Delta Lake is interwoven in the fabric of the lives of those who live in the towns of Western and Lee, which are situated on its shore. Western was established in 1797 and Lee in 1812. People like Gen. William Floyd, a signer of the Declaration of Independence, helped settle Western, where he was eventually buried. The first town board meeting for the combined communities was held in the home of the Sheldon brothers, early settlers of the Lee area, and descendants of other early settlers still inhabit the area today. The small village of Delta was flooded to make way for Delta Lake, which would supply water to the states canal system and give Lee and Western a body of water that now hosts one of the best state parks in the Northeast. Around Delta Lake: Lee and Western showcases memories of the Bowman and Westernville Hotels, mud-truck racing, and annual field days in both towns.
Ready to simplify the process of building data lakehouses and data pipelines at scale? In this practical guide, learn how Delta Lake is helping data engineers, data scientists, and data analysts overcome key data reliability challenges with modern data engineering and management techniques. Authors Denny Lee, Tristen Wentling, Scott Haines, and Prashanth Babu (with contributions from Delta Lake maintainer R. Tyler Croy) share expert insights on all things Delta Lake--including how to run batch and streaming jobs concurrently and accelerate the usability of your data. You'll also uncover how ACID transactions bring reliability to data lakehouses at scale. This book helps you: Understand key data reliability challenges and how Delta Lake solves them Explain the critical role of Delta transaction logs as a single source of truth Learn the Delta Lake ecosystem with technologies like Apache Flink, Kafka, and Trino Architect data lakehouses with the medallion architecture Optimize Delta Lake performance with features like deletion vectors and liquid clustering
Home to a community of hardworking farmers and mill workers, the village of Delta stood along the banks of the Mohawk River until it was evacuated by the state to raise the water in the Erie Canal. Before the flooding of the river, Delta was a small country village with the same postmaster for over 30 years and families farming the same land for generations. In order to raise the water, the state approved the construction of five reservoirs across New York. The town was evacuated soon after, and the land that generations of residents toiled over now sits at the bottom of Lake Delta.
With the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS. This practical book shows data engineers, data scientists, and data analysts how to get Delta Lake and its features up and running. The ultimate goal of building data pipelines and applications is to gain insights from data. You'll understand how your storage solution choice determines the robustness and performance of the data pipeline, from raw data to insights. You'll learn how to: Use modern data management and data engineering techniques Understand how ACID transactions bring reliability to data lakes at scale Run streaming and batch jobs against your data lake concurrently Execute update, delete, and merge commands against your data lake Use time travel to roll back and examine previous data versions Build a streaming data quality pipeline following the medallion architecture
Delta Lake is interwoven in the fabric of the lives of those who live in the towns of Western and Lee, which are situated on its shore. Western was established in 1797 and Lee in 1812. People like Gen. William Floyd, a signer of the Declaration of Independence, helped settle Western, where he was eventually buried. The first town board meeting for the combined communities was held in the home of the Sheldon brothers, early settlers of the Lee area, and descendants of other early settlers still inhabit the area today. The small village of Delta was flooded to make way for Delta Lake, which would supply water to the state's canal system and give Lee and Western a body of water that now hosts one of the best state parks in the Northeast. Around Delta Lake: Lee and Western showcases memories of the Bowman and Westernville Hotels, mud-truck racing, and annual field days in both towns.
Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stack Key Features Learn how to leverage modern data stack tools and technologies for effective data integration Design and implement data integration solutions with practical advice and best practices Focus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologies Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You’ll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you’ll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you’ll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.What you will learn Discover the evolving architecture and technologies shaping data integration Process large data volumes efficiently with data warehousing Tackle the complexities of integrating large datasets from diverse sources Harness the power of data warehousing for efficient data storage and processing Design and optimize effective data integration solutions Explore data governance principles and compliance requirements Who this book is for This book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you’re a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape.
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
