Data-intensive systems : principles and fundamentals using Hadoop and Spark /: principles and fundamentals using Hadoop and Spark. ([2019])
- Record Type:
- Book
- Title:
- Data-intensive systems : principles and fundamentals using Hadoop and Spark /: principles and fundamentals using Hadoop and Spark. ([2019])
- Main Title:
- Data-intensive systems : principles and fundamentals using Hadoop and Spark
- Further Information:
- Note: Tomasz Wiktorski.
- Authors:
- Wiktorski, Tomasz
- Contents:
- Intro; Contents; List of Figures; List of Listings; 1 Preface; 1.1 Conventions Used in this Book; 1.2 Listed Code; 1.3 Terminology; 1.4 Examples and Exercises; 2 Introduction; 2.1 Growing Datasets; 2.2 Hardware Trends; 2.3 The V's of Big Data; 2.4 NOSQL; 2.5 Data as the Fourth Paradigm of Science; 2.6 Example Applications; 2.6.1 Data Hub; 2.6.2 Search and Recommendations; 2.6.3 Retail Optimization; 2.6.4 Healthcare; 2.6.5 Internet of Things; 2.7 Main Tools; 2.7.1 Hadoop; 2.7.2 Spark; 2.8 Exercises; References; 3 Hadoop 101 and Reference Scenario; 3.1 Reference Scenario; 3.2 Hadoop Setup 3.3 Analyzing Unstructured Data3.4 Analyzing Structured Data; 3.5 Exercises; 4 Functional Abstraction; 4.1 Functional Programming Overview; 4.2 Functional Abstraction for Data Processing; 4.3 Functional Abstraction and Parallelism; 4.4 Lambda Architecture; 4.5 Exercises; Reference; 5 Introduction to MapReduce; 5.1 Reference Code; 5.2 Map Phase; 5.3 Combine Phase; 5.4 Shuffle Phase; 5.5 Reduce Phase; 5.6 Embarrassingly Parallel Problems; 5.7 Running MapReduce Programs; 5.8 Exercises; 6 Hadoop Architecture; 6.1 Architecture Overview; 6.2 Data Handling; 6.2.1 HDFS Architecture; 6.2.2 Read Flow 6.2.3 Write Flow6.2.4 HDFS Failovers; 6.3 Job Handling; 6.3.1 Job Flow; 6.3.2 Data Locality; 6.3.3 Job and Task Failures; 6.4 Exercises; 7 MapReduce Algorithms and Patterns; 7.1 Counting, Summing, and Averaging; 7.2 Search Assist; 7.3 Random Sampling; 7.4 Multiline Input; 7.5 Inverted Index; 7.6 Exercises;Intro; Contents; List of Figures; List of Listings; 1 Preface; 1.1 Conventions Used in this Book; 1.2 Listed Code; 1.3 Terminology; 1.4 Examples and Exercises; 2 Introduction; 2.1 Growing Datasets; 2.2 Hardware Trends; 2.3 The V's of Big Data; 2.4 NOSQL; 2.5 Data as the Fourth Paradigm of Science; 2.6 Example Applications; 2.6.1 Data Hub; 2.6.2 Search and Recommendations; 2.6.3 Retail Optimization; 2.6.4 Healthcare; 2.6.5 Internet of Things; 2.7 Main Tools; 2.7.1 Hadoop; 2.7.2 Spark; 2.8 Exercises; References; 3 Hadoop 101 and Reference Scenario; 3.1 Reference Scenario; 3.2 Hadoop Setup 3.3 Analyzing Unstructured Data3.4 Analyzing Structured Data; 3.5 Exercises; 4 Functional Abstraction; 4.1 Functional Programming Overview; 4.2 Functional Abstraction for Data Processing; 4.3 Functional Abstraction and Parallelism; 4.4 Lambda Architecture; 4.5 Exercises; Reference; 5 Introduction to MapReduce; 5.1 Reference Code; 5.2 Map Phase; 5.3 Combine Phase; 5.4 Shuffle Phase; 5.5 Reduce Phase; 5.6 Embarrassingly Parallel Problems; 5.7 Running MapReduce Programs; 5.8 Exercises; 6 Hadoop Architecture; 6.1 Architecture Overview; 6.2 Data Handling; 6.2.1 HDFS Architecture; 6.2.2 Read Flow 6.2.3 Write Flow6.2.4 HDFS Failovers; 6.3 Job Handling; 6.3.1 Job Flow; 6.3.2 Data Locality; 6.3.3 Job and Task Failures; 6.4 Exercises; 7 MapReduce Algorithms and Patterns; 7.1 Counting, Summing, and Averaging; 7.2 Search Assist; 7.3 Random Sampling; 7.4 Multiline Input; 7.5 Inverted Index; 7.6 Exercises; References; 8 NOSQL Databases; 8.1 NOSQL Overview and Examples; 8.1.1 CAP and PACELC Theorem; 8.2 HBase Overview; 8.3 Data Model; 8.4 Architecture; 8.4.1 Regions; 8.4.2 HFile, HLog, and Memstore; 8.4.3 Region Server Failover; 8.5 MapReduce and HBase; 8.5.1 Loading Data 8.5.2 Running Queries8.6 Exercises; References; 9 Spark; 9.1 Motivation; 9.2 Data Model; 9.2.1 Resilient Distributed Datasets and DataFrames; 9.2.2 Other Data Structures; 9.3 Programming Model; 9.3.1 Data Ingestion; 9.3.2 Basic Actions-Count, Take, and Collect; 9.3.3 Basic Transformations-Filter, Map, and reduceByKey; 9.3.4 Other Operations-flatMap and Reduce; 9.4 Architecture; 9.5 SparkSQL; 9.6 Exercises … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2019
- Extent:
- 1 online resource
- Subjects:
- 005.74
Databases
Big data
Big data
Databases
Electronic books
Electronic books - Languages:
- English
- ISBNs:
- 9783030046033
3030046036 - Related ISBNs:
- 9783030046026
3030046028 - Notes:
- Note: Includes bibliographical references.
Note: Description based on online resource; title from digital title page (viewed on February 14, 2019). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.382160
- Ingest File:
- 02_369.xml