Data engineering with Python : (Record no. 199985)

MARC details
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control field OSt
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control field 20260520102554.0
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781839214189
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 183921418X
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.133 CRI-D
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Crickard, Paul
245 ## - TITLE STATEMENT
Title Data engineering with Python :
Remainder of title work with massive datasets to design data models and automate data pipelines using Python
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Birmingham - Mumbai
Name of publisher Packt Publishing
Year of publication 2020
300 ## - PHYSICAL DESCRIPTION
Number of Pages xii, 337p.
500 ## - GENERAL NOTE
General note Published by Packt Publishing Limited, Birmingham, UK. Title page: Birmingham—Mumbai.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Preface -- Section 1. Building data pipelines: extract, transform, and load -- Ch. 1. What is data engineering? -- Ch. 2. Building our data engineering infrastructure -- Ch. 3. Reading and writing files -- Ch. 4. Working with databases -- Ch. 5. Cleaning, transforming, and enriching data -- Ch. 6. Building a 311 data pipeline -- Section 2. Deploying data pipelines in production -- Ch. 7. Features of a production pipeline -- Ch. 8. Version control with the NiFi registry -- Ch. 9. Monitoring data pipelines --Ch. 10. Building a production data pipeline -- Section 3. Beyond batch: real-time and streaming data -- Ch. 11. Building a custom NiFi processor -- Ch. 12. Streaming data with Apache NiFi -- Ch. 13. Streaming data with Apache Kafka -- Ch. 14. Data processing with Apache Spark -- Ch. 15. Real-time edge data with MiNiFi, Kafka, and Spark --Appendix and building a NiFi cluster -- Index.
520 ## - SUMMARY, ETC.
Summary, etc Practical guide to data engineering using Python and open-source Apache technologies for building, deploying, and managing data pipelines. Three sections: (1) Building ETL pipelines — reading/writing files, relational and NoSQL databases, data cleaning and transformation, Apache NiFi pipeline; (2) Production deployment — NiFi registry version control, monitoring, staging, validation, failure handling; (3) Real-time and streaming data — Apache NiFi streaming, Apache Kafka (Python producers and consumers), Apache Spark and PySpark processing, real-time edge data with MiNiFi, Kafka and Spark. Appendix: building a distributed NiFi cluster. Code files on GitHub. Suitable for data engineers, data analysts, ETL developers, and IT professionals transitioning to data-driven roles.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Computer program language
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Python
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Data mining.
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Topical Term Apache Kafka (Computer program)
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Topical Term Apache Spark (Electronic resource)
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Topical Term Real-time data processing.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books and Monographs
Holdings
Full call number Accession Number Koha item type Lost status Damaged status Permanent Location Current Location Shelving location Date acquired Source of acquisition
005.133 CRI-D 102776 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 20.05.2026 Mumbai, TV Enterprises
005.133 CRI-D 102777 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 20.05.2026 Mumbai, TV Enterprises
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