LEADER 00000cam a2200601Ii 4500 
001    on1019128795 
003    OCoLC 
005    20200717185543.1 
006    m     o  d         
007    cr unu|||||||| 
008    180111s2017    enka    o     000 0 eng d 
015    GBB820007|2bnb 
016 7  018649654|2Uk 
020    1788294114 
020    1788296400 
020    9781788296403 
020    9781788294119|q(electronic book) 
035    (OCoLC)1019128795 
037    CL0500000927|bSafari Books Online 
040    UMI|beng|erda|epn|cUMI|dIDEBK|dTOH|dNLE|dSTF|dCEF|dOCLCF
       |dKSU|dDEBBG|dUKMGB|dG3B|dLVT|dS9I|dUAB|dUKAHL|dN$T 
049    RIDW 
050  4 QA76.9.D343 
082 04 005.1|223 
090    QA76.9.D343 
100 1  Weise, Thomas,|eauthor. 
245 10 Learning Apache Apex :|bReal-time streaming applications 
       with Apex /|cThomas Weise, Munagala V. Ramanath, David Yan,
       Kenneth Knowles. 
264  1 Birmingham, UK :|bPackt Publishing,|c2017. 
300    1 online resource (1 volume) :|billustrations 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    data file|2rda 
505 0  Cover -- Title Page -- Copyright -- Credits -- About the 
       Authors -- About the Reviewer -- www.PacktPub.com -- 
       Customer Feedback -- Table of Contents -- Preface -- 
       Chapter 1: Introduction to Apex -- Unbounded data and 
       continuous processing -- Stream processing -- Stream 
       processing systems -- What is Apex and why is it 
       important? -- Use cases and case studies -- Real-time 
       insights for Advertising Tech (PubMatic) -- Industrial IoT
       applications (GE) -- Real-time threat detection (Capital 
       One) -- Silver Spring Networks (SSN) -- Application Model 
       and API -- Directed Acyclic Graph (DAG) -- Apex DAG Java 
       API -- High-level Stream Java API -- SQL -- JSON -- 
       Windowing and time -- Value proposition of Apex -- Low 
       latency and stateful processing -- Native streaming versus
       micro-batch -- Performance -- Where Apex excels -- Where 
       Apex is not suitable -- Summary -- Chapter 2: Getting 
       Started with Application Development -- Development 
       process and methodology -- Setting up the development 
       environment -- Creating a new Maven project -- Application
       specifications -- Custom operator development -- The Apex 
       operator model -- CheckpointListener/
       CheckpointNotificationListener -- ActivationListener -- 
       IdleTimeHandler -- Application configuration -- Testing in
       the IDE -- Writing the integration test -- Running the 
       application on YARN -- Execution layer components -- 
       Installing Apex Docker sandbox -- Running the application 
       -- Working on the cluster -- YARN web UI -- Apex CLI -- 
       Logging -- Dynamically adjusting logging levels -- Summary
       -- Chapter 3: The Apex Library -- An overview of the 
       library -- Integrations -- Apache Kafka -- Kafka input -- 
       Kafka output -- Other streaming integrations -- JMS 
       (ActiveMQ, SQS, and so on) -- Kinesis streams -- Files -- 
       File input -- File splitter and block reader -- File 
       writer -- Databases -- JDBC input -- JDBC output -- Other 
       databases. 
505 8  Transformations -- Parser -- Filter -- Enrichment -- Map 
       transform -- Custom functions -- Windowed transformations 
       -- Windowing -- Global Window -- Time Windows -- Sliding 
       Time Windows -- Session Windows -- Window propagation -- 
       State -- Accumulation -- Accumulation Mode -- State 
       storage -- Watermarks -- Allowed lateness -- Triggering --
       Merging of streams -- The windowing example -- Dedup -- 
       Join -- State Management -- Summary -- Chapter 4: 
       Scalability, Low Latency, and Performance -- Partitioning 
       and how it works -- Elasticity -- Partitioning toolkit -- 
       Configuring and triggering partitioning -- StreamCodec -- 
       Unifier -- Custom dynamic partitioning -- Performance 
       optimizations -- Affinity and anti-affinity -- Low-latency
       versus throughput -- Sample application for dynamic 
       partitioning -- Performance -- other aspects for custom 
       operators -- Summary -- Chapter 5: Fault Tolerance and 
       Reliability -- Distributed systems need to be resilient --
       Fault-tolerance components and mechanism in Apex -- 
       Checkpointing -- When to checkpoint -- How to checkpoint -
       - What to checkpoint -- Incremental state saving -- 
       Incremental recovery -- Processing guarantees -- Example -
       - exactly-once counting -- The exactly-once output to JDBC
       -- Summary -- Chapter 6: Example Project -- Real-Time 
       Aggregation and Visualization -- Streaming ETL and beyond 
       -- The application pattern in a real-world use case -- 
       Analyzing Twitter feed -- Top Hashtags -- TweetStats -- 
       Running the application -- Configuring Twitter API access 
       -- Enabling WebSocket output -- The Pub/Sub server -- 
       Grafana visualization -- Installing Grafana -- Installing 
       Grafana Simple JSON Datasource -- The Grafana Pub/Sub 
       adapter server -- Setting up the dashboard -- Summary -- 
       Chapter 7: Example Project -- Real-Time Ride Service Data 
       Processing -- The goal -- Datasource -- The pipeline. 
505 8  Simulation of a real-time feed using historical data -- 
       Parsing the data -- Looking up of the zip code and 
       preparing for the windowing operation -- Windowed operator
       configuration -- Serving the data with WebSocket -- 
       Running the application -- Running the application on GCP 
       Dataproc -- Summary -- Chapter 8: Example Project -- ETL 
       Using SQL -- The application pipeline -- Building and 
       running the application -- Application configuration -- 
       The application code -- Partitioning -- Application 
       testing -- Understanding application logs -- Calcite 
       integration -- Summary -- Chapter 9: Introduction to 
       Apache Beam -- Introduction to Apache Beam -- Beam 
       concepts -- Pipelines, PTransforms, and PCollections -- 
       ParDo -- elementwise computation -- GroupByKey/
       CombinePerKey -- aggregation across elements -- Windowing,
       watermarks, and triggering in Beam -- Windowing in Beam --
       Watermarks in Beam -- Triggering in Beam -- Advanced topic
       -- stateful ParDo -- WordCount in Apache Beam -- Setting 
       up your pipeline -- Reading the works of Shakespeare in 
       parallel -- Splitting each line on spaces -- Eliminating 
       empty strings -- Counting the occurrences of each word -- 
       Format your results -- Writing to a sharded text file in 
       parallel -- Testing the pipeline at small scale with 
       DirectRunner -- Running Apache Beam WordCount on Apache 
       Apex -- Summary -- Chapter 10: The Future of Stream 
       Processing -- Lower barrier for building streaming 
       pipelines -- Visual development tools -- Streaming SQL -- 
       Better programming API -- Bridging the gap between data 
       science and engineering -- Machine learning integration --
       State management -- State query and data consistency -- 
       Containerized infrastructure -- Management tools -- 
       Summary -- Index. 
588    Description based on online resource; title from title 
       page (viewed January 9, 2018). 
590    eBooks on EBSCOhost|bEBSCO eBook Subscription Academic 
       Collection - North America 
630 00 Apache Apex. 
650  0 Data mining.|0https://id.loc.gov/authorities/subjects/
       sh97002073 
650  0 Big data.|0https://id.loc.gov/authorities/subjects/
       sh2012003227 
650  7 Data mining.|2fast|0https://id.worldcat.org/fast/887946 
650  7 Big data.|2fast|0https://id.worldcat.org/fast/1892965 
655  4 Electronic books. 
700 1  Ramanath, Munagala V.,|eauthor. 
700 1  Yan, David,|0https://id.loc.gov/authorities/names/
       nb2017021640|eauthor. 
700 1  Knowles, Kenneth,|eauthor. 
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
       search.ebscohost.com/login.aspx?direct=true&scope=site&
       db=nlebk&AN=1643015|zOnline ebook via EBSCO. Access 
       restricted to current Rider University students, faculty, 
       and staff. 
856 42 |3Instructions for reading/downloading the EBSCO version 
       of this ebook|uhttp://guides.rider.edu/ebooks/ebsco 
901    MARCIVE 20231220 
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994    92|bRID