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Data Pipelines with Apache Airflow
88% of respondents would recommend this to a friend
GHS 872
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Apache Airflow provides a single customizable environment for building and managing data pipelines, reducing the need for a hodgepodge collection of tools and homegrown processes
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What Stands Out
Product Details
- Teaches how to build and maintain effective data pipelines using Apache Airflow
- Covers common usage patterns, including aggregating multiple data sources and cloud deployment
- Explains directed acyclic graphs (DAGs) in Airflow and customizing them for specific needs
- Includes topics like automation, data transformation, and historical data analysis
- Suitable for DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills
- Provides best practices for operating Airflow in production and securing the platform
| Country of Origin | This item will be imported from US |
| Item Weight | 1.5 lbs (680 grams) |
| What is in the box | Data Pipelines with Apache... For more details, please check description/product details |
Who Should Buy?
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Data Engineers
Ideal for data engineers building and managing data workflows, ensuring automation, and improving data pipeline reliability.
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Analytics Teams
Perfect for analytics teams who need to orchestrate complex processes and manage dependencies in their data analysis.
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Cloud Professionals
Beneficial for professionals working with cloud services, enabling them to integrate and automate cloud data solutions effectively.
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Beginners
Not suitable for beginners without prior experience in data engineering or orchestration, as it has a steeper learning curve.
Product Description
Data Pipelines with Apache Airflow
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Cloud Computing Editorial Review
"Data Pipelines with Apache Airflow" is a comprehensive guide to using Apache Airflow for building data pipelines. The book provides best practices for pipeline management, beyond just using Apache Airflow as a tool. The first two chapters are enough for beginners to understand the fundamentals, and the rest of the book builds on that knowledge. The guide covers both basic and advanced topics, such as testing dags and running tasks in containers. The emphasis on counter-intuitive features to save time is appreciated. However, the book has a flaw: security is not addressed until page 322, and it should be one of the critical things covered in data engineering culture.
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Pros
- Comprehensive guide to using Apache Airflow for building data pipelines
- Provides best practices for pipeline management
- Covers both basic and advanced topics
- Emphasis on preventing beginners from wasting time on figuring out tweaks themselves
Cons
- Security is not covered until page 322
Platform Trust & Buyer Confidence
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GHS 872
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Features & Benefits
- Learn how to build and maintain effective data pipelines
- Reduce operational overhead and simplify pipeline automation
- Using real-world scenarios and examples
- Develop custom components and analyze historical datasets
- For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills
- Includes a free eBook in PDF, Kindle, and ePub formats
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