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| c# for data engineering: Programming C# 8.0 Ian Griffiths, 2019-11-26 C# is undeniably one of the most versatile programming languages available to engineers today. With this comprehensive guide, you’ll learn just how powerful the combination of C# and .NET can be. Author Ian Griffiths guides you through C# 8.0 fundamentals and techniques for building cloud, web, and desktop applications. Designed for experienced programmers, this book provides many code examples to help you work with the nuts and bolts of C#, such as generics, LINQ, and asynchronous programming features. You’ll get up to speed on .NET Core and the latest C# 8.0 additions, including asynchronous streams, nullable references, pattern matching, default interface implementation, ranges and new indexing syntax, and changes in the .NET tool chain. Discover how C# supports fundamental coding features, such as classes, other custom types, collections, and error handling Learn how to write high-performance memory-efficient code with .NET Core’s Span and Memory types Query and process diverse data sources, such as in-memory object models, databases, data streams, and XML documents with LINQ Use .NET’s multithreading features to exploit your computer’s parallel processing capabilities Learn how asynchronous language features can help improve application responsiveness and scalability |
| c# for data engineering: Fundamentals of Data Engineering Joe Reis, Matt Housley, 2022-06-22 Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle |
| c# for data engineering: Fundamentals of Computer Programming with C# Svetlin Nakov, Veselin Kolev, 2013-09-01 The free book Fundamentals of Computer Programming with C# is a comprehensive computer programming tutorial that teaches programming, logical thinking, data structures and algorithms, problem solving and high quality code with lots of examples in C#. It starts with the first steps in programming and software development like variables, data types, conditional statements, loops and arrays and continues with other basic topics like methods, numeral systems, strings and string processing, exceptions, classes and objects. After the basics this fundamental programming book enters into more advanced programming topics like recursion, data structures (lists, trees, hash-tables and graphs), high-quality code, unit testing and refactoring, object-oriented principles (inheritance, abstraction, encapsulation and polymorphism) and their implementation the C# language. It also covers fundamental topics that each good developer should know like algorithm design, complexity of algorithms and problem solving. The book uses C# language and Visual Studio to illustrate the programming concepts and explains some C# / .NET specific technologies like lambda expressions, extension methods and LINQ. The book is written by a team of developers lead by Svetlin Nakov who has 20+ years practical software development experience. It teaches the major programming concepts and way of thinking needed to become a good software engineer and the C# language in the meantime. It is a great start for anyone who wants to become a skillful software engineer. The books does not teach technologies like databases, mobile and web development, but shows the true way to master the basics of programming regardless of the languages, technologies and tools. It is good for beginners and intermediate developers who want to put a solid base for a successful career in the software engineering industry. The book is accompanied by free video lessons, presentation slides and mind maps, as well as hundreds of exercises and live examples. Download the free C# programming book, videos, presentations and other resources from http://introprogramming.info. Title: Fundamentals of Computer Programming with C# (The Bulgarian C# Programming Book) ISBN: 9789544007737 ISBN-13: 978-954-400-773-7 (9789544007737) ISBN-10: 954-400-773-3 (9544007733) Author: Svetlin Nakov & Co. Pages: 1132 Language: English Published: Sofia, 2013 Publisher: Faber Publishing, Bulgaria Web site: http://www.introprogramming.info License: CC-Attribution-Share-Alike Tags: free, programming, book, computer programming, programming fundamentals, ebook, book programming, C#, CSharp, C# book, tutorial, C# tutorial; programming concepts, programming fundamentals, compiler, Visual Studio, .NET, .NET Framework, data types, variables, expressions, statements, console, conditional statements, control-flow logic, loops, arrays, numeral systems, methods, strings, text processing, StringBuilder, exceptions, exception handling, stack trace, streams, files, text files, linear data structures, list, linked list, stack, queue, tree, balanced tree, graph, depth-first search, DFS, breadth-first search, BFS, dictionaries, hash tables, associative arrays, sets, algorithms, sorting algorithm, searching algorithms, recursion, combinatorial algorithms, algorithm complexity, OOP, object-oriented programming, classes, objects, constructors, fields, properties, static members, abstraction, interfaces, encapsulation, inheritance, virtual methods, polymorphism, cohesion, coupling, enumerations, generics, namespaces, UML, design patterns, extension methods, anonymous types, lambda expressions, LINQ, code quality, high-quality code, high-quality classes, high-quality methods, code formatting, self-documenting code, code refactoring, problem solving, problem solving methodology, 9789544007737, 9544007733 |
| c# for data engineering: Database Internals Alex Petrov, 2019-09-13 When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals. Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed. This book examines: Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each Storage building blocks: Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log Distributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns Database clusters: Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency |
| c# for data engineering: Computational Methods and Data Engineering Vijendra Singh, Vijayan K. Asari, Sanjay Kumar, R. B. Patel, 2020-08-19 This book gathers selected high-quality research papers from the International Conference on Computational Methods and Data Engineering (ICMDE 2020), held at SRM University, Sonipat, Delhi-NCR, India. Focusing on cutting-edge technologies and the most dynamic areas of computational intelligence and data engineering, the respective contributions address topics including collective intelligence, intelligent transportation systems, fuzzy systems, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing. |
| c# for data engineering: Systems, Patterns and Data Engineering with Geometric Calculi Sebastià Xambó-Descamps, 2021-07-16 The intention of this collection agrees with the purposes of the homonymous mini-symposium (MS) at ICIAM-2019, which were to overview the essentials of geometric calculus (GC) formalism, to report on state-of-the-art applications showcasing its advantages and to explore the bearing of GC in novel approaches to deep learning. The first three contributions, which correspond to lectures at the MS, offer perspectives on recent advances in the application GC in the areas of robotics, molecular geometry, and medical imaging. The next three, especially invited, hone the expressiveness of GC in orientation measurements under different metrics, the treatment of contact elements, and the investigation of efficient computational methodologies. The last two, which also correspond to lectures at the MS, deal with two aspects of deep learning: a presentation of a concrete quaternionic convolutional neural network layer for image classification that features contrast invariance and a general overview of automatic learning aimed at steering the development of neural networks whose units process elements of a suitable algebra, such as a geometric algebra. The book fits, broadly speaking, within the realm of mathematical engineering, and consequently, it is intended for a wide spectrum of research profiles. In particular, it should bring inspiration and guidance to those looking for materials and problems that bridge GC with applications of great current interest, including the auspicious field of GC-based deep neural networks. |
| c# for data engineering: Developing on AWS with C# Noah Gift, James Charlesworth, 2022-10-04 Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you're a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS. Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at Pendo, take you through the depth and breadth of .NET tools on AWS. You'll examine modernization techniques and pathways for incorporating Linux and Windows containers and serverless architecture to build, maintain, and scale modern .NET apps on AWS. With this book, you'll learn how to make your applications more modern, resilient, and cost-effective. Get started building solutions with C# on AWS Learn DevOps best practices for AWS Explore the development tools and services that AWS provides Successfully migrate a legacy .NET application to AWS Develop serverless .NET microservices on AWS Containerize your .NET applications and move into the cloud Monitor and test your AWS .NET applications Build cloud native solutions that combine the best of the .NET platform and AWS |
| c# for data engineering: Azure Data Engineering Cookbook Nagaraj Venkatesan, Ahmad Osama, 2022-09-26 Nearly 80 recipes to help you collect and transform data from multiple sources into a single data source, making it way easier to perform analytics on the data Key FeaturesBuild data pipelines from scratch and find solutions to common data engineering problemsLearn how to work with Azure Data Factory, Data Lake, Databricks, and Synapse AnalyticsMonitor and maintain your data engineering pipelines using Log Analytics, Azure Monitor, and Azure PurviewBook Description The famous quote 'Data is the new oil' seems more true every day as the key to most organizations' long-term success lies in extracting insights from raw data. One of the major challenges organizations face in leveraging value out of data is building performant data engineering pipelines for data visualization, ingestion, storage, and processing. This second edition of the immensely successful book by Ahmad Osama brings to you several recent enhancements in Azure data engineering and shares approximately 80 useful recipes covering common scenarios in building data engineering pipelines in Microsoft Azure. You'll explore recipes from Azure Synapse Analytics workspaces Gen 2 and get to grips with Synapse Spark pools, SQL Serverless pools, Synapse integration pipelines, and Synapse data flows. You'll also understand Synapse SQL Pool optimization techniques in this second edition. Besides Synapse enhancements, you'll discover helpful tips on managing Azure SQL Database and learn about security, high availability, and performance monitoring. Finally, the book takes you through overall data engineering pipeline management, focusing on monitoring using Log Analytics and tracking data lineage using Azure Purview. By the end of this book, you'll be able to build superior data engineering pipelines along with having an invaluable go-to guide. What you will learnProcess data using Azure Databricks and Azure Synapse AnalyticsPerform data transformation using Azure Synapse data flowsPerform common administrative tasks in Azure SQL DatabaseBuild effective Synapse SQL pools which can be consumed by Power BIMonitor Synapse SQL and Spark pools using Log AnalyticsTrack data lineage using Microsoft Purview integration with pipelinesWho this book is for This book is for data engineers, data architects, database administrators, and data professionals who want to get well versed with the Azure data services for building data pipelines. Basic understanding of cloud and data engineering concepts will help in getting the most out of this book. |
| c# for data engineering: Financial Data Engineering Tamer Khraisha, 2024-10-09 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering The structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source tools and APIs Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector. |
| c# for data engineering: 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. |
| c# for data engineering: Data Engineering and Management Rajkumar Kannan, Frederic Andres, 2012-01-16 This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Data Engineering and Management, ICDEM 2010, held in Tiruchirappalli, India, in July 2010. The 46 revised full papers presented together with 1 keynote paper and 2 tutorial papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on Digital Library; Knowledge and Mulsemedia; Data Management and Knowledge Extraction; Natural Language Processing; Workshop on Data Mining with Graphs and Matrices. |
| c# for data engineering: Recent Progress in Data Engineering and Internet Technology Ford Lumban Gaol, 2012-08-13 The latest inventions in internet technology influence most of business and daily activities. Internet security, internet data management, web search, data grids, cloud computing, and web-based applications play vital roles, especially in business and industry, as more transactions go online and mobile. Issues related to ubiquitous computing are becoming critical. Internet technology and data engineering should reinforce efficiency and effectiveness of business processes. These technologies should help people make better and more accurate decisions by presenting necessary information and possible consequences for the decisions. Intelligent information systems should help us better understand and manage information with ubiquitous data repository and cloud computing. This book is a compilation of some recent research findings in Internet Technology and Data Engineering. This book provides state-of-the-art accounts in computational algorithms/tools, database management and database technologies, intelligent information systems, data engineering applications, internet security, internet data management, web search, data grids, cloud computing, web-based application, and other related topics. |
| c# for data engineering: 97 Things Every Data Engineer Should Know Tobias Macey, 2021-06-11 Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail |
| c# for data engineering: Data Engineering with Scala and Spark Eric Tome, Rupam Bhattacharjee, David Radford, 2024-01-31 Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key Features Transform data into a clean and trusted source of information for your organization using Scala Build streaming and batch-processing pipelines with step-by-step explanations Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD) Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learn Set up your development environment to build pipelines in Scala Get to grips with polymorphic functions, type parameterization, and Scala implicits Use Spark DataFrames, Datasets, and Spark SQL with Scala Read and write data to object stores Profile and clean your data using Deequ Performance tune your data pipelines using Scala Who this book is for This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies. |
| c# for data engineering: 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. |
| c# for data engineering: Data Engineering on Azure Vlad Riscutia, 2021-09-21 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 |
| c# for data engineering: 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 |
| c# for data engineering: Data-Oriented Programming Yehonathan Sharvit, 2022-08-16 Eliminate the unavoidable complexity of object-oriented designs. The innovative data-oriented programming paradigm makes your systems less complex by making it simpler to access and manipulate data. In Data-Oriented Programming you will learn how to: Separate code from data Represent data with generic data structures Manipulate data with general-purpose functions Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data Benefit from polymorphism without objects Debug programs without a debugger Data-Oriented Programming is a one-of-a-kind guide that introduces the data-oriented paradigm. This groundbreaking approach represents data with generic immutable data structures. It simplifies state management, eases concurrency, and does away with the common problems you’ll find in object-oriented code. The book presents powerful new ideas through conversations, code snippets, and diagrams that help you quickly grok what’s great about DOP. Best of all, the paradigm is language-agnostic—you’ll learn to write DOP code that can be implemented in JavaScript, Ruby, Python, Clojure, and also in traditional OO languages like Java or C#. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Code that combines behavior and data, as is common in object-oriented designs, can introduce almost unmanageable complexity for state management. The Data-oriented programming (DOP) paradigm simplifies state management by holding application data in immutable generic data structures and then performing calculations using non-mutating general-purpose functions. Your applications are free of state-related bugs and your code is easier to understand and maintain. About the book Data-Oriented Programming teaches you to design software using the groundbreaking data-oriented paradigm. You’ll put DOP into action to design data models for business entities and implement a library management system that manages state without data mutation. The numerous diagrams, intuitive mind maps, and a unique conversational approach all help you get your head around these exciting new ideas. Every chapter has a lightbulb moment that will change the way you think about programming. What's inside Separate code from data Represent data with generic data structures Manage state without mutating data Control concurrency in highly scalable systems Write data-oriented unit tests Specify the shape of your data About the reader For programmers who have experience with a high-level programming language like JavaScript, Java, Python, C#, Clojure, or Ruby. About the author Yehonathan Sharvit has over twenty years of experience as a software engineer. He blogs, speaks at conferences, and leads Data-Oriented Programming workshops around the world. Table of Contents PART 1 FLEXIBILITY 1 Complexity of object-oriented programming 2 Separation between code and data 3 Basic data manipulation 4 State management 5 Basic concurrency control 6 Unit tests PART 2 SCALABILITY 7 Basic data validation 8 Advanced concurrency control 9 Persistent data structures 10 Database operations 11 Web services PART 3 MAINTAINABILITY 12 Advanced data validation 13 Polymorphism 14 Advanced data manipulation 15 Debugging |
| c# for data engineering: Model and Data Engineering Alfredo Cuzzocrea, Sofian Maabout, 2013-09-10 This book constitutes the refereed proceedings of the Third International Conference on Model and Data Engineering, MEDI 2013, held in Amantea, Calabria, Italy, in September 2013. The 19 long papers and 3 short papers presented were carefully reviewed and selected from 61 submissions. The papers specifically focus on model engineering and data engineering with special emphasis on most recent and relevant topics in the areas of model-driven engineering, ontology engineering, formal modeling, security, and database modeling. |
| c# for data engineering: Intelligent Data Engineering and Automated Learning -- IDEAL 2011 Hujun Yin, Wenjia Wang, Victor J. Rayward-Smith, 2011-08-30 This book constitutes the refereed proceedings of the 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011, held in Norwich, UK, in September 2011. The 59 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence. |
| c# for data engineering: Intelligent Data Engineering and Analytics Vikrant Bhateja, Xin-She Yang, Jerry Chun-Wei Lin, Ranjita Das, 2023-02-23 The book presents the proceedings of the 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022), held at NIT Mizoram, Aizawl, Mizoram, India during 18 – 19 June 2022. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. These proceedings are divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This volume is a valuable resource for postgraduate students in various engineering disciplines. |
| c# for data engineering: Intelligent Data Engineering and Analytics Suresh Chandra Satapathy, Yu-Dong Zhang, Vikrant Bhateja, Ritanjali Majhi, 2020-08-29 This book gathers the proceedings of the 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), held at NIT Surathkal, Karnataka, India, on 4–5 January 2020. In these proceedings, researchers, scientists, engineers and practitioners share new ideas and lessons learned in the field of intelligent computing theories with prospective applications in various engineering disciplines. The respective papers cover broad areas of the information and decision sciences, and explore both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. Given its scope, the book offers a valuable resource for graduate students in various engineering disciplines. |
| c# for data engineering: Pro C# 2010 and the .NET 4 Platform Andrew Troelsen, 2010-07-07 The first edition of this book was released at the 2001 Tech-Ed conference in Atlanta, Georgia. At that time, the .NET platform was still a beta product, and in many ways, so was this book. This is not to say that the early editions of this text did not have merit—after all, the book was a 2002 Jolt Award finalist and it won the 2003 Referenceware Excellence Award. However, over the years that author Andrew Troelsen spent working with the common language runtime (CLR), he gained a much deeper understanding of the .NET platform and the subtleties of the C# programming language, and he feels that this fifth edition of the book is as close to a “final release” as he’s come yet. This new edition has been comprehensively revised and rewritten to make it accurately reflect the C# 4 language specification for the .NET 4 platform. You’ll find new chapters covering the important concepts of dynamic lookups, named and optional arguments, Parallel LINQ (PLINQ), improved COM interop, and variance for generics. If you’re checking out this book for the first time, do understand that it's targeted at experienced software professionals and/or graduate students of computer science (so don't expect three chapters on iteration or decision constructs!). The mission of this text is to provide you with a rock-solid foundation in the C# programming language and the core aspects of the .NET platform (assemblies, remoting, Windows Forms, Web Forms, ADO.NET, XML web services, etc.). Once you digest the information presented in these 25 chapters, you’ll be in a perfect position to apply this knowledge to your specific programming assignments, and you’ll be well equipped to explore the .NET universe on your own terms. |
| c# for data engineering: AI-DRIVEN DATA ENGINEERING TRANSFORMING BIG DATA INTO ACTIONABLE INSIGHT Eswar Prasad Galla, Chandrababu Kuraku, Hemanth Kumar Gollangi, Janardhana Rao Sunkara, Chandrakanth Rao Madhavaram, ..... |
| c# for data engineering: Advances in Artificial Intelligence and Data Engineering Niranjan N. Chiplunkar, Takanori Fukao, 2020-08-13 This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering. |
| c# for data engineering: Microsoft Visual C# Step by Step John Sharp, 2015-10-28 Your hands-on guide to Microsoft Visual C# fundamentals with Visual Studio 2015 Expand your expertise--and teach yourself the fundamentals of programming with the latest version of Visual C# with Visual Studio 2015. If you are an experienced software developer, you’ll get all the guidance, exercises, and code you need to start building responsive, scalable Windows 10 and Universal Windows Platform applications with Visual C#. Discover how to: Quickly start creating Visual C# code and projects with Visual Studio 2015 Work with variables, operators, expressions, and methods Control program flow with decision and iteration statements Build more robust apps with error, exception, and resource management Master the essentials of Visual C# object-oriented programming Use enumerations, structures, generics, collections, indexers, and other advanced features Create in-memory data queries with LINQ query expressions Improve application throughput and response time with asynchronous methods Decouple application logic and event handling Streamline development with new app templates Implement the Model-View-ViewModel (MVVM) pattern Build Universal Windows Platform apps that smoothly adapt to PCs, tablets, and Windows phones Integrate Microsoft Azure cloud databases and RESTful web services About You For software developers who are new to Visual C# or who are upgrading from older versions Readers should have experience with at least one programming language No prior Microsoft .NET or Visual Studio development experience required |
| c# for data engineering: Fundamentals for Self-Taught Programmers Jasmine Greenaway, 2023-04-28 An absolute beginner's guide to strengthening the fundamentals before learning your first programming language Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore fundamental computer science concepts from data structures through to object-oriented programming Progress from understanding the software engineering landscape to writing your first program Authored by a Microsoft community insider and filled with case studies from software engineering roles Book Description Software engineering is a set of techniques, including programming, within the computer science discipline associated with the development of software products. This practical guide to software engineering will enable aspiring and new developers to satisfy their curiosity about the industry and become ready to learn more about the basics before beginning to explore programming languages, along with helping junior and upcoming developers to effectively apply their knowledge in the field. The book begins by providing you with a comprehensive introduction to software engineering, helping you gain a clear, holistic understanding of its various sub-fields. As you advance, you'll get to grips with the fundamentals of software engineering, such as flow control, data structures and algorithms. The book also introduces you to C# and guides you in writing your first program. The concluding chapters will cover case studies, including people working in the industry in different engineering roles, as well as interview tips and tricks and coding best practices. By the end of this programming book, you'll have gained practical knowledge of the implementation and associated methodologies in programming that will have you up and running and productive in no time. What you will learn Gain an understanding of the software engineering landscape Get up and running with fundamental programming concepts in C# Implement object-oriented programming (OOP) in C# Gain insights on how to keep the code readable and reusable Discover various tips and tricks to efficiently prepare for a software engineering interview Implement various popular algorithms using C# Who this book is for This book is for anyone who is curious about programming and interested in entering the field of software engineering by beginning at the fundamentals. No prior knowledge of computer science or software engineering is necessary. |
| c# for data engineering: Professional C# 7 and .NET Core 2.0 Christian Nagel, 2018-04-17 The professional’s guide to C# 7, with expert guidance on the newest features Professional C# 7 and .NET Core 2.0 provides experienced programmers with the information they need to work effectively with the world’s leading programming language. The latest C# update added many new features that help you get more done in less time, and this book is your ideal guide for getting up to speed quickly. C# 7 focuses on data consumption, code simplification, and performance, with new support for local functions, tuple types, record types, pattern matching, non-nullable reference types, immutable types, and better support for variables. Improvements to Visual Studio will bring significant changes to the way C# developers interact with the space, bringing .NET to non-Microsoft platforms and incorporating tools from other platforms like Docker, Gulp, and NPM. Guided by a leading .NET expert and steeped in real-world practicality, this guide is designed to get you up to date and back to work. With Microsoft speeding up its release cadence while offering more significant improvement with each update, it has never been more important to get a handle on new tools and features quickly. This book is designed to do just that, and more—everything you need to know about C# is right here, in the single-volume resource on every developer’s shelf. Tour the many new and enhanced features packed into C# 7 and .NET Core 2.0 Learn how the latest Visual Studio update makes developers’ jobs easier Streamline your workflow with a new focus on code simplification and performance enhancement Delve into improvements made for localization, networking, diagnostics, deployments, and more Whether you’re entirely new to C# or just transitioning to C# 7, having a solid grasp of the latest features allows you to exploit the language’s full functionality to create robust, high -quality apps. Professional C# 7 and .NET Core 2.0 is the one-stop guide to everything you need to know. |
| c# for data engineering: Trends in Data Engineering Methods for Intelligent Systems Jude Hemanth, Tuncay Yigit, Bogdan Patrut, Anastassia Angelopoulou, 2021-07-05 This book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics. |
| c# for data engineering: Intelligence Science and Big Data Engineering. Image and Video Data Engineering Xiaofei He, Xinbo Gao, Yanning Zhang, Zhi-Hua Zhou, Zhi-Yong Liu, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang, 2015-10-13 The two-volume set LNCS 9242 + 9243 constitutes the proceedings of the 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, held in Suzhou, China, in June 2015. The total of 126 papers presented in the proceedings was carefully reviewed and selected from 416 submissions. They deal with big data, neural networks, image processing, computer vision, pattern recognition and graphics, object detection, dimensionality reduction and manifold learning, unsupervised learning and clustering, anomaly detection, semi-supervised learning. |
| c# for data engineering: Data Teams Jesse Anderson, 2020 |
| c# for data engineering: Implementing Design Patterns in C# 11 and .NET 7 Alexandre F. Malavasi Cardoso, 2023-10-13 Unlock the potential of design patterns to write better code in C# 11 and .NET 7 KEY FEATURES ● Learn the essentials of C# and object-oriented programming. ● Gain insights into best practices for quality coding. ● Learn how to use design patterns to write code that is reusable, flexible, and maintainable. DESCRIPTION This book is a complete guide to design patterns and object-oriented programming (OOP) in C# and .NET. It covers everything from the basics of C# and Visual Studio to advanced topics like software architecture and best coding practices, including the SOLID principles. The book starts with the basics of C#, .NET, the SOLID principles, and the OOP paradigm. Then, it introduces widely-used design patterns with hands-on examples in C# and .NET. These examples include real-world scenarios and step-by-step instructions. In addition, the book provides an overview of advanced features in the .NET ecosystem, insights into current market solutions for software strategy, and guidance on when to use a design pattern-centric approach. The book concludes with valuable recommendations and best practices for .NET applications, especially when using design patterns. WHAT YOU WILL LEARN ● Learn how to use the Singleton pattern to ensure that only one instance of a class exists in your application. ● Learn how to use the Prototype pattern to create new objects by copying existing objects. ● Learn how to use the Factory Method pattern to create objects without specifying their concrete classes. ● Learn how to use the Adapter pattern to make incompatible interfaces work together. ● Learn how to use the Proxy pattern to control access to objects. ● Learn how to use the Strategy pattern to encapsulate algorithms. WHO THIS BOOK IS FOR This book is invaluable for software developers switching to .NET, experienced .NET developers learning about advanced design patterns, object-oriented programming paradigms, and SOLID principles, and .NET Core enthusiasts looking for information on core functionalities and recent platform advancements. TABLE OF CONTENTS 1. C# Fundamentals 2. .NET Fundamentals 3. Basic Concepts of Object-Oriented Programming in C# 4. SOLID Principles in C# 5. Introduction to Design Patterns 6. Singleton Pattern in .NET Applications 7. Abstract Factory Pattern with Blazor 8. Prototype Pattern with ASP.NET Razor 9. Factory Method Pattern Using New Features on C# 11 10. Adapter Pattern with Entity Framework Core 11. Composite Pattern with ASP.NET MVC 12. Proxy Pattern with GRPC 13. Command Pattern Using MediatR 14. Strategy Pattern Using Azure C# and Azure Functions 15. Observer Pattern |
| c# for data engineering: Azure Data Engineer Associate Certification Guide Giacinto Palmieri, Surendra Mettapalli, Newton Alex, 2024-05-23 Achieve Azure Data Engineer Associate certification success with this DP-203 exam guide Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, and exam tips, and the eBook PDF Key Features Prepare for the DP-203 exam with expert insights, real-world examples, and practice resources Gain up-to-date skills to thrive in the dynamic world of cloud data engineering Build secure and sustainable data solutions using Azure services Book DescriptionOne of the top global cloud providers, Azure offers extensive data hosting and processing services, driving widespread cloud adoption and creating a high demand for skilled data engineers. The Azure Data Engineer Associate (DP-203) certification is a vital credential, demonstrating your proficiency as an Azure data engineer to prospective employers. This comprehensive exam guide is designed for both beginners and seasoned professionals, aligned with the latest DP-203 certification exam, to help you pass the exam on your first try. The book provides a foundational understanding of IaaS, PaaS, and SaaS, starting with core concepts like virtual machines (VMs), VNETS, and App Services and progressing to advanced topics such as data storage, processing, and security. What sets this exam guide apart is its hands-on approach, seamlessly integrating theory with practice through real-world examples, practical exercises, and insights into Azure's evolving ecosystem. Additionally, you'll unlock lifetime access to supplementary practice material on an online platform, including mock exams, interactive flashcards, and exam tips, ensuring a comprehensive exam prep experience. By the end of this book, you’ll not only be ready to excel in the DP-203 exam, but also be equipped to tackle complex challenges as an Azure data engineer.What you will learn Design and implement data lake solutions with batch and stream pipelines Secure data with masking, encryption, RBAC, and ACLs Perform standard extract, transform, and load (ETL) and analytics operations Implement different table geometries in Azure Synapse Analytics Write Spark code, design ADF pipelines, and handle batch and stream data Use Azure Databricks or Synapse Spark for data processing using Notebooks Leverage Synapse Analytics and Purview for comprehensive data exploration Confidently manage VMs, VNETS, App Services, and more Who this book is for This book is for data engineers who want to take the Azure Data Engineer Associate (DP-203) exam and delve deep into the Azure cloud stack. Engineers and product managers new to Azure or preparing for interviews with companies working on Azure technologies will find invaluable hands-on experience with Azure data technologies through this book. A basic understanding of cloud technologies, ETL, and databases will assist with understanding the concepts covered. |
| c# for data engineering: Data-Oriented Design Richard Fabian, 2018-09-29 The projects tackled by the software development industry have grown in scale and complexity. Costs are increasing along with the number of developers. Power bills for distributed projects have reached the point where optimisations pay literal dividends. Over the last 10 years, a software development movement has gained traction, a movement founded in games development. The limited resources and complexity of the software and hardware needed to ship modern game titles demanded a different approach. Data-oriented design is inspired by high-performance computing techniques, database design, and functional programming values. It provides a practical methodology that reduces complexity while improving performance of both your development team and your product. Understand the goal, understand the data, understand the hardware, develop the solution. This book presents foundations and principles helping to build a deeper understanding of data-oriented design. It provides instruction on the thought processes involved when considering data as the primary detail of any project. |
| c# for data engineering: Dependency Injection Principles, Practices, and Patterns Mark Seemann, Steven van Deursen, 2019-03-06 Summary Dependency Injection Principles, Practices, and Patterns teaches you to use DI to reduce hard-coded dependencies between application components. You'll start by learning what DI is and what types of applications will benefit from it. Then, you'll work through concrete scenarios using C# and the .NET framework to implement DI in your own projects. As you dive into the thoroughly-explained examples, you'll develop a foundation you can apply to any of the many DI libraries for .NET and .NET Core. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Dependency Injection (DI) is a great way to reduce tight coupling between software components. Instead of hard-coding dependencies, such as specifying a database driver, you make those connections through a third party. Central to application frameworks like ASP.NET Core, DI enables you to better manage changes and other complexity in your software. About the Book Dependency Injection Principles, Practices, and Patterns is a revised and expanded edition of the bestselling classic Dependency Injection in .NET. It teaches you DI from the ground up, featuring relevant examples, patterns, and anti-patterns for creating loosely coupled, well-structured applications. The well-annotated code and diagrams use C# examples to illustrate principles that work flawlessly with modern object-oriented languages and DI libraries. What's Inside Refactoring existing code into loosely coupled code DI techniques that work with statically typed OO languages Integration with common .NET frameworks Updated examples illustrating DI in .NET Core About the Reader For intermediate OO developers. About the Authors Mark Seemann is a programmer, software architect, and speaker who has been working with software since 1995, including six years with Microsoft. Steven van Deursen is a seasoned .NET developer and architect, and the author and maintainer of the Simple Injector DI library. Table of Contents PART 1 Putting Dependency Injection on the map The basics of Dependency Injection: What, why, and how Writing tightly coupled code Writing loosely coupled code PART 2 Catalog DI patterns DI anti-patterns Code smells PART 3 Pure DI Application composition Object lifetime Interception Aspect-Oriented Programming by design Tool-based Aspect-Oriented Programming PART 4 DI Containers DI Container introduction The Autofac DI Container The Simple Injector DI Container The Microsoft.Extensions.DependencyInjection DI Container |
| c# for data engineering: Data Binding with Windows Forms 2.0 Brian Noyes, 2006-01-12 Data binding is the most important part of many business applications–and one of the most difficult things to understand. Data Binding with Windows Forms 2.0 is the first book to focus on this crucial area of development. It will quickly get you up to speed on binding data sources to Windows Forms components. The book contains clear examples in C# that work with SQL Server 2000 and SQL Server 2005. Visual Basic .NET examples are available on the book’s Web site. Brian Noyes, leading consultant and speaker on .NET programming, teaches you both the theory and practice of data binding and provides numerous samples ready to run in Visual Studio 2005. From his in-depth coverage, you’ll learn how to Use Visual Studio 2005 to generate a data-bound application from a database Use the new Visual Studio 2005 typed data set designer, and how and why to use typed data sets and typed data adapters Perform simple and complex binding of data to controls, and how to use the .NET 2.0 BindingSource Use the Binding object for simple binding with automatic formatting, and how to handle binding events Generate bound controls with the Visual Studio Designer, and how to use Data Sources Present data with the new DataGridView control, and how to implement advanced features of the DataGridView Implement custom data-bound controls in Windows Forms Create custom business objects and collections that are suitable for use in data binding Implement validation and error handling at the Windows Forms and data-binding levels Implement data binding with ASP.NET 2.0 and the upcoming Windows Presentation Foundation (Avalon) technologies |
| c# for data engineering: CLR Via C# Jeffrey Richter, 2006 Dig deep and master the intricacies of the common language runtime (CLR) and the .NET Framework. Written by a highly regarded programming expert and consultant to the Microsoft .NET team, this guide is ideal for developers building any kind of application--including Microsoft ASP.NET, Windows Forms, Microsoft SQL Server, Web services, and console applications. You'll get hands-on instruction and extensive code C# code samples to help you tackle the tough topics and develop high-performance applications. Discover how to: Build, deploy, administer, and version applications, components, and shared assemblies Design types using constants, fields, constructors, methods, properties, and events Work effectively with the CLR's special types including enumerators, arrays, and strings Declare, create, and use delegates to expose callback functions Define and employ re-usable algorithms with interfaces and generics Define, use, and detect custom attributes Use exception handling to build robust, reliable, and security-enhanced components Manage memory automatically with the garbage collector and work with native resources Apply CLR Hosting, AppDomains, assembly loading, and reflection to build dynamically extensible applications PLUS--Get code samples on the Web |
| c# for data engineering: Azure Data Engineer Associate Certification Guide Newton Alex, 2022-02-28 Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certification Key Features Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification exam Explore the various Azure services for building end-to-end data solutions Gain a solid understanding of building secure and sustainable data solutions using Azure services Book DescriptionAzure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What you will learn Gain intermediate-level knowledge of Azure the data infrastructure Design and implement data lake solutions with batch and stream pipelines Identify the partition strategies available in Azure storage technologies Implement different table geometries in Azure Synapse Analytics Use the transformations available in T-SQL, Spark, and Azure Data Factory Use Azure Databricks or Synapse Spark to process data using Notebooks Design security using RBAC, ACL, encryption, data masking, and more Monitor and optimize data pipelines with debugging tips Who this book is for This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack. The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book. |
| c# for data engineering: Big Data Processing with Apache Spark Srini Penchikala, 2018-03-13 Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX. |
| c# for data engineering: Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) Jemal H. Abawajy, Mohamed Othman, Rozaida Ghazali, Mustafa Mat Deris, Hairulnizam Mahdin, Tutut Herawan, 2019-08-09 These proceedings gather outstanding research papers presented at the Second International Conference on Data Engineering 2015 (DaEng-2015) and offer a consolidated overview of the latest developments in databases, information retrieval, data mining and knowledge management. The conference brought together researchers and practitioners from academia and industry to address key challenges in these fields, discuss advanced data engineering concepts and form new collaborations. The topics covered include but are not limited to: • Data engineering • Big data • Data and knowledge visualization • Data management • Data mining and warehousing • Data privacy & security • Database theory • Heterogeneous databases • Knowledge discovery in databases • Mobile, grid and cloud computing • Knowledge management • Parallel and distributed data • Temporal data • Web data, services and information engineering • Decision support systems • E-Business engineering and management • E-commerce and e-learning • Geographical information systems • Information management • Information quality and strategy • Information retrieval, integration and visualization • Information security • Information systems and technologies |
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