Compartir
Modern Data Architectures with Python: A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python (en Inglés)
Brian Lipp
(Autor)
·
Packt Publishing
· Tapa Blanda
Modern Data Architectures with Python: A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python (en Inglés) - Lipp, Brian
$ 261.096
$ 395.600
Ahorras: $ 134.504
Elige la lista en la que quieres agregar tu producto o crea una nueva lista
✓ Producto agregado correctamente a la lista de deseos.
Ir a Mis Listas
Origen: Estados Unidos
(Costos de importación incluídos en el precio)
Se enviará desde nuestra bodega entre el
Jueves 27 de Junio y el
Lunes 08 de Julio.
Lo recibirás en cualquier lugar de Colombia entre 1 y 5 días hábiles luego del envío.
Reseña del libro "Modern Data Architectures with Python: A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python (en Inglés)"
Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and KafkaKey Features: Develop modern data skills used in emerging technologiesLearn pragmatic design methodologies such as Data Mesh and data lakehousesGain a deeper understanding of data governancePurchase of the print or Kindle book includes a free PDF eBookBook Description: Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You'll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake.Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You'll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you'll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you'll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you'll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you'll get hands-on experience with Apache Spark, one of the key data technologies in today's market.By the end of this book, you'll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What You Will Learn: Understand data patterns including delta architectureDiscover how to increase performance with Spark internalsFind out how to design critical data diagramsExplore MLOps with tools such as AutoML and MLflowGet to grips with building data products in a data meshDiscover data governance and build confidence in your dataIntroduce data visualizations and dashboards into your data practiceWho this book is for: This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they're not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.
