English | Size: 13.96 MB
Genre: eLearning
About This Book
Dive into the integration of machine learning and data science workflows into open data platforms with Modern Data Architectures with Python. This comprehensive guide will walk you through creating open lakehouses that are compatible with any technology, employing the medallion architecture and Delta Lake. From building pipelines on Databricks using SQL and Python to mastering streaming and batch-based data processing with Apache Spark and Confluent Kafka, this book covers all bases. Learn to deploy resources using infrastructure as code, automate workflows, delve into the basics of ML and modern MLOps tooling, and gain hands-on experience with Apache Spark. By the end, you’ll be well-equipped to build, manage, orchestrate, and architect data ecosystems with a solid foundation in both practical and theoretical knowledge.
Modern Data Processing Architecture
This chapter kicks off with an exploration of data architecture, including the methodologies for designing a robust data ecosystem. Navigating the complexities of architecting a data solution, you’ll learn about the essential concepts and their practical applications. Topics include databases, data warehouses, data lakes, data platform architecture, Lambda and Kappa architecture, Lakehouse and Delta architectures, and data mesh theory and practice. By the end of this chapter, you’ll be primed to architect data solutions at a high level, equipped with the foundational knowledge necessary for building your data solution.
Technical Requirements
For creating diagrams and technical documentation, several tools are recommended, including Lucid Chart, Draw.io, and OmniGraffle. These tools will aid in visualizing and planning your data architecture projects effectively.
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