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Develop modern data skills used in emerging technologies

Learn pragmatic design methodologies such as Data Mesh and data lakehouses

Gain a deeper understanding of data governance

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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 architecture

Discover how to increase performance with Spark internals

Find out how to design critical data diagrams

Explore MLOps with tools such as AutoML and MLflow

Get to grips with building data products in a data mesh

Discover data governance and build confidence in your data

Introduce data visualizations and dashboards into your data practice

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Machine Learning Engineering with Python

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Exploratory Data Analysis with Python Cookbook

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Amazon verified reviews

Om S Nov 09, 2023

Embarking on a journey into the world of data, this book is like a friendly guide, showing the way to navigate the complexities of modern technology. It skillfully breaks down the intricacies of emerging data skills, offering practical insights into design methodologies like Data Mesh and data lakehouses.As you delve into the pages, the book demystifies the world of data governance, providing a deeper understanding. Through clear examples in Python, it guides readers in building data pipelines on platforms like Databricks, employing tried-and-true techniques like the medallion architecture and Delta Lake. From understanding data patterns and enhancing performance with Spark internals to exploring MLOps tools and integrating visualization into data practices, this book serves as a bridge for developers, analytics engineers, and managers looking to fortify their organization's data ecosystem. The straightforward language, coupled with practical examples, makes it an invaluable resource for those eager to navigate the data landscape.

Amazon Verified review

Steven Fernandes Oct 29, 2023

This book provides a deep dive into understanding and utilizing data patterns, with a particular focus on delta architecture. It equips readers with knowledge on enhancing performance through Spark internals and instructs on designing essential data diagrams. Covering the operational aspects of machine learning, it explores MLOps, highlighting tools like AutoML and MLflow. The text also delves into constructing data products within a data mesh framework and emphasizes the importance of data governance to bolster confidence in data integrity. Finally, it introduces readers to the integration of data visualizations and dashboards as a critical component of data practice.

Rohan Pandit May 06, 2024

Gives great understanding of Data and its Various Architectures 5 starsThe text thoroughly explores the development of data products within a data mesh framework, underscoring the importance of data governance in bolstering trust in data accuracy and reliability. Additionally, it accounts readers with the integration of data visualizations and dashboards, emphasizing their pivotal role in fostering effective data management practices.

S.Kundu Dec 23, 2023

Modern Data Architectures with Python is an excellent read if you want to build end to end data pipelines with python. I would definitely recommend the book to someone who want to learn every phase of development of a data pipeline starting from scratch to finally deploy the application.Few important topics of the book that I want to highlight are as below:The book will start by introducing Modern Data processing architectures such as Lambda, Kappa, Lakehouse and Delta. Then will give an overview of Data Cleansing techniques along with Relational and Dimensional Modeling concepts.The book will then teach you Apache Spark architecture and will go through details of Delta Lake along with concepts such as Z-ordering, Bloom filters, etc. Then will teach you how to perform Batch and Stream Data Processing using PySpark along with how to work with Kafka when handling Streaming data. It will explain the Kafka architecture along with brief overview on Schema Registry and Kafka Connect.The book will then help you to learn MLOps along with concepts of Machine Learning, Hyperparameters, AutoML and MLFlow. Then will teach you how to use Notebooks for Data Visualization along with giving overview of different chart types. It will also cover the topic of SQL Analytics using Databricks. In this section the book will also cover concepts of GitHub, Docker, Terraform and how to use them for continuous integration in your workflow along with how to orchestrate your data workflows.Finally you will go through Hands-on Project where you will work on creating data pipelines along with creation of a machine learning application. You will then display all the data using dashboards. In the process of creating the project you will create GitHub repos, perform Terraform setup and create data catalogs as part of Data Governance.One of most important highlight of the book is each chapter contains one or more Practical labs which will help you to learn in a practical manner after learning the theoretical concepts.

H2N Oct 26, 2023

This book is essential for those keen to enhance their grasp on data. Designed for engineers, analysts, and managers, it explores modern data platforms and informed decision-making. It covers data architecture design, insights into analytics, Apache Spark's intricacies, Spark's batch and streaming capabilities, Kafka in data pipelines, MLOps for ML/AI, data visualization techniques, Python app integration with CI tools, practical Databricks applications, data governance essentials, and setting up projects using diverse tools. A captivating read for data enthusiasts!

Modern Data Architectures with Python: A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python

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