Data pipelines with Apache Airflow / Bas Harenslak and Julian de Ruiter.
By: Harenslak, Bas [author.].
Contributor(s): Ruiter, Julian de [author.].
Publisher: Shelter Island, NY : Manning Publications Co., ©2021Description: 454 p: illustrations ; 24 cm.Content type: text | still image Media type: unmediated Carrier type: volumeISBN: 9781617296901; 1617296902.Subject(s): Airflow (Electronic resource : Apache Software Foundation) | Data mining | Cloud computing | Programming languages (Electronic computers) | Python (Computer program language) | Big data | Machine learning | Electronic data processing | Information storage and retrieval systems -- Scalability | Application program interfaces (Computer software) | Application program interfaces (Computer software) | Big data | Cloud computing | Data mining | Electronic data processing | Information storage and retrieval systems -- Scalability | Machine learning | Programming languages (Electronic computers) | Python (Computer program language)Genre/Form: Print books.Summary: Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. 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... 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. 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. 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. For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. -- From publisher's description.Current location | Call number | Status | Date due | Barcode | Item holds |
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On Shelf | QA76.9 .D343 2021 (Browse shelf) | Available | AU00000000017361 |
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Includes index.
Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. 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... 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. 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. 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. For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. -- From publisher's description.