—including delayed block reports, configurable block deletion, federation redesign, client monitoring, temp‑directory sharding, and small‑file handling—along with configuration snippets and real‑world results.
This article presents a comprehensive overview of financial risk management, detailing business challenges such as identity verification and fraud, and describing technical solutions including feature engineering, sample handling, model optimization, and online validation, emphasizing the integration of data-driven AI techniques throughout the process.
This article explains Spark Streaming’s architecture, core concepts such as DStream, windowing, and the two Kafka integration methods—Receiver-based and Direct approaches—detailing their configurations, memory implications, checkpointing, and best‑practice recommendations for reliable, high‑throughput real‑time data processing.
In a QCon 2019 talk, Ant Financial’s deputy CTO Hu Xi outlines the company’s fifteen‑year journey reshaping payments and micro‑loans through blockchain, AI, security, IoT and cloud computing, and details the emerging cloud‑native, high‑availability, data‑intelligent architecture that will underpin the next generation of financial technology.
The article presents a comprehensive overview of Pony.ai's autonomous driving infrastructure, covering the core infrastructure team’s responsibilities, vehicle onboard systems, simulation platform, data architecture, and supporting services, while discussing the technical challenges and engineering practices employed to achieve scalability, reliability, and high performance.
Ant Group’s former CTO Hu Xi outlines the 15‑year evolution of its fintech architecture, highlighting the five BASIC technologies—blockchain, AI, security, IoT, and cloud computing—while detailing the shift to cloud‑native, distributed middleware, OceanBase, service mesh, risk‑auto‑recovery, and open‑intelligent data platforms.
The article reviews Ant Financial’s fifteen‑year journey reshaping payments and micro‑loans through blockchain, AI, security, IoT and cloud computing, explains how distributed middleware, OceanBase, service‑mesh‑based cloud‑native infrastructure and open intelligent computing architectures enable high‑availability, scalable financial services, and introduces the BASIC College talent program.
This article explains how Kafka attains high throughput by using sequential disk writes, memory‑mapped files, sendfile zero‑copy, and batch compression, detailing both write and read path optimizations and their impact on performance.
This article introduces the main business scenarios, system architecture, and core technologies of a time‑series data platform, covering data collection, processing, storage, analysis, and the specific features required for high‑performance, scalable, and reliable time‑series data management.
This article explains why HBase rowkey design is critical for performance, outlines common interview expectations, and provides visual guidelines to help developers create efficient rowkeys for production workloads, including best‑practice tips on key length, salting, and ordering to avoid hotspotting.
Chief Scientist Tang Jian explains how Didi leverages next‑generation AI—big‑data, hybrid‑augmented, autonomous, and collective intelligence—to transform smart mobility through advanced dispatch, safety systems, in‑car perception, traffic‑signal optimization, and global collaborations, while confronting challenges of model scale, computing power, and safety assurance.
EasyScheduler is an open‑source big‑data workflow scheduler that uses a decentralized architecture with Master and Worker nodes coordinated via ZooKeeper, supporting DAG‑based task definitions, various task types, fault tolerance, priority handling, distributed locks, and remote logging, all illustrated with detailed component diagrams.
FlinkSQL's retract updates allow handling of data modifications in streaming queries by using toRetractStream, contrasting with the append-only toAppendStream mode, and this article explains the differences, when each mode applies, and provides illustrative examples and visual diagrams.
To overcome severe performance degradation of Flink sliding windows over very long time ranges, Youzan engineers applied time‑slicing based on the greatest common divisor of window length and slide step, reducing state writes and timers, which yielded 3‑8× speedups in production.
This article introduces HBase, a distributed column‑oriented NoSQL database built on Hadoop, explains its architecture, data model, key concepts such as rowkeys, column families, timestamps, regions, and ZooKeeper, outlines its main features and typical use cases, and provides common HBase shell commands with examples.
This article provides a comprehensive guide to Hive SQL performance tuning, covering optimization goals, common pitfalls, execution flow, table and job settings, map, shuffle, reduce, and query-level improvements such as join, bucket join, group‑by, and count‑distinct optimizations.
The article explains how situational awareness—covering pre‑attack, during‑attack, and post‑attack stages—leverages big data, AI, threat intelligence, UEBA and visualization to turn security platforms into proactive “security brains,” while also critiquing current product implementations and market practices.
This article explains how to perform row-to-column transformations (pivot) in Hive using two methods: a traditional SQL approach mimicking Oracle/SQL Server pivot syntax and a more concise map-based technique, comparing their syntax, performance, and memory considerations.
The Travel Time Index (TTI) quantifies urban congestion by comparing actual travel time to free‑flow conditions, and this study details domestic and international evaluation methods, free‑flow speed estimation, weight calculation, link extraction via PostGIS, system architecture, and validation using massive Didi trajectory data to support city traffic management.
The talk introduces Alibaba’s Fish Brain platform, an AI‑powered decision‑support system for entertainment that combines a three‑layer data‑model, AI‑processed basic data, and application models, leveraging NLP, computer‑vision, custom embeddings, loss functions and predictive hybrid networks to analyze content, user behavior, and forecast performance.
This article provides a comprehensive overview of Hive as a Hadoop‑based data warehouse, explains its architecture, query‑to‑MapReduce translation, high‑availability design, and compares its batch‑oriented processing with Impala's low‑latency SQL engine for big data analytics.
This weekly bulletin summarizes four Hadoop knowledge points—compression formats, MapReduce join techniques, Hive installation, and YARN Capacity Scheduler—while also sharing personal updates about a PhD graduation, the upcoming May Day holiday, and a request for likes and shares.
Didi’s vice‑president and chief scientist of smart transportation, Professor Henry Liu, delivered the “Big Data‑Driven Smart Transportation” lecture—part of the AI Industry Applications course—on China University MOOC, teaching students fundamental concepts, real‑world cases, and future prospects of big‑data and AI in traffic management.
This article reviews a decade of MaxCompute development, covering its origins, core technologies, performance gains, ecosystem integration, intelligent features, competitive positioning, and commercialization, while highlighting the platform's role as Alibaba's central big‑data compute engine.
This tutorial provides a comprehensive, step-by-step procedure for setting up Hive 2.1.0 on a Hadoop 2.7.1 cluster running Ubuntu 14.04, covering environment preparation, Hive installation, configuration of environment variables, MySQL metastore integration, client setup, service startup, and basic verification commands.
This report details Ctrip Financial's end‑to‑end risk control development, covering business overview, a three‑layer data platform, the progression of credit scoring and anti‑fraud models from rule‑based to advanced AI techniques, and the evaluation, monitoring, and social‑network‑based fraud detection strategies employed.
This article explains how to handle a real‑time OLAP monitoring platform processing 10‑12 billion daily events and 400 billion yearly records by optimizing Elasticsearch 5.3.3 through cluster planning, storage strategies, index sharding, compression, hot‑warm architecture, routing, index templates, rollover, and cross‑cluster search, providing concrete configurations and code examples.
This article provides two reusable Java code samples that demonstrate how to perform a map‑side join and a reduce‑side join in Hadoop MapReduce, enabling efficient joining of a large dataset with a smaller reference table.
This article explores practical techniques for handling massive, high‑concurrency data workloads, covering relational database limits, read/write separation, vertical and horizontal sharding, key selection, archival to NoSQL stores, and the use of heterogeneous index tables to maintain performance.
This article reviews Jia Yangqing’s insights on why deep learning finally succeeded—highlighting the roles of big data and high‑performance computing—while examining its current limitations, emerging challenges, and future opportunities across AI engineering, AutoML, and hardware‑software co‑design.
This weekly note shares personal updates and a concise technical overview covering Yarn's resource scheduling, Hadoop's rack‑aware architecture, HDFS data flow, and practical solutions to the HDFS small‑file problem, along with links to further reading and upcoming work plans.
This article introduces Apache Kafka as a distributed streaming platform, explaining its three core capabilities, key concepts such as producers, topics, brokers, partitions and consumers, and detailing how messages are delivered, stored in partitions, and consumed by consumer groups.
This guide introduces Hadoop and HBase fundamentals, explains their architectures and advantages, and provides step‑by‑step instructions for setting up a multi‑node Hadoop cluster, configuring core services, installing HBase, and performing basic HBase shell operations.
This article explains how JD.com leverages the open‑source HBase database for massive, low‑latency data storage across various business lines, detailing its architecture, multi‑tenant isolation, disaster‑recovery mechanisms, and integration with Phoenix SQL for OLTP workloads.
This article answers common Kafka questions, explaining why Kafka cannot operate without Zookeeper, describing its two retention strategies based on time and size, detailing how simultaneous time‑ and size‑based cleanup works, listing performance bottlenecks, and offering practical guidelines for sizing and configuring Kafka clusters.
This article explains how HDFS stores files in replicated data blocks, implements rack awareness to improve reliability and performance, shows the necessary configuration in core-site.xml, provides sample scripts, and demonstrates how to add new DataNode machines without restarting the NameNode.
This article explains why YARN was created to overcome the limitations of MapReduce 1.x, describes its architecture—including ResourceManager, NodeManager, ApplicationMaster, Container, and Client—and outlines the step‑by‑step execution flow that enables multiple computation frameworks to run on Hadoop.
This article reviews the evolution of Youzan's order search architecture over two years, detailing challenges from data growth, the creation of a hot‑state index covering half of search traffic, time‑sharded indexes, and the AKF expansion cube that guides multi‑axis scalability.
This weekly briefing shares five curated resources covering interview reflections, a concise Hadoop introduction, the principles of MapReduce, an overview of HDFS, and upcoming plans to study Hive and HBase, emphasizing the distributed nature of big‑data processing.
