Manning – Data Pipelines With Apache Airflow

Manning – Data Pipelines With Apache Airflow-iLLiTERATE
English | Size: 3.68 GB
Category: Tutorial


In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

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.

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