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020    9781617296901|q(electronic audio bk.) 
020    1617296902|q(electronic audio bk.) 
024 8  9781617296901AU 
029 1  AU@|b000071968363 
035    (OCoLC)1290493300 
037    9781617296901AU|bO'Reilly Media 
040    TOH|beng|cTOH|dOCLCO|dORMDA|dOCLCF|dOCLCO|dOCLCL 
049    INap 
082 04 006.3/12 
082 04 006.3/12|223 
099    eAudiobook O’Reilly for Public Libraries 
100 1  Harenslak, Bas,|eauthor. 
245 10 Data Pipelines with Apache Airflow /|cJulian de Ruiter.
       |h[O'Reilly for electronic resources] 
250    1st edition. 
264  1 |bManning Publications,|c2021. 
300    1 online resource (1 sound file) 
336    spoken word|bspw|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    audio file 
365    |b399.00 
520    An Airflow bible. Useful for all kinds of users, from 
       novice to expert. Rambabu Posa, Sai Aashika Consultancy A 
       successful pipeline moves data efficiently, minimizing 
       pauses and blockages between tasks, keeping every process 
       along the way operational. Apache Airflow provides a 
       single customizable environment for building and managing 
       data pipelines, eliminating the need for a hodgepodge 
       collection of tools, snowflake code, and homegrown 
       processes. Using real-world scenarios and examples, Data 
       Pipelines with Apache Airflow teaches you how to simplify 
       and automate data pipelines, reduce operational overhead, 
       and smoothly integrate all the technologies in your stack.
       about the technology Data pipelines manage the flow of 
       data from initial collection through consolidation, 
       cleaning, analysis, visualization, and more. Apache 
       Airflow provides a single platform you can use to design, 
       implement, monitor, and maintain your pipelines. Its easy-
       to-use UI, plug-and-play options, and flexible Python 
       scripting make Airflow perfect for any data management 
       task. about the book Data Pipelines with Apache Airflow 
       teaches you how to build and maintain effective data 
       pipelines. You'll explore the most common usage patterns, 
       including aggregating multiple data sources, connecting to
       and from data lakes, and cloud deployment. Part reference 
       and part tutorial, this practical guide covers every 
       aspect of the directed acyclic graphs (DAGs) that power 
       Airflow, and how to customize them for your pipeline's 
       needs. what's inside Build, test, and deploy Airflow 
       pipelines as DAGs Automate moving and transforming data 
       Analyze historical datasets using backfilling Develop 
       custom components Set up Airflow in production 
       environments about the audience For DevOps, data engineers,
       machine learning engineers, and sysadmins with 
       intermediate Python skills. about the author Bas Harenslak
       and Julian de Ruiter are data engineers with extensive 
       experience using Airflow to develop pipelines for major 
       companies. Bas is also an Airflow committer. An easy-to-
       follow exploration of the benefits of orchestrating your 
       data pipeline jobs with Airflow. Daniel Lamblin, Coupang 
       The one reference you need to create, author, schedule, 
       and monitor workflows with Apache Airflow. Clear 
       recommendation. Thorsten Weber, bbv Software Services AG 
       By far the best resource for Airflow. Jonathan Wood, 
       LexisNexis NARRATED BY JULIE BRIERLEY. 
542    |f© 2021 Manning Publications Co. All rights reserved.
       |g2021 
550    Made available through: Safari, an O'Reilly Media Company.
588 0  Online resource; Title from title page (viewed May 9, 
       2021). 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Data mining. 
650  0 Cloud computing. 
650  0 Programming languages (Electronic computers) 
650  0 Python (Computer program language) 
650  0 Big data. 
650  0 Machine learning. 
650  0 Electronic data processing. 
650  0 Information storage and retrieval systems|xScalability. 
650  0 Application program interfaces (Computer software) 
650  2 Data Mining 
650  6 Exploration de données (Informatique) 
650  6 Infonuagique. 
650  6 Python (Langage de programmation) 
650  6 Données volumineuses. 
650  6 Apprentissage automatique. 
650  6 Interfaces de programmation d'applications. 
650  7 APIs (interfaces)|2aat 
650  7 Application program interfaces (Computer software)|2fast
       |0(OCoLC)fst00811704 
650  7 Big data.|2fast|0(OCoLC)fst01892965 
650  7 Cloud computing.|2fast|0(OCoLC)fst01745899 
650  7 Data mining.|2fast|0(OCoLC)fst00887946 
650  7 Electronic data processing.|2fast|0(OCoLC)fst00906956 
650  7 Information storage and retrieval systems|xScalability.
       |2fast|0(OCoLC)fst01921149 
650  7 Machine learning.|2fast|0(OCoLC)fst01004795 
650  7 Programming languages (Electronic computers)|2fast
       |0(OCoLC)fst01078704 
650  7 Python (Computer program language)|2fast
       |0(OCoLC)fst01084736 
655  4 Downloadable audio books. 
655  7 Audiobooks.|2fast|0(OCoLC)fst01726208 
655  7 Audiobooks.|2lcgft 
655  7 Livres audio.|2rvmgf 
700 1  Ruiter, Julian de,|eauthor. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781617296901AU/?ar
       |zAvailable on O’Reilly for Public Libraries 
994    92|bJFN