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Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights [electronic resource]
Kumar, V. Naresh
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Modern Big Data Processing with Hadoop [electronic resource]: Expert techniques for architecting end-to-end big data solutions to get valuable insights/ V Naresh Kumar, Prashant Shindgikar. |
| 개인저자 | Kumar, V. Naresh,author, Shindgikar, Prashant,author, |
| 발행사항 | Birmingham: Packt Publishing, 2018. |
| 형태사항 | 1 online resource (394 pages). |
| 기타형태 저록 | Print version: Kumar, V Naresh. Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights. Birmingham : Packt Publishing, 2018 |
| ISBN | 9781787128811 1787128814 |
| 기타표준부호 | 9781787122765 |
| 일반주기 |
Table of ContentsHadoop Design Consideration Hadoop Life Cycle ManagementData Modeling in HadoopDesigning Streaming Data PipelinesBuilding Enterprise Search Platform Data Movement TechniquesEnterprise Data Architecture PrinciplesArchitecting Large Scale Data Processing Solutions using Spark Developing Application using Cloud InfrastructureDesigning Data Visualization Solutions Production Hadoop Administration and Cluster Deployment.
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| 내용주기 | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Enterprise Data Architecture Principles; Data architecture principles; Volume; Velocity; Variety; Veracity; The importance of metadata; Data governance; Fundamentals of data governance; Data security; Application security; Input data; Big data security; RDBMS security; BI security; Physical security; Data encryption; Secure key management; Data as a Service; Evolution data architecture with Hadoop; Hierarchical database architecture; Network database architecture. Relational database architectureEmployees; Devices; Department; Department and employee mapping table; Hadoop data architecture; Data layer; Data management layer; Job execution layer; Summary; Chapter 2: Hadoop Life Cycle Management; Data wrangling; Data acquisition; Data structure analysis; Information extraction; Unwanted data removal; Data transformation; Data standardization; Data masking; Substitution; Static ; Dynamic; Encryption; Hashing; Hiding; Erasing; Truncation; Variance; Shuffling; Data security; What is Apache Ranger?; Apache Ranger installation using Ambari; Ambari admin UI. Add serviceService placement; Service client placement; Database creation on master; Ranger database configuration; Configuration changes; Configuration review; Deployment progress; Application restart; Apache Ranger user guide; Login to UI; Access manager; Service details; Policy definition and auditing for HDFS; Summary; Chapter 3: Hadoop Design Consideration; Understanding data structure principles; Installing Hadoop cluster; Configuring Hadoop on NameNode; Format NameNode; Start all services; Exploring HDFS architecture; Defining NameNode; Secondary NameNode; NameNode safe mode; DataNode. Data replicationRack awareness; HDFS WebUI; Introducing YARN; YARN architecture; Resource manager; Node manager; Configuration of YARN; Configuring HDFS high availability; During Hadoop 1.x; During Hadoop 2.x and onwards; HDFS HA cluster using NFS; Important architecture points; Configuration of HA NameNodes with shared storage; HDFS HA cluster using the quorum journal manager; Important architecture points; Configuration of HA NameNodes with QJM; Automatic failover; Important architecture points; Configuring automatic failover; Hadoop cluster composition; Typical Hadoop cluster. Best practices Hadoop deploymentHadoop file formats; Text/CSV file; JSON; Sequence file; Avro; Parquet; ORC; Which file format is better?; Summary; Chapter 4: Data Movement Techniques; Batch processing versus real-time processing; Batch processing; Real-time processing; Apache Sqoop; Sqoop Import; Import into HDFS; Import a MySQL table into an HBase table; Sqoop export; Flume; Apache Flume architecture; Data flow using Flume; Flume complex data flow architecture; Flume setup; Log aggregation use case; Apache NiFi; Main concepts of Apache NiFi; Apache NiFi architecture; Key features. |
| 요약 | This book presents unique techniques to conquer different Big Data processing and analytics challenges using Hadoop. Practical examples are provided to boost your understanding of Big Data concepts and their implementation. By the end of the book, you will have all the knowledge and skills you need to become a true Big Data expert. |
| 주제명 (통일서명) | Apache Hadoop. Apache Hadoop. --fast |
| 일반주제명 | Electronic data processing --Distributed processing. Computers --Database Management --Data Mining. Computers --Data Modeling & Design. Database design & theory. Data mining. Information architecture. Computers --Data Processing. Data capture & analysis. Electronic data processing --Distributed processing. COMPUTERS / Computer Literacy COMPUTERS / Computer Science COMPUTERS / Data Processing COMPUTERS / Hardware / General COMPUTERS / Information Technology COMPUTERS / Machine Theory COMPUTERS / Reference |
| 언어 | 영어 |
| 바로가기 | URL |
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