Modern Big Data Processing with Hadoop :: Expert techniques for architecting end-to-end big data solutions to get valuable insights /
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...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 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. |
| Beschreibung: | 1 online resource (394 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781787128811 1787128814 178712276X 9781787122765 |
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| 245 | 1 | 0 | |a Modern Big Data Processing with Hadoop : |b Expert techniques for architecting end-to-end big data solutions to get valuable insights / |c V Naresh Kumar, Prashant Shindgikar. |
| 264 | 1 | |a Birmingham : |b Packt Publishing, |c 2018. | |
| 300 | |a 1 online resource (394 pages) | ||
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| 500 | |a 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. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 504 | |a Includes bibliographical references. | ||
| 630 | 0 | 0 | |a Apache Hadoop. |0 http://id.loc.gov/authorities/names/n2013024279 |
| 630 | 0 | 7 | |a Apache Hadoop |2 fast |
| 650 | 0 | |a Electronic data processing |x Distributed processing. |0 http://id.loc.gov/authorities/subjects/sh85042293 | |
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| 776 | 0 | 8 | |i Print version: |a Kumar, V Naresh. |t Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights. |d Birmingham : Packt Publishing, ©2018 |
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| contents | 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. |
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| id | ZDB-4-EBA-on1032152617 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:28:19Z |
| institution | BVB |
| isbn | 9781787128811 1787128814 178712276X 9781787122765 |
| language | English |
| oclc_num | 1032152617 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (394 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing, |
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| spelling | Kumar, V. Naresh, author. Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / V Naresh Kumar, Prashant Shindgikar. Birmingham : Packt Publishing, 2018. 1 online resource (394 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. 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. 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. Includes bibliographical references. Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Traitement réparti. Database design & theory. bicssc Data mining. bicssc Information architecture. bicssc Data capture & analysis. bicssc Computers Database Management Data Mining. bisacsh Computers Data Modeling & Design. bisacsh Computers Data Processing. bisacsh COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Electronic data processing Distributed processing fast Shindgikar, Prashant, author. has work: Modern big data processing with Hadoop (Text) https://id.oclc.org/worldcat/entity/E39PCYVtfqwyTgQvFtBVQWhbVC https://id.oclc.org/worldcat/ontology/hasWork 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 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1789468 Volltext LDL ebooks ONIX to marcxml transformation using Record_Load-eBooks_Legal_Deposit_onix2marc_v2-1.xsl 20180412 9781787128811 Uk. |
| spellingShingle | Kumar, V. Naresh Shindgikar, Prashant Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / 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. Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Traitement réparti. Database design & theory. bicssc Data mining. bicssc Information architecture. bicssc Data capture & analysis. bicssc Computers Database Management Data Mining. bisacsh Computers Data Modeling & Design. bisacsh Computers Data Processing. bisacsh COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Electronic data processing Distributed processing fast |
| subject_GND | http://id.loc.gov/authorities/names/n2013024279 http://id.loc.gov/authorities/subjects/sh85042293 |
| title | Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / |
| title_auth | Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / |
| title_exact_search | Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / |
| title_full | Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / V Naresh Kumar, Prashant Shindgikar. |
| title_fullStr | Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / V Naresh Kumar, Prashant Shindgikar. |
| title_full_unstemmed | Modern Big Data Processing with Hadoop : Expert techniques for architecting end-to-end big data solutions to get valuable insights / V Naresh Kumar, Prashant Shindgikar. |
| title_short | Modern Big Data Processing with Hadoop : |
| title_sort | modern big data processing with hadoop expert techniques for architecting end to end big data solutions to get valuable insights |
| title_sub | Expert techniques for architecting end-to-end big data solutions to get valuable insights / |
| topic | Apache Hadoop. http://id.loc.gov/authorities/names/n2013024279 Apache Hadoop fast Electronic data processing Distributed processing. http://id.loc.gov/authorities/subjects/sh85042293 Traitement réparti. Database design & theory. bicssc Data mining. bicssc Information architecture. bicssc Data capture & analysis. bicssc Computers Database Management Data Mining. bisacsh Computers Data Modeling & Design. bisacsh Computers Data Processing. bisacsh COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Electronic data processing Distributed processing fast |
| topic_facet | Apache Hadoop. Apache Hadoop Electronic data processing Distributed processing. Traitement réparti. Database design & theory. Data mining. Information architecture. Data capture & analysis. Computers Database Management Data Mining. Computers Data Modeling & Design. Computers Data Processing. COMPUTERS Computer Literacy. COMPUTERS Computer Science. COMPUTERS Hardware General. COMPUTERS Information Technology. COMPUTERS Machine Theory. COMPUTERS Reference. Electronic data processing Distributed processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1789468 |
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