Data Pipelines With Apache Airflow by Bas Harenslak

£39.99
Available
Product Ref. (e.g. ISBN, SKU)
9781617296901
Published:
07 Jun 2021
Format:
Paperback
Number of pages:
425 pages
Dimensions:
234x186x28mm
Weight:
0.860kgs
Author:
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 option

Pipelines can be challenging to manage, especially when your data has to flow through a collection of application components, servers, and cloud services. Airflow lets you schedule, restart, and backfill pipelines, and its easy-to-use UI and workflows with Python scripting has users praising its incredible flexibility. Data Pipelines with Apache Airflow takes you through best practices for creating pipelines for multiple tasks, including data lakes, cloud deployments, and data science.

 

Data Pipelines with Apache Airflow teaches you the ins-and-outs of the Directed Acyclic Graphs (DAGs) that power Airflow, and how to write your own DAGs to meet the needs of your projects. With complete coverage of both foundational and lesser-known features, when you're done you'll be set to start using Airflow for seamless data pipeline development and management.

 

Key Features

Framework foundation and best practices

Airflow's execution and dependency system

Testing Airflow DAGs

Running Airflow in production

 

For data-savvy developers, DevOps and data engineers, and system

administrators with intermediate Python skills.

 

About the technology

Data pipelines are used to extract, transform and load data to and from multiple sources, routing it wherever it's needed -- whether that's visualisation tools, business intelligence dashboards, or machine learning models. Airflow streamlines the whole process, giving you one tool for programmatically developing and monitoring batch data pipelines, and integrating all the pieces you use in your data stack.

 

Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.

About the Author: Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.

More Information
Number of pages 425

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