Delta Lake Architecture Diagram

  delta lake architecture diagram: Delta Lake: The Definitive Guide Denny Lee, Tristen Wentling, Scott Haines, Prashanth Babu, 2024-10-30 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
  delta lake architecture diagram: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Manoj Kukreja, Danil Zburivsky, 2021-10-22 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.
  delta lake architecture diagram: Building the Data Lakehouse Bill Inmon, Ranjeet Srivastava, Mary Levins, 2021-10 The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.
  delta lake architecture diagram: Modern Data Architecture on AWS Behram Irani, 2023-08-31 Discover all the essential design and architectural patterns in one place to help you rapidly build and deploy your modern data platform using AWS services Key Features Learn to build modern data platforms on AWS using data lakes and purpose-built data services Uncover methods of applying security and governance across your data platform built on AWS Find out how to operationalize and optimize your data platform on AWS Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.What you will learn Familiarize yourself with the building blocks of modern data architecture on AWS Discover how to create an end-to-end data platform on AWS Design data architectures for your own use cases using AWS services Ingest data from disparate sources into target data stores on AWS Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services Find out how to implement data governance using AWS services Who this book is for This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud.
  delta lake architecture diagram: Essential PySpark for Scalable Data Analytics Sreeram Nudurupati, 2021-10-29 Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
  delta lake architecture diagram: Practical Lakehouse Architecture Gaurav Ashok Thalpati, 2024-07-24 This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse
  delta lake architecture diagram: Delta Lake: Up and Running Bennie Haelen, Dan Davis, 2023-10-16 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 architecture diagram: Architecting Data and Machine Learning Platforms Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner, 2023-10-12 All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach
  delta lake architecture diagram: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data
  delta lake architecture diagram: Mastering Databricks Lakehouse Platform Sagar Lad, Anjani Kumar, 2022-07-11 Enable data and AI workloads with absolute security and scalability KEY FEATURES ● Detailed, step-by-step instructions for every data professional starting a career with data engineering. ● Access to DevOps, Machine Learning, and Analytics wirthin a single unified platform. ● Includes design considerations and security best practices for efficient utilization of Databricks platform. DESCRIPTION Starting with the fundamentals of the databricks lakehouse platform, the book teaches readers on administering various data operations, including Machine Learning, DevOps, Data Warehousing, and BI on the single platform. The subsequent chapters discuss working around data pipelines utilizing the databricks lakehouse platform with data processing and audit quality framework. The book teaches to leverage the Databricks Lakehouse platform to develop delta live tables, streamline ETL/ELT operations, and administer data sharing and orchestration. The book explores how to schedule and manage jobs through the Databricks notebook UI and the Jobs API. The book discusses how to implement DevOps methods on the Databricks Lakehouse platform for data and AI workloads. The book helps readers prepare and process data and standardizes the entire ML lifecycle, right from experimentation to production. The book doesn't just stop here; instead, it teaches how to directly query data lake with your favourite BI tools like Power BI, Tableau, or Qlik. Some of the best industry practices on building data engineering solutions are also demonstrated towards the end of the book. WHAT YOU WILL LEARN ● Acquire capabilities to administer end-to-end Databricks Lakehouse Platform. ● Utilize Flow to deploy and monitor machine learning solutions. ● Gain practical experience with SQL Analytics and connect Tableau, Power BI, and Qlik. ● Configure clusters and automate CI/CD deployment. ● Learn how to use Airflow, Data Factory, Delta Live Tables, Databricks notebook UI, and the Jobs API. WHO THIS BOOK IS FOR This book is for every data professional, including data engineers, ETL developers, DB administrators, Data Scientists, SQL Developers, and BI specialists. You don't need any prior expertise with this platform because the book covers all the basics. TABLE OF CONTENTS 1. Getting started with Databricks Platform 2. Management of Databricks Platform 3. Spark, Databricks, and Building a Data Quality Framework 4. Data Sharing and Orchestration with Databricks 5. Simplified ETL with Delta Live Tables 6. SCD Type 2 Implementation with Delta Lake 7. Machine Learning Model Management with Databricks 8. Continuous Integration and Delivery with Databricks 9. Visualization with Databricks 10. Best Security and Compliance Practices of Databricks
  delta lake architecture diagram: Exam Ref DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI Daniil Maslyuk, Justin Frebault, 2023-07-09 Prepare for Microsoft Exam DP-500 and demonstrate your real-world ability to design, create, and deploy enterprise-scale data analytics solutions. Designed for business intelligence developers, architects, data analysts, and other professionals, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Enterprise Data Analyst Associate level. Focus on the expertise measured by these objectives: Implement and manage a data analytics environment Query and transform data Implement and manage data models Explore and visualize data This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are a business intelligence developer, architect, data engineer, data architect, data analyst, or another professional with Power BI and Azure experience. About the Exam Exam DP-500 focuses on knowledge needed to govern and administer data analytics environments; integrate analytics platforms into existing IT infrastructure; manage the analytics development lifecycle; query data with Azure Synapse Analytics; ingest and transform data with Power BI; design and build tabular models; optimize enterprise-scale data models; explore data with Azure Synapse Analytics; and visualize data with Power BI. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Enterprise Data Analyst Associate certification, demonstrating your knowledge of designing, creating, and deploying enterprise-scale data analytics solutions. Responsibilities include performing advanced data analytics at scale, collecting enterprise-level requirements for data analytics solutions that include Azure and Microsoft Power BI, advising on data governance and configuration for Power BI administration, monitoring data usage, and optimizing solution performance. See full details at: microsoft.com/learn
  delta lake architecture diagram: Distributed Data Systems with Azure Databricks Alan Bernardo Palacio, 2021-05-25 Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key FeaturesGet to grips with the distributed training and deployment of machine learning and deep learning modelsLearn how ETLs are integrated with Azure Data Factory and Delta LakeExplore deep learning and machine learning models in a distributed computing infrastructureBook Description Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline. What you will learnCreate ETLs for big data in Azure DatabricksTrain, manage, and deploy machine learning and deep learning modelsIntegrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creationDiscover how to use Horovod for distributed deep learningFind out how to use Delta Engine to query and process data from Delta LakeUnderstand how to use Data Factory in combination with DatabricksUse Structured Streaming in a production-like environmentWho this book is for This book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.
  delta lake architecture diagram: Modern Data Architectures with Python Brian Lipp, 2023-09-29 Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features 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 Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern 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 Who this book is forThis 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.
  delta lake architecture diagram: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.
  delta lake architecture diagram: Mastering Data Engineering and Analytics with Databricks Manoj Kumar, 2024-09-30 TAGLINE Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges KEY FEATURES ● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow. ● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action. ● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines. ● Offers proven strategies to optimize workflows and avoid common pitfalls. DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. WHAT WILL YOU LEARN ● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases. ● Optimize query performance and efficiently manage cloud resources for cost-effective data processing. ● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation. ● Build and deploy real-time data processing solutions for timely and actionable insights. ● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. WHO IS THIS BOOK FOR? This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content. TABLE OF CONTENTS SECTION 1 1. Introducing Data Engineering with Databricks 2. Setting Up a Databricks Environment for Data Engineering 3. Working with Databricks Utilities and Clusters SECTION 2 4. Extracting and Loading Data Using Databricks 5. Transforming Data with Databricks 6. Handling Streaming Data with Databricks 7. Creating Delta Live Tables 8. Data Partitioning and Shuffling 9. Performance Tuning and Best Practices 10. Workflow Management 11. Databricks SQL Warehouse 12. Data Storage and Unity Catalog 13. Monitoring Databricks Clusters and Jobs 14. Production Deployment Strategies 15. Maintaining Data Pipelines in Production 16. Managing Data Security and Governance 17. Real-World Data Engineering Use Cases with Databricks 18. AI and ML Essentials 19. Integrating Databricks with External Tools Index
  delta lake architecture diagram: Hybrid Intelligent Systems Ajith Abraham, Tzung-Pei Hong, Ketan Kotecha, Kun Ma, Pooja Manghirmalani Mishra, Niketa Gandhi, 2023-05-24 This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 97 selected papers from the 22nd International Conference on Hybrid Intelligent Systems (HIS 2022) and 26 papers from the 18th International Conference on Information Assurance and Security, which was held online, from 13 to 15 December 2022. A premier conference in the field of artificial intelligence and machine learning applications, HIS–IAS 2022, brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from over 35 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
  delta lake architecture diagram: Databricks Data Intelligence Platform Nikhil Gupta,
  delta lake architecture diagram: Experiencing Architecture, second edition Steen Eiler Rasmussen, 1964-03-15 A classic examination of superb design through the centuries. Widely regarded as a classic in the field, Experiencing Architecture explores the history and promise of good design. Generously illustrated with historical examples of designing excellence—ranging from teacups, riding boots, and golf balls to the villas of Palladio and the fish-feeding pavilion of Beijing's Winter Palace—Rasmussen's accessible guide invites us to appreciate architecture not only as a profession, but as an art that shapes everyday experience. In the past, Rasmussen argues, architecture was not just an individual pursuit, but a community undertaking. Dwellings were built with a natural feeling for place, materials and use, resulting in “a remarkably suitable comeliness.” While we cannot return to a former age, Rasmussen notes, we can still design spaces that are beautiful and useful by seeking to understand architecture as an art form that must be experienced. An understanding of good design comes not only from one's professional experience of architecture as an abstract, individual pursuit, but also from one's shared, everyday experience of architecture in real time—its particular use of light, color, shape, scale, texture, rhythm and sound. Experiencing Architecture reminds us of what good architectural design has accomplished over time, what it can accomplish still, and why it is worth pursuing. Wide-ranging and approachable, it is for anyone who has ever wondered “what instrument the architect plays on.”
  delta lake architecture diagram: Simplifying Data Engineering and Analytics with Delta Anindita Mahapatra, Doug May, 2022-07-29 Explore how Delta brings reliability, performance, and governance to your data lake and all the AI and BI use cases built on top of it Key Features • Learn Delta’s core concepts and features as well as what makes it a perfect match for data engineering and analysis • Solve business challenges of different industry verticals using a scenario-based approach • Make optimal choices by understanding the various tradeoffs provided by Delta Book Description Delta helps you generate reliable insights at scale and simplifies architecture around data pipelines, allowing you to focus primarily on refining the use cases being worked on. This is especially important when you consider that existing architecture is frequently reused for new use cases. In this book, you'll learn about the principles of distributed computing, data modeling techniques, and big data design patterns and templates that help solve end-to-end data flow problems for common scenarios and are reusable across use cases and industry verticals. You'll also learn how to recover from errors and the best practices around handling structured, semi-structured, and unstructured data using Delta. After that, you'll get to grips with features such as ACID transactions on big data, disciplined schema evolution, time travel to help rewind a dataset to a different time or version, and unified batch and streaming capabilities that will help you build agile and robust data products. By the end of this Delta book, you'll be able to use Delta as the foundational block for creating analytics-ready data that fuels all AI/BI use cases. What you will learn • Explore the key challenges of traditional data lakes • Appreciate the unique features of Delta that come out of the box • Address reliability, performance, and governance concerns using Delta • Analyze the open data format for an extensible and pluggable architecture • Handle multiple use cases to support BI, AI, streaming, and data discovery • Discover how common data and machine learning design patterns are executed on Delta • Build and deploy data and machine learning pipelines at scale using Delta Who this book is for Data engineers, data scientists, ML practitioners, BI analysts, or anyone in the data domain working with big data will be able to put their knowledge to work with this practical guide to executing pipelines and supporting diverse use cases using the Delta protocol. Basic knowledge of SQL, Python programming, and Spark is required to get the most out of this book.
  delta lake architecture diagram: From Saline to Freshwater Scott W. Starratt, Michael R. Rosen, 2021-12-23
  delta lake architecture diagram: Designing and Operating a Data Reservoir Mandy Chessell, Nigel L Jones, Jay Limburn, David Radley, Kevin Shank, IBM Redbooks, 2015-05-26 Together, big data and analytics have tremendous potential to improve the way we use precious resources, to provide more personalized services, and to protect ourselves from unexpected and ill-intentioned activities. To fully use big data and analytics, an organization needs a system of insight. This is an ecosystem where individuals can locate and access data, and build visualizations and new analytical models that can be deployed into the IT systems to improve the operations of the organization. The data that is most valuable for analytics is also valuable in its own right and typically contains personal and private information about key people in the organization such as customers, employees, and suppliers. Although universal access to data is desirable, safeguards are necessary to protect people's privacy, prevent data leakage, and detect suspicious activity. The data reservoir is a reference architecture that balances the desire for easy access to data with information governance and security. The data reservoir reference architecture describes the technical capabilities necessary for a system of insight, while being independent of specific technologies. Being technology independent is important, because most organizations already have investments in data platforms that they want to incorporate in their solution. In addition, technology is continually improving, and the choice of technology is often dictated by the volume, variety, and velocity of the data being managed. A system of insight needs more than technology to succeed. The data reservoir reference architecture includes description of governance and management processes and definitions to ensure the human and business systems around the technology support a collaborative, self-service, and safe environment for data use. The data reservoir reference architecture was first introduced in Governing and Managing Big Data for Analytics and Decision Makers, REDP-5120, which is available at: http://www.redbooks.ibm.com/redpieces/abstracts/redp5120.html. This IBM® Redbooks publication, Designing and Operating a Data Reservoir, builds on that material to provide more detail on the capabilities and internal workings of a data reservoir.
  delta lake architecture diagram: Engineering Data Mesh in Azure Cloud Aniruddha Deswandikar, 2024-03-29 Overcome data mesh adoption challenges using the cloud-scale analytics framework and make your data analytics landscape agile and efficient by using standard architecture patterns for diverse analytical workloads Key Features Delve into core data mesh concepts and apply them to real-world situations Safely reassess and redesign your framework for seamless data mesh integration Conquer practical challenges, from domain organization to building data contracts Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDecentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help. The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you’ll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud. The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI). By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.What you will learn Build a strategy to implement a data mesh in Azure Cloud Plan your data mesh journey to build a collaborative analytics platform Address challenges in designing, building, and managing data contracts Get to grips with monitoring and governing a data mesh Understand how to build a self-service portal for analytics Design and implement a secure data mesh architecture Resolve practical challenges related to data mesh adoption Who this book is for This book is for chief data officers and data architects of large and medium-size organizations who are struggling to maintain silos of data and analytics projects. Data architects and data engineers looking to understand data mesh and how it can help their organizations democratize data and analytics will also benefit from this book. Prior knowledge of managing centralized analytical systems, as well as experience with building data lakes, data warehouses, data pipelines, data integrations, and transformations is needed to get the most out of this book.
  delta lake architecture diagram: Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020-07-16 Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow
  delta lake architecture diagram: Introducing Microsoft SQL Server 2019 Kellyn Gorman, Allan Hirt, Dave Noderer, Mitchell Pearson, James Rowland-Jones, Dustin Ryan, Arun Sirpal, Buck Woody, 2020-04-27 Explore the impressive storage and analytic tools available with the in-cloud and on-premises versions of Microsoft SQL Server 2019. Key FeaturesGain insights into what’s new in SQL Server 2019Understand use cases and customer scenarios that can be implemented with SQL Server 2019Discover new cross-platform tools that simplify management and analysisBook Description Microsoft SQL Server comes equipped with industry-leading features and the best online transaction processing capabilities. If you are looking to work with data processing and management, getting up to speed with Microsoft Server 2019 is key. Introducing SQL Server 2019 takes you through the latest features in SQL Server 2019 and their importance. You will learn to unlock faster querying speeds and understand how to leverage the new and improved security features to build robust data management solutions. Further chapters will assist you with integrating, managing, and analyzing all data, including relational, NoSQL, and unstructured big data using SQL Server 2019. Dedicated sections in the book will also demonstrate how you can use SQL Server 2019 to leverage data processing platforms, such as Apache Hadoop and Spark, and containerization technologies like Docker and Kubernetes to control your data and efficiently monitor it. By the end of this book, you'll be well versed with all the features of Microsoft SQL Server 2019 and understand how to use them confidently to build robust data management solutions. What you will learnBuild a custom container image with a DockerfileDeploy and run the SQL Server 2019 container imageUnderstand how to use SQL server on LinuxMigrate existing paginated reports to Power BI Report ServerLearn to query Hadoop Distributed File System (HDFS) data using Azure Data StudioUnderstand the benefits of In-Memory OLTPWho this book is for This book is for database administrators, architects, big data engineers, or anyone who has experience with SQL Server and wants to explore and implement the new features in SQL Server 2019. Basic working knowledge of SQL Server and relational database management system (RDBMS) is required.
  delta lake architecture diagram: Big Data, Machine Learning, and Applications Ripon Patgiri, Sivaji Bandyopadhyay, Malaya Dutta Borah, Dalton Meitei Thounaojam, 2020-11-27 This book constitutes refereed proceedings of the First International First International Conference on Big Data, Machine Learning, and Applications, BigDML 2019, held in Silchar, India, in December. The 6 full papers and 3 short papers were carefully reviewed and selected from 152 submissions. The papers present research on such topics as computing methodology; machine learning; artificial intelligence; information systems; security and privacy.
  delta lake architecture diagram: The Definitive Guide to Azure Data Engineering Ron C. L'Esteve, 2021-08-24 Build efficient and scalable batch and real-time data ingestion pipelines, DevOps continuous integration and deployment pipelines, and advanced analytics solutions on the Azure Data Platform. This book teaches you to design and implement robust data engineering solutions using Data Factory, Databricks, Synapse Analytics, Snowflake, Azure SQL database, Stream Analytics, Cosmos database, and Data Lake Storage Gen2. You will learn how to engineer your use of these Azure Data Platform components for optimal performance and scalability. You will also learn to design self-service capabilities to maintain and drive the pipelines and your workloads. The approach in this book is to guide you through a hands-on, scenario-based learning process that will empower you to promote digital innovation best practices while you work through your organization’s projects, challenges, and needs. The clear examples enable you to use this book as a reference and guide for building data engineering solutions in Azure. After reading this book, you will have a far stronger skill set and confidence level in getting hands on with the Azure Data Platform. What You Will Learn Build dynamic, parameterized ELT data ingestion orchestration pipelines in Azure Data Factory Create data ingestion pipelines that integrate control tables for self-service ELT Implement a reusable logging framework that can be applied to multiple pipelines Integrate Azure Data Factory pipelines with a variety of Azure data sources and tools Transform data with Mapping Data Flows in Azure Data Factory Apply Azure DevOps continuous integration and deployment practices to your Azure Data Factory pipelines and development SQL databases Design and implement real-time streaming and advanced analytics solutions using Databricks, Stream Analytics, and Synapse Analytics Get started with a variety of Azure data services through hands-on examples Who This Book Is For Data engineers and data architects who are interested in learning architectural and engineering best practices around ELT and ETL on the Azure Data Platform, those who are creating complex Azure data engineering projects and are searching for patterns of success, and aspiring cloud and data professionals involved in data engineering, data governance, continuous integration and deployment of DevOps practices, and advanced analytics who want a full understanding of the many different tools and technologies that Azure Data Platform provides
  delta lake architecture diagram: Data Science in Production Ben Weber, 2020 Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub.
  delta lake architecture diagram: Building Micro-Frontends Luca Mezzalira, 2021-11-17 What's the answer to today's increasingly complex web applications? Micro-frontends. Inspired by the microservices model, this approach lets you break interfaces into separate features managed by different teams of developers. With this practical guide, Luca Mezzalira shows software architects, tech leads, and software developers how to build and deliver artifacts atomically rather than use a big bang deployment. You'll learn how micro-frontends enable your team to choose any library or framework. This gives your organization technical flexibility and allows you to hire and retain a broad spectrum of talent. Micro-frontends also support distributed or colocated teams more efficiently. Pick up this book and learn how to get started with this technological breakthrough right away. Explore available frontend development architectures Learn how microservice principles apply to frontend development Understand the four pillars for creating a successful micro-frontend architecture Examine the benefits and pitfalls of existing micro-frontend architectures Learn principles and best practices for creating successful automation strategies Discover patterns for integrating micro-frontend architectures using microservices or a monolith API layer
  delta lake architecture diagram: Data Lake Architecture Bill Inmon, 2016 Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities
  delta lake architecture diagram: Software Architecture for Big Data and the Cloud Ivan Mistrik, Rami Bahsoon, Nour Ali, Maritta Heisel, Bruce Maxim, 2017-06-12 Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. - Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques - Presents case studies involving enterprise, business, and government service deployment of big data applications - Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data
  delta lake architecture diagram: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
  delta lake architecture diagram: Google BigQuery: The Definitive Guide Valliappa Lakshmanan, Jordan Tigani, 2019-10-23 Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently. Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable.
  delta lake architecture diagram: Architect's Pocket Book Ann Ross, Jonathan Hetreed, 2011-04-11 View the dedicated microsite for free sample chapters and videos - architecturalpress.com/architects-pocket-book This handy pocket book brings together a wealth of useful information that architects need on a daily basis - on site or in the studio. The book provides guidance on a range of tasks, from complying with the Building Regulations, including the recent revisions to Part L, to helping with planning, use of materials and detailing. Compact and easy to use, the Architect’s Pocket Book has sold well over 65,000 copies to the nation’s architects, architecture students, designers and construction professionals who do not have an architectural background but need to understand the basics, fast. This is the famous little blue book that you can’t afford to be without. About the authors: Charlotte Baden-Powell was trained at the Architectural Association in London. She practised architecture for over 40 years, during which time she identified the need for this book, which was first published in 1997 and her vision is as relevant today. Jonathan Hetreed and Ann Ross have drawn from years of experience of running a small practice in Bath to update and extend the scope of the new edition to reflect continuing revisions to regulations and the increasing demand for sustainable construction methods. Customer reviews: “I have had this for ages and it’s no lie when I say it’s the one book I use the most. It’s exceptional, it’s a must.” “From brick and board sizes, technical details, terminology, symbols and information for Building Reg's - this book is extremely useful, very handy and concise.” “This is a must have for anyone working in the architectural field. It's a pocket of knowledge that almost always has what you're looking for.”
  delta lake architecture diagram: Business Intelligence with Databricks SQL Vihag Gupta, 2022-09-16 Master critical skills needed to deploy and use Databricks SQL and elevate your BI from the warehouse to the lakehouse with confidence Key FeaturesLearn about business intelligence on the lakehouse with features and functions of Databricks SQLMake the most of Databricks SQL by getting to grips with the enablers of its data warehousing capabilitiesA unique approach to teaching concepts and techniques with follow-along scenarios on real datasetsBook Description In this new era of data platform system design, data lakes and data warehouses are giving way to the lakehouse – a new type of data platform system that aims to unify all data analytics into a single platform. Databricks, with its Databricks SQL product suite, is the hottest lakehouse platform out there, harnessing the power of Apache Spark™, Delta Lake, and other innovations to enable data warehousing capabilities on the lakehouse with data lake economics. This book is a comprehensive hands-on guide that helps you explore all the advanced features, use cases, and technology components of Databricks SQL. You'll start with the lakehouse architecture fundamentals and understand how Databricks SQL fits into it. The book then shows you how to use the platform, from exploring data, executing queries, building reports, and using dashboards through to learning the administrative aspects of the lakehouse – data security, governance, and management of the computational power of the lakehouse. You'll also delve into the core technology enablers of Databricks SQL – Delta Lake and Photon. Finally, you'll get hands-on with advanced SQL commands for ingesting data and maintaining the lakehouse. By the end of this book, you'll have mastered Databricks SQL and be able to deploy and deliver fast, scalable business intelligence on the lakehouse. What you will learnUnderstand how Databricks SQL fits into the Databricks Lakehouse PlatformPerform everyday analytics with Databricks SQL Workbench and business intelligence toolsOrganize and catalog your data assetsProgram the data security model to protect and govern your dataTune SQL warehouses (computing clusters) for optimal query experienceTune the Delta Lake storage format for maximum query performanceDeliver extreme performance with the Photon query execution engineImplement advanced data ingestion patterns with Databricks SQLWho this book is for This book is for business intelligence practitioners, data warehouse administrators, and data engineers who are new to Databrick SQL and want to learn how to deliver high-quality insights unhindered by the scale of data or infrastructure. This book is also for anyone looking to study the advanced technologies that power Databricks SQL. Basic knowledge of data warehouses, SQL-based analytics, and ETL processes is recommended to effectively learn the concepts introduced in this book and appreciate the innovation behind the platform.
  delta lake architecture diagram: Territory ETH Studio Basel, Contemporary City Institute, Roger Diener, Liisa Gunnarsson, Mathias Gunz, Vesna Jovanović, Marcel Meili, Christian Mueller Inderbitzin, Christian Schmid, 2016 Between 2008 and 2014, ETH Studio Basel, under the guidance of Roger Diener and Marcel Meili, has been investigating the process of urbanisation taking place outside cities. Territory - in the context of this investigation denotes both: the surroundings that a city subsumes into its own structure and the core city itself, which is the centre of this process of urbanisation, or confiscation. Investigated were six regions on six continents: The Nile Valley with the dense corset of natural landscape surrounding a linear city; Rome-Adria, where territorial cells have formed within the territory, spawning an urban type of tremendous dynamism; Florida, presenting highly complex patterns of territorial organisation; Vietnam's Red River Delta, where recent reform exposed traditional settlement and cultivation of the delta to freer forces; Oman, where urbanisation of a territory essentially means reclaiming the desert with the immediate necessity to develop a system for water distribution; and Belo Horizonte, where natural conditions likewise play a major role in organising the territory as surface mining entails huge transformations of the natural terrain. The new book features two introductory essays on ETH Studio Basel's research approach and on terminology, concise illustrated reports on the six regions, and four concluding topical essays.
  delta lake architecture diagram: Cassandra: The Definitive Guide Jeff Carpenter, Eben Hewitt, 2016-06-29 Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This expanded second edition—updated for Cassandra 3.0—provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s non-relational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility. Understand Cassandra’s distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell Create a working data model and compare it with an equivalent relational model Develop sample applications using client drivers for languages including Java, Python, and Node.js Explore cluster topology and learn how nodes exchange data Maintain a high level of performance in your cluster Deploy Cassandra on site, in the Cloud, or with Docker Integrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene
  delta lake architecture diagram: Patterns of Enterprise Application Architecture Martin Fowler, 2012-03-09 The practice of enterprise application development has benefited from the emergence of many new enabling technologies. Multi-tiered object-oriented platforms, such as Java and .NET, have become commonplace. These new tools and technologies are capable of building powerful applications, but they are not easily implemented. Common failures in enterprise applications often occur because their developers do not understand the architectural lessons that experienced object developers have learned. Patterns of Enterprise Application Architecture is written in direct response to the stiff challenges that face enterprise application developers. The author, noted object-oriented designer Martin Fowler, noticed that despite changes in technology--from Smalltalk to CORBA to Java to .NET--the same basic design ideas can be adapted and applied to solve common problems. With the help of an expert group of contributors, Martin distills over forty recurring solutions into patterns. The result is an indispensable handbook of solutions that are applicable to any enterprise application platform. This book is actually two books in one. The first section is a short tutorial on developing enterprise applications, which you can read from start to finish to understand the scope of the book's lessons. The next section, the bulk of the book, is a detailed reference to the patterns themselves. Each pattern provides usage and implementation information, as well as detailed code examples in Java or C#. The entire book is also richly illustrated with UML diagrams to further explain the concepts. Armed with this book, you will have the knowledge necessary to make important architectural decisions about building an enterprise application and the proven patterns for use when building them. The topics covered include · Dividing an enterprise application into layers · The major approaches to organizing business logic · An in-depth treatment of mapping between objects and relational databases · Using Model-View-Controller to organize a Web presentation · Handling concurrency for data that spans multiple transactions · Designing distributed object interfaces
  delta lake architecture diagram: Precedents in Architecture Roger H. Clark, Michael Pause, 1996 Precedents in Architecture provides a vocabulary for architectural analysis that will help you understand the works of others, and aid you in creating your own designs. Here, you will examine the work of internationally known architects with the help of a unique diagrammatic technique, which you can also use to analyze existing buildings. In addition to the sixteen original contributors, the Second Edition features seven new, distinguished architects. All 23 architects were selected because of the strength, quality, and interest of their designs.
  delta lake architecture diagram: The Enterprise Big Data Lake Alex Gorelik, 2019-02-21 The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries
  delta lake architecture diagram: Docker in Action, Second Edition Jeffrey Nickoloff, Stephen Kuenzli, 2019-10-28 Summary Docker in Action, Second Edition teaches you the skills and knowledge you need to create, deploy, and manage applications hosted in Docker containers. This bestseller has been fully updated with new examples, best practices, and a number of entirely new chapters. About the technology The idea behind Docker is simple—package just your application and its dependencies into a lightweight, isolated virtual environment called a container. Applications running inside containers are easy to install, manage, and remove. This simple idea is used in everything from creating safe, portable development environments to streamlining deployment and scaling for microservices. In short, Docker is everywhere. About the book Docker in Action, Second Edition teaches you to create, deploy, and manage applications hosted in Docker containers running on Linux. Fully updated, with four new chapters and revised best practices and examples, this second edition begins with a clear explanation of the Docker model. Then, you go hands-on with packaging applications, testing, installing, running programs securely, and deploying them across a cluster of hosts. With examples showing how Docker benefits the whole dev lifecycle, you’ll discover techniques for everything from dev-and-test machines to full-scale cloud deployments. What's inside Running software in containers Packaging software for deployment Securing and distributing containerized applications About the reader Written for developers with experience working with Linux. About the author Jeff Nickoloff and Stephen Kuenzli have designed, built, deployed, and operated highly available, scalable software systems for nearly 20 years.
Delta Waterfowl's new logo. - Duck Hunting Forum
Jul 2, 2013 · I'm sure most of the guys know of the 2 can logo Delta has used forever. Here is the new logo. The idea behind the update is to make production of logo'd items less expensive …

HUNTING THE DELTA - Duck Hunting Forum
Apr 2, 2007 · The north delta has been known to hold a few birds later in the season. I know a few guys that killed a bird or 2 down south. those guys in the Antiock area are posting moderate …

Pennsylvania Delta Waterfowl Chapters | Duck Hunting Forum
Jul 18, 2013 · With Delta allowing us to keep money for local work, we're in a unique position to make a difference locally and nationally. GET INVOLVED. We need active members and …

Vhull vs flat bottom duck boat for delta | Duck Hunting Forum
Jan 8, 2015 · A Delta hunter buddy insists I get a flat bottom boat to get into the shallows. Ive had other people insist I get a vhull for stability in rougher waters. I want a boat thats going to be …

MS Delta Duck Shared Lease - Duck Hunting Forum
May 7, 2016 · I am looking for first hand information from someone that was a member of the MS Delta Duck shared lease program in the last two years. I have read old reviews from back in …

Delta Waterfowl Chapters and events in LA | Duck Hunting Forum
Sep 8, 2008 · The Northshore Louisiana Chapter of Delta Waterfowl would like to invite everyone to the 1st Annual Heritage Festival at Rookies Sports Cafe in Mandeville, LA. The event will be …

Mississippi Delta Best duck clubs for the Money
Dec 26, 2011 · I would like to get some information on duck clubs in the Mississippi Delta. I am locaed in South Carolina, and most anyone knows that the hunting here is terrible. I am …

delta level defence ar15 please explain to me why this gun is …
Delta Level Defense CAGE CODE: 7R7S9 NAICS CODE: 332994 DUNS: 961413619 👍 2. Comment. Post Cancel.

Delta Waterfowl Employment Opportunity - Missouri - Regional …
Dec 3, 2024 · Are you ready to take on the exciting role of Regional Director for Delta Waterfowl? Join us in making a difference for ducks and duck hunters. For additional details about the job …

THR 1st Annual Delta Waterfowl Banquet | Duck Hunting Forum
Jan 8, 2010 · If you want to join the newest chapter of Delta Waterfowl and the nation's fastest growing and most progressive waterfowl conservation organization today, reserve your tickets …

Delta Waterfowl's new logo. - Duck Hunting Forum
Jul 2, 2013 · I'm sure most of the guys know of the 2 can logo Delta has used forever. Here is the new logo. The idea behind the update is to make production of logo'd items less expensive …

HUNTING THE DELTA - Duck Hunting Forum
Apr 2, 2007 · The north delta has been known to hold a few birds later in the season. I know a few guys that killed a bird or 2 down south. those guys in the Antiock area are posting moderate …

Pennsylvania Delta Waterfowl Chapters | Duck Hunting Forum
Jul 18, 2013 · With Delta allowing us to keep money for local work, we're in a unique position to make a difference locally and nationally. GET INVOLVED. We need active members and …

Vhull vs flat bottom duck boat for delta | Duck Hunting Forum
Jan 8, 2015 · A Delta hunter buddy insists I get a flat bottom boat to get into the shallows. Ive had other people insist I get a vhull for stability in rougher waters. I want a boat thats going to be …

MS Delta Duck Shared Lease - Duck Hunting Forum
May 7, 2016 · I am looking for first hand information from someone that was a member of the MS Delta Duck shared lease program in the last two years. I have read old reviews from back in …

Delta Waterfowl Chapters and events in LA | Duck Hunting Forum
Sep 8, 2008 · The Northshore Louisiana Chapter of Delta Waterfowl would like to invite everyone to the 1st Annual Heritage Festival at Rookies Sports Cafe in Mandeville, LA. The event will be …

Mississippi Delta Best duck clubs for the Money
Dec 26, 2011 · I would like to get some information on duck clubs in the Mississippi Delta. I am locaed in South Carolina, and most anyone knows that the hunting here is terrible. I am …

delta level defence ar15 please explain to me why this gun is legal …
Delta Level Defense CAGE CODE: 7R7S9 NAICS CODE: 332994 DUNS: 961413619 👍 2. Comment. Post Cancel.

Delta Waterfowl Employment Opportunity - Missouri - Regional …
Dec 3, 2024 · Are you ready to take on the exciting role of Regional Director for Delta Waterfowl? Join us in making a difference for ducks and duck hunters. For additional details about the job …

THR 1st Annual Delta Waterfowl Banquet | Duck Hunting Forum
Jan 8, 2010 · If you want to join the newest chapter of Delta Waterfowl and the nation's fastest growing and most progressive waterfowl conservation organization today, reserve your tickets …