滿額折
Data Pipelines with Apache Airflow
商品資訊
相關商品
商品簡介
商品簡介
Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. Summary
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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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 reader
For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the author
Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Table of Contents PART 1 - GETTING STARTED 1 Meet Apache Airflow
2 Anatomy of an Airflow DAG
3 Scheduling in Airflow
4 Templating tasks using the Airflow context
5 Defining dependencies between tasks PART 2 - BEYOND THE BASICS 6 Triggering workflows
7 Communicating with external systems
8 Building custom components
9 Testing
10 Running tasks in containers PART 3 - AIRFLOW IN PRACTICE 11 Best practices
12 Operating Airflow in production
13 Securing Airflow
14 Project: Finding the fastest way to get around NYC PART 4 - IN THE CLOUDS 15 Airflow in the clouds
16 Airflow on AWS
17 Airflow on Azure
18 Airflow in GCP
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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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 reader
For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the author
Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Table of Contents PART 1 - GETTING STARTED 1 Meet Apache Airflow
2 Anatomy of an Airflow DAG
3 Scheduling in Airflow
4 Templating tasks using the Airflow context
5 Defining dependencies between tasks PART 2 - BEYOND THE BASICS 6 Triggering workflows
7 Communicating with external systems
8 Building custom components
9 Testing
10 Running tasks in containers PART 3 - AIRFLOW IN PRACTICE 11 Best practices
12 Operating Airflow in production
13 Securing Airflow
14 Project: Finding the fastest way to get around NYC PART 4 - IN THE CLOUDS 15 Airflow in the clouds
16 Airflow on AWS
17 Airflow on Azure
18 Airflow in GCP
您曾經瀏覽過的商品
購物須知
外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
無庫存之商品，在您完成訂單程序之後，將以空運的方式為你下單調貨。為了縮短等待的時間，建議您將外文書與其他商品分開下單，以獲得最快的取貨速度，平均調貨時間為1~2個月。
為了保護您的權益，「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨，請在商品鑑賞期內寄回，且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。