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LEADER 00000cam a2200805 i 4500
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007 cr cnu---unuuu
008 180702s2018 cau ob 000 0 eng d
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019 1047669376|a1055400060|a1066575257|a1081290112|a1082143752
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020 9781484235225|q(electronic bk.)
020 1484235223|q(electronic bk.)
020 1484235215
020 9781484235218
024 7 10.1007/978-1-4842-3522-5|2doi
029 1 AU@|b000063679146
029 1 AU@|b000067503457
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037 com.springer.onix.9781484235225|bSpringer Nature
040 N$T|beng|erda|epn|cN$T|dN$T|dEBLCP|dGW5XE|dUAB|dUPM|dOCLCF
|dOCLCQ|dVT2|dWYU|dOTZ|dLVT|dUKMGB|dU3W|dUMI|dG3B|dCAUOI
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049 INap
082 04 004.36
082 04 004.36|223
099 eBook O'Reilly for Public Libraries
100 1 Gupta, Saurabh,|eauthor.
245 10 Practical Enterprise Data Lake Insights :|bhandle data-
driven challenges in an Enterprise Big Data Lake /
|cSaurabh Gupta, Venkata Giri.|h[O'Reilly electronic
resource]
264 1 [Berkeley, CA] :|bApress,|c2018.
300 1 online resource
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file
347 |bPDF
504 Includes bibliographical references.
505 0 Intro; Table of Contents; About the Authors; About the
Technical Reviewer; Acknowledgments; Foreword; Chapter 1:
Introduction to Enterprise Data Lakes; Data explosion: the
beginning; Big data ecosystem; Hadoop and MapReduce --
Early days; Evolution of Hadoop; History of Data Lake;
Data Lake: the concept; Data lake architecture; Why Data
Lake?; Data Lake Characteristics; Data lake vs. Data
warehouse; How to achieve success with Data Lake?; Data
governance and data operations; Data democratization with
data lake; Fast Data -- Life beyond Big Data; Conclusion.
505 8 Chapter 2: Data lake ingestion strategiesWhat is data
ingestion?; Understand the data sources; Structured vs.
Semi-structured vs. Unstructured data; Data ingestion
framework parameters; ETL vs. ELT; Big Data Integration
with Data Lake; Hadoop Distributed File System (HDFS);
Copy files directly into HDFS; Batched data ingestion;
Challenges and design considerations; Design
considerations; Commercial ETL tools; Real-time ingestion;
CDC design considerations; Example of CDC pipeline:
Databus, LinkedIn's open-source solution; Apache Sqoop;
Sqoop 1; Sqoop 2; How Sqoop works?
505 8 Sqoop design considerationsNative ingestion utilities;
Oracle copyToBDA; Greenplum gphdfs utility; Data transfer
from Greenplum to using gpfdist; Ingest unstructured data
into Hadoop; Apache Flume; Tiered architecture for
convergent flow of events; Features and design
considerations; Conclusion; Chapter 3: Capture Streaming
Data with Change-Data-Capture; Change Data Capture
Concepts; Strategies for Data Capture; Retention and
Replay; Retention Period; Types of CDC; Incremental; Bulk;
Hybrid; CDC -- Trade-offs; CDC Tools; Challenges;
Downstream Propagation; Use Case.
505 8 Centralization of Change DataAnalyzing a Centralized Data
Store; Metadata: Data about Data; Structure of Data;
Privacy/Sensitivity Information; Special Fields; Data
Formats; Delimited Format; Avro File Format; Consumption
and Checkpointing; Simple Checkpoint Mechanism;
Parallelism; Merging and Consolidation; Design
Considerations for Merge and Consolidate; Data Quality;
Challenges; Design Aspects; Operational Aspects;
Publishing to Kafka; Schema and Data; Sample Schema;
Schema Repository; Multiple Topics and Partitioning;
Sizing and Scaling; Tools; Conclusion.
505 8 Chapter 4: Data Processing Strategies in Data
LakesMapReduce Processing Framework; Motivation: Why
MapReduce?; MapReduce V1 Refresher and Design
Considerations; Yet Another Resource Negotiator -- YARN;
YARN concepts; Hive; Hive -- Quick Refresher; Hive
Components; Hive Metastore (a.k.a. HCatalog); Hive --
Design Considerations; Hive LLAP; Apache Pig; Pig
Execution Architecture; Apache Spark; Why Spark?;
Resilient Distributed Datasets (RDD); RDD Runtime
Components; RDD Composition; Datasets and DataFrames;
Bucketing, Sorting, and Partitioning; Deployment Modes of
Spark Application.
520 Use this practical guide to successfully handle the
challenges encountered when designing an enterprise data
lake and learn industry best practices to resolve issues.
When designing an enterprise data lake you often hit a
roadblock when you must leave the comfort of the
relational world and learn the nuances of handling non-
relational data. Starting from sourcing data into the
Hadoop ecosystem, you will go through stages that can
bring up tough questions such as data processing, data
querying, and security. Concepts such as change data
capture and data streaming are covered. The book takes an
end-to-end solution approach in a data lake environment
that includes data security, high availability, data
processing, data streaming, and more. Each chapter
includes application of a concept, code snippets, and use
case demonstrations to provide you with a practical
approach. You will learn the concept, scope, application,
and starting point. What You'll Learn: Get to know data
lake architecture and design principles Implement data
capture and streaming strategies Implement data processing
strategies in Hadoop Understand the data lake security
framework and availability model.
588 0 Online resource; title from PDF title page (EBSCO, viewed
July 5, 2018).
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Electronic data processing|xDistributed processing
|xManagement.
650 0 Big data.
650 0 Information storage and retrieval systems.
650 2 Information Systems
650 6 Données volumineuses.
650 6 Systèmes d'information.
650 7 Information technology: general issues.|2bicssc
650 7 Business mathematics & systems.|2bicssc
650 7 Databases.|2bicssc
650 7 Big data|2fast
650 7 Electronic data processing|xDistributed processing
|xManagement|2fast
650 7 Information storage and retrieval systems|2fast
700 1 Giri, Venkata,|eauthor.
776 08 |iPrinted edition:|z9781484235218
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781484235225/?ar
|zAvailable on O'Reilly for Public Libraries
938 YBP Library Services|bYANK|n15575404
938 Askews and Holts Library Services|bASKH|nAH35093466
938 ProQuest Ebook Central|bEBLB|nEBL5438674
938 EBSCOhost|bEBSC|n1840106
994 92|bJFN