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The Ultimate Hands-On Hadoop · LearnSpace
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courseraIT и технологии

The Ultimate Hands-On Hadoop

Курс от Packt
Средний≈ 20.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Updated in May 2025. This course now features Coursera Coach — your interactive learning companion that helps you test your knowledge, challenge assumptions, and deepen your understanding as you progress. Build a strong, hands-on foundation in Hadoop and big data processing with this comprehensive course designed for data engineers, developers, and IT professionals. From installation to advanced analytics, you’ll learn how to work confidently with Hadoop’s ecosystem and design scalable solutions for real-world data challenges. You’ll begin by installing the Hortonworks Data Platform (HDP) Sandbox on your local machine, giving you an isolated environment to explore Hadoop’s core components. Through guided exercises, you’ll work with the Hadoop Distributed File System (HDFS) and build your understanding of MapReduce, learning how large-scale distributed processing works behind the scenes. As you progress, you’ll move into advanced Hadoop programming with Pig, Hive, and Spark. You’ll write complex queries, analyze large datasets, and work with real-world data to build scalable data workflows. You’ll also explore machine learning with Spark MLLib, giving you a practical introduction to distributed ML techniques. In the final modules, you’ll learn how to manage and optimize Hadoop clusters using YARN, ZooKeeper, Oozie, and Kafka. You’ll practice feeding data into your cluster, orchestrating workflows, managing resources, and analyzing streaming data in real time — essential skills for production-grade environments. By the end of this course, you will have: - Installed and configured the Hortonworks Sandbox for Hadoop development. - Worked with HDFS, MapReduce, and Hadoop’s core data processing concepts. - Written queries and pipelines using Pig, Hive, and Spark. - Performed distributed machine learning with Spark MLLib. - Integrated relational and non-relational data sources with Hadoop. - Managed clusters and streaming workflows with YARN, ZooKeeper, Oozie, and Kafka. - Gained the confidence to design and implement Hadoop-based data solutions. This course is ideal for data engineers, developers, and IT professionals with basic programming or data management experience. Familiarity with Java, SQL, or the Linux command line is helpful but not required.

Навыки, которые вы освоите

Apache KafkaSystem Design and ImplementationData Import/ExportAWS KinesisReal Time DataMongoDBNoSQLApplied Machine LearningSystems DesignScalabilityApache HiveApache SparkData PipelinesQuery LanguagesApache HadoopSQLBig DataData IntegrationDistributed ComputingData Processing

Программа курса

12 модулей · 112 учебных материалов

01Learning All the Buzzwords and Installing the Hortonworks Data Platform Sandbox6 материалов

Learning All the Buzzwords and Installing the Hortonworks Data Platform Sandbox

Introduction to the Course 'The Ultimate Hands-On Hadoop'ЧтениеIntroduction and Installation of HadoopВидеоFull Course ResourcesЧтениеThe Hortonworks and Cloudera Merger and its Effects on the CourseВидео

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Преподаватель курса

The Ultimate Hands-On Hadoop
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Обучение на Coursera

≈ 20.3 ч

12 модулей

Язык: Английский

Субтитры: Казахский

Часть программы вашего университета
Hadoop Overview and HistoryВидео
Overview of the Hadoop EcosystemВидео
02Using the Hadoop's Core: Hadoop Distributed File System (HDFS) and MapReduce11 материалов

Using the Hadoop's Core: Hadoop Distributed File System (HDFS) and MapReduce

Hadoop Distributed File System (HDFS): What it is and How it WorksВидеоInstalling the MovieLens DatasetВидеоActivity - Installing the MovieLens Dataset into Hadoop's Distributed File System (HDFS) using the Command LineВидеоMapReduce: What it is and How it WorksВидеоHow MapReduce Distributes ProcessingВидеоMapReduce Example: Breaking Down the Movie Ratings by Rating ScoreВидеоActivity - Installing Python, MRJob, and NanoВидеоActivity - Coding Up and Running the Ratings Histogram MapReduce JobВидеоExercise - Ranking Movies by Their PopularityВидеоActivity - Checking ResultsВидеоExploring HDFS Core Concepts and ArchitectureDIALOGUE
03Programming Hadoop with Pig9 материалов

Programming Hadoop with Pig

Introducing AmbariВидеоIntroducing the PigВидеоExample - Finding the Oldest Movie with Five-Star Rating Using the PigВидеоActivity - Finding the Old Five-Star Movies with PigВидеоMore Pig LatinВидеоExercise - Finding the Most-Rated One-Star MovieВидеоPig Challenge - Comparing ResultsВидеоGetting to Know Apache AmbariDIALOGUEAssessment 1Задание
04Programming Hadoop with Spark9 материалов

Programming Hadoop with Spark

Why Spark?ВидеоThe Resilient Distributed Datasets (RDD)ВидеоActivity - Finding the Movie with the Lowest Average Rating with the Resilient Distributed Datasets (RDD)ВидеоDatasets and Spark 2.0ВидеоActivity - Finding the movie with the Lowest Average Rating with DataFramesВидеоActivity - Recommending a Movie with Spark's Machine Learning Library (MLLib)ВидеоExercise - Filtering the Lowest-Rated Movies by Number of RatingsВидеоActivity - Checking ResultsВидеоUnderstanding Apache Spark RDDs and TransformationsDIALOGUE
05Using Relational Datastores with Hadoop10 материалов

Using Relational Datastores with Hadoop

What is Hive?ВидеоActivity - Using Hive to Find the Most Popular MovieВидеоHow Hive Works?ВидеоExercise - Using Hive to Find the Movie with the Highest Average RatingВидеоComparing SolutionsВидеоIntegrating MySQL with HadoopВидеоActivity - Installing MySQL and Importing Movie DataВидеоActivity - Using Sqoop to Import Data from MySQL to HFDS/HiveВидеоActivity - Using Sqoop to Export Data from Hadoop to MySQLВидеоIntegrating Hive with Hadoop for SQL QueriesDIALOGUE
06Using Non-Relational Data Stores with Hadoop14 материалов

Using Non-Relational Data Stores with Hadoop

Why NoSQL?ВидеоWhat is HBase?ВидеоActivity - Importing Movie Ratings into HBaseВидеоActivity - Using HBase with Pig to Import Data at ScaleВидеоCassandra - OverviewВидеоActivity - Installing CassandraВидеоActivity - Writing Spark Output into CassandraВидеоMongoDB - OverviewВидеоActivity - Installing MongoDB and Integrating Spark with MongoDBВидеоActivity - Using the MongoDB ShellВидеоChoosing Database TechnologyВидеоExercise - Choosing a Database for a Given ProblemВидеоUnderstanding NoSQL with MongoDBDIALOGUEAssessment 2Задание
07Querying Data Interactively10 материалов

Querying Data Interactively

Overview of DrillВидеоActivity - Setting Up DrillВидеоActivity - Querying Across Multiple Databases with DrillВидеоOverview of PhoenixВидеоActivity - Installing Phoenix and Querying HBaseВидеоActivity - Integrating Phoenix with the PigВидеоOverview of PrestoВидеоActivity - Installing Presto and Querying HiveВидеоActivity - Querying Both Cassandra and Hive Using PrestoВидеоExploring Hadoop Query Engines: Apache Drill, Phoenix, and PrestoDIALOGUE
08Managing Your Cluster14 материалов

Managing Your Cluster

Yet Another Resource Negotiator (YARN)ВидеоTezВидеоActivity - Using Hive on Tez and Measuring the Performance BenefitВидеоMesosВидеоZooKeeperВидеоActivity - Simulating a Failing Master with ZooKeeperВидеоOozieВидеоActivity - Setting Up a Simple Oozie WorkflowВидеоZeppelin - OverviewВидеоHands-On with Zeppelin for Spark and MovieLens AnalysisВидеоSQL and Data Visualization in Zeppelin: MovieLens Analysis with SparkВидеоHue - OverviewВидеоOther Technologies Worth MentioningВидеоUnderstanding Apache YARNDIALOGUE
09Feeding Data to Your Cluster8 материалов

Feeding Data to Your Cluster

KafkaВидеоActivity - Setting Up Kafka and Publishing DataВидеоActivity - Publishing Web Logs with KafkaВидеоFlumeВидеоActivity - Setting up Flume and Publishing LogsВидеоActivity - Setting Up Flume to Monitor a Directory and Store its Data in Hadoop Distributed File System (HDFS)ВидеоPublishing Data to a Big Data Cluster with KafkaDIALOGUEAssessment 3Задание
10Analyzing Streams of Data9 материалов

Analyzing Streams of Data

Spark Streaming: IntroductionВидеоActivity - Analyzing Web Logs Published with Flume using Spark StreamingВидеоExercise - Monitor Flume-Published Logs for Errors in Real TimeВидеоExercise Solution: Aggregating the Hypertext Transfer Protocol (HTTP) Access Codes with Spark StreamingВидеоApache Storm: IntroductionВидеоActivity - Counting Words with StormВидеоFlink: OverviewВидеоActivity - Counting Words with FlinkВидеоReal-Time Data Processing with Spark StreamingDIALOGUE
11Designing Real-World Systems9 материалов

Designing Real-World Systems

The Best of the RestВидеоReview: How the Pieces Fit Together?ВидеоUnderstanding Your RequirementsВидеоSample Application: Consuming Web Server Logs and Keeping Track of Top-SellersВидеоSample Application: Serving Movie Recommendations to a WebsiteВидеоExercise - Designing a System to Report Web Sessions Per DayВидеоExercise Solution: Designing a System to Count Daily SessionsВидеоNavigating the Distributed Computing LandscapeDIALOGUEAssessment 4Задание
12Learning More3 материалов

Learning More

Conclusion to the Course 'The Ultimate Hands-On Hadoop'ЧтениеBooks and Online ResourcesВидеоFull Course AssessmentЗадание