東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Practical data engineering with Apache projects = solving everyday data challenges with Spark, Iceberg, Kafka, Flink, and more /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical data engineering with Apache projects/ by Dunith Danushka.
Reminder of title:	solving everyday data challenges with Spark, Iceberg, Kafka, Flink, and more /
Author:	Danushka, Dunith.
Published:	Berkeley, CA :Apress : : 2025.,
Description:	xix, 252 p. :ill., digital ;24 cm.
[NT 15003449]:	Part I: Data Lakehouses, Iceberg, Batch ETL, and Orchestration -- Chapter 1: Foundational Data Engineering Concepts -- Chapter 2: Building a Data Lakehouse with Apache Iceberg -- Chapter 3: Batch ETL Pipeline with Apache Spark -- Chapter 4: Data Visualization with Apache Superset -- Chapter 5: Workflow Orchestration with Apache Airflow -- Part II: Streaming Data and Real-time Analytics. - Chapter 6: Change Data Capture with Debezium and Kafka -- Chapter 7: Low-latency Analytics Dashboard with ClickHouse -- Chapter 8: Real-time Fraud Detection with Apache Flink -- Part III: Machine Learning and Generative AI -- Chapter 9: Building a Product Recommendation Engine with Spark MLlib -- Chapter 10: Vector Similarity Search with Postgres and pgvector.
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/979-8-8688-2142-4
ISBN:	9798868821424

Practical data engineering with Apache projects = solving everyday data challenges with Spark, Iceberg, Kafka, Flink, and more /
Danushka, Dunith.

Practical data engineering with Apache projectssolving everyday data challenges with Spark, Iceberg, Kafka, Flink, and more /[electronic resource] :by Dunith Danushka. - Berkeley, CA :Apress :2025. - xix, 252 p. :ill., digital ;24 cm.

Part I: Data Lakehouses, Iceberg, Batch ETL, and Orchestration -- Chapter 1: Foundational Data Engineering Concepts -- Chapter 2: Building a Data Lakehouse with Apache Iceberg -- Chapter 3: Batch ETL Pipeline with Apache Spark -- Chapter 4: Data Visualization with Apache Superset -- Chapter 5: Workflow Orchestration with Apache Airflow -- Part II: Streaming Data and Real-time Analytics. - Chapter 6: Change Data Capture with Debezium and Kafka -- Chapter 7: Low-latency Analytics Dashboard with ClickHouse -- Chapter 8: Real-time Fraud Detection with Apache Flink -- Part III: Machine Learning and Generative AI -- Chapter 9: Building a Product Recommendation Engine with Spark MLlib -- Chapter 10: Vector Similarity Search with Postgres and pgvector.

This book is a comprehensive guide designed to equip you with the practical skills and knowledge necessary to tackle real-world data challenges using open-source solutions. Focusing on real-world data engineering projects, it caters specifically to data engineers at the early stages of their careers, providing a strong foundation in essential open source tools and techniques such as Apache Spark, Flink, Airflow, Kafka, and many more. Each chapter is dedicated to a single project, starting with a clear presentation of the problem it addresses. You will then be guided through a step-by-step process to solve the problem, leveraging widely-used open-source data tools. This hands-on approach ensures that you not only understand the theoretical aspects of data engineering but also gain valuable experience in applying these concepts to real-world scenarios. At the end of each chapter, the book delves into common challenges that may arise during the implementation of the solution, offering practical advice on troubleshooting these issues effectively. Additionally, the book highlights best practices that data engineers should follow to ensure the robustness and efficiency of their solutions. A major focus of the book is using open-source projects and tools to solve problems encountered in data engineering. In summary, this book is an indispensable resource for data engineers looking to build a strong foundation in the field. By offering practical, real-world projects and emphasizing problem-solving and best practices, it will prepare you to tackle the complex data challenges encountered throughout your career. Whether you are an aspiring data engineer or looking to enhance your existing skills, this book provides the knowledge and tools you need to succeed in the ever-evolving world of data engineering. You Will Learn: The foundational concepts of data engineering and practical experience in solving real-world data engineering problems How to proficiently use open-source data tools like Apache Kafka, Flink, Spark, Airflow, and Trino 10 hands-on data engineering projects Troubleshoot common challenges in data engineering projects.

ISBN: 9798868821424

Standard No.: 10.1007/979-8-8688-2142-4doiSubjects--Uniform Titles:

Spark (Electronic resource : Apache Software Foundation)
Subjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Practical data engineering with Apache projects = solving everyday data challenges with Spark, Iceberg, Kafka, Flink, and more /
LDR:03980nmm a2200325 a 4500 001 2422998
003 DE-He213
005 20260102122933.0
006 m d
007 cr nn 008maaau
008 260505s2025 cau s 0 eng d
020 $a 9798868821424 $q (electronic bk.)
020 $a 9798868821417 $q (paper)
024 7 $a 10.1007/979-8-8688-2142-4 $2 doi
035 $a 979-8-8688-2142-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a U $2 bicssc
072 7 $a COM051390 $2 bisacsh
072 7 $a ULJ $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b D195 2025
100 1 $a Danushka, Dunith. $3 3805198
245 1 0 $a Practical data engineering with Apache projects $h [electronic resource] : $b solving everyday data challenges with Spark, Iceberg, Kafka, Flink, and more / $c by Dunith Danushka.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xix, 252 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part I: Data Lakehouses, Iceberg, Batch ETL, and Orchestration -- Chapter 1: Foundational Data Engineering Concepts -- Chapter 2: Building a Data Lakehouse with Apache Iceberg -- Chapter 3: Batch ETL Pipeline with Apache Spark -- Chapter 4: Data Visualization with Apache Superset -- Chapter 5: Workflow Orchestration with Apache Airflow -- Part II: Streaming Data and Real-time Analytics. - Chapter 6: Change Data Capture with Debezium and Kafka -- Chapter 7: Low-latency Analytics Dashboard with ClickHouse -- Chapter 8: Real-time Fraud Detection with Apache Flink -- Part III: Machine Learning and Generative AI -- Chapter 9: Building a Product Recommendation Engine with Spark MLlib -- Chapter 10: Vector Similarity Search with Postgres and pgvector.
520 $a This book is a comprehensive guide designed to equip you with the practical skills and knowledge necessary to tackle real-world data challenges using open-source solutions. Focusing on real-world data engineering projects, it caters specifically to data engineers at the early stages of their careers, providing a strong foundation in essential open source tools and techniques such as Apache Spark, Flink, Airflow, Kafka, and many more. Each chapter is dedicated to a single project, starting with a clear presentation of the problem it addresses. You will then be guided through a step-by-step process to solve the problem, leveraging widely-used open-source data tools. This hands-on approach ensures that you not only understand the theoretical aspects of data engineering but also gain valuable experience in applying these concepts to real-world scenarios. At the end of each chapter, the book delves into common challenges that may arise during the implementation of the solution, offering practical advice on troubleshooting these issues effectively. Additionally, the book highlights best practices that data engineers should follow to ensure the robustness and efficiency of their solutions. A major focus of the book is using open-source projects and tools to solve problems encountered in data engineering. In summary, this book is an indispensable resource for data engineers looking to build a strong foundation in the field. By offering practical, real-world projects and emphasizing problem-solving and best practices, it will prepare you to tackle the complex data challenges encountered throughout your career. Whether you are an aspiring data engineer or looking to enhance your existing skills, this book provides the knowledge and tools you need to succeed in the ever-evolving world of data engineering. You Will Learn: The foundational concepts of data engineering and practical experience in solving real-world data engineering problems How to proficiently use open-source data tools like Apache Kafka, Flink, Spark, Airflow, and Trino 10 hands-on data engineering projects Troubleshoot common challenges in data engineering projects.
630 0 0 $a Spark (Electronic resource : Apache Software Foundation) $3 2200514
650 0 $a Data mining. $3 562972
650 1 4 $a Open Source. $3 2210577
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Data Science. $3 3538937
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-2142-4
950 $a Professional and Applied Computing (SpringerNature-12059)