| data engineering side projects: 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 |
| data engineering side projects: Software Engineering at Google Titus Winters, Tom Manshreck, Hyrum Wright, 2020-02-28 Today, software engineers need to know not only how to program effectively but also how to develop proper engineering practices to make their codebase sustainable and healthy. This book emphasizes this difference between programming and software engineering. How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life? Based on their experience at Google, software engineers Titus Winters and Hyrum Wright, along with technical writer Tom Manshreck, present a candid and insightful look at how some of the worldâ??s leading practitioners construct and maintain software. This book covers Googleâ??s unique engineering culture, processes, and tools and how these aspects contribute to the effectiveness of an engineering organization. Youâ??ll explore three fundamental principles that software organizations should keep in mind when designing, architecting, writing, and maintaining code: How time affects the sustainability of software and how to make your code resilient over time How scale affects the viability of software practices within an engineering organization What trade-offs a typical engineer needs to make when evaluating design and development decisions |
| data engineering side projects: Data Engineering with Google Cloud Platform Adi Wijaya, 2022-03-31 Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book. |
| data engineering side projects: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data engineering side projects: The New And Improved Flask Mega-Tutorial Miguel Grinberg, 2018-02-03 The Flask Mega-Tutorial is an overarching tutorial for Python beginner and intermediate developers that teaches web development with the Flask framework. The tutorial has been thoroughly revised and expanded in 2017, now containing 23 chapters. The concepts that are covered go well beyond Flask, including a wide range of topics Python web developers need to know when writing their own applications. |
| data engineering side projects: Data Engineering with Python Paul Crickard, 2020-10-23 Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. |
| data engineering side projects: 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. |
| data engineering side projects: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| data engineering side projects: Start Small, Stay Small Rob Walling, 2010 Start Small, Stay Small is a step-by-step guide to launching a self-funded startup. If you're a desktop, mobile or web developer, this book is your blueprint to getting your startup off the ground with no outside investment.This book intentionally avoids topics restricted to venture-backed startups such as: honing your investment pitch, securing funding, and figuring out how to use the piles of cash investors keep placing in your lap.This book assumes: You don't have $6M of investor funds sitting in your bank account You're not going to relocate to the handful of startup hubs in the world You're not going to work 70 hour weeks for low pay with the hope of someday making millions from stock options There's nothing wrong with pursuing venture funding and attempting to grow fast like Amazon, Google, Twitter, and Facebook. It just so happened that most people are not in a place to do this.Start Small, Stay Small also focuses on the single most important element of a startup that most developers avoid: marketing. There are many great resources for learning how to write code, organize source control, or connect to a database. This book does not cover the technical aspects developers already know or can learn elsewhere. It focuses on finding your idea, testing it before you build, and getting it into the hands of your customers. |
| data engineering side projects: 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 |
| data engineering side projects: 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. |
| data engineering side projects: The Productive Programmer Neal Ford, 2008-07-03 Anyone who develops software for a living needs a proven way to produce it better, faster, and cheaper. The Productive Programmer offers critical timesaving and productivity tools that you can adopt right away, no matter what platform you use. Master developer Neal Ford not only offers advice on the mechanics of productivity-how to work smarter, spurn interruptions, get the most out your computer, and avoid repetition-he also details valuable practices that will help you elude common traps, improve your code, and become more valuable to your team. You'll learn to: Write the test before you write the code Manage the lifecycle of your objects fastidiously Build only what you need now, not what you might need later Apply ancient philosophies to software development Question authority, rather than blindly adhere to standards Make hard things easier and impossible things possible through meta-programming Be sure all code within a method is at the same level of abstraction Pick the right editor and assemble the best tools for the job This isn't theory, but the fruits of Ford's real-world experience as an Application Architect at the global IT consultancy ThoughtWorks. Whether you're a beginner or a pro with years of experience, you'll improve your work and your career with the simple and straightforward principles in The Productive Programmer. |
| data engineering side projects: Site Reliability Engineering Niall Richard Murphy, Betsy Beyer, Chris Jones, Jennifer Petoff, 2016-03-23 The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use |
| data engineering side projects: The Dark Side of Software Engineering Johann Rost, Robert L. Glass, 2011-03-23 Betrayal! Corruption! Software engineering? Industry experts Johann Rost and Robert L. Glass explore the seamy underbelly of software engineering in this timely report on and analysis of the prevalance of subversion, lying, hacking, and espionage on every level of software project management. Based on the authors' original research and augmented by frank discussion and insights from other well-respected figures, The Dark Side of Software Engineering goes where other management studies fear to tread -- a corporate environment where schedules are fabricated, trust is betrayed, millions of dollars are lost, and there is a serious need for the kind of corrective action that this book ultimately proposes. |
| data engineering side projects: Data Engineering with dbt Roberto Zagni, 2023-06-30 Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and run Purchase of the print or Kindle book includes a free PDF eBook Key Features Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasets Book Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You’ll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you’ll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you’ll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that’ll enable you to build reports with the BI tool of your choice.What you will learn Create a dbt Cloud account and understand the ELT workflow Combine Snowflake and dbt for building modern data engineering pipelines Use SQL to transform raw data into usable data, and test its accuracy Write dbt macros and use Jinja to apply software engineering principles Test data and transformations to ensure reliability and data quality Build a lightweight pragmatic data platform using proven patterns Write easy-to-maintain idempotent code using dbt materialization Who this book is for This book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started. |
| data engineering side projects: Powerful Python Aaron Maxwell, 2024-11-08 Once you've mastered the basics of Python, how do you skill up to the top 1%? How do you focus your learning time on topics that yield the most benefit for production engineering and data teams—without getting distracted by info of little real-world use? This book answers these questions and more. Based on author Aaron Maxwell's software engineering career in Silicon Valley, this unique book focuses on the Python first principles that act to accelerate everything else: the 5% of programming knowledge that makes the remaining 95% fall like dominos. It's also this knowledge that helps you become an exceptional Python programmer, fast. Learn how to think like a Pythonista: explore advanced Pythonic thinking Create lists, dicts, and other data structures using a high-level, readable, and maintainable syntax Explore higher-order function abstractions that form the basis of Python libraries Examine Python's metaprogramming tool for priceless patterns of code reuse Master Python's error model and learn how to leverage it in your own code Learn the more potent and advanced tools of Python's object system Take a deep dive into Python's automated testing and TDD Learn how Python logging helps you troubleshoot and debug more quickly |
| data engineering side projects: 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. |
| data engineering side projects: Industrial Megaprojects Edward W. Merrow, 2011-05-03 Avoid common pitfalls in large-scale projects using these smart strategies Over half of large-scale engineering and construction projects—off-shore oil platforms, chemical plants, metals processing, dams, and similar projects—have miserably poor results. These include billions of dollars in overruns, long delays in design and construction, and poor operability once finally completed. Industrial Megaprojects gives you a clear, nontechnical understanding of why these major projects get into trouble, and how your company can prevent hazardous and costly errors when undertaking such large technical and management challenges. Clearly explains the underlying causes of over-budget, delayed, and unsafe megaprojects Examines effects of poor project management, destructive team behaviors, weak accountability systems, short-term focus, and lack of investment in technical expertise Author is the CEO of the leading consulting firm for evaluating billion-dollar projects Companies worldwide are rethinking their large-scale projects. Industrial Megaprojects is your essential guide for this rethink, offering the tools and principles that are the true foundation of safe, cost-effective, successful megaprojects. |
| data engineering side projects: Innovative Technologies for Market Leadership Patrick Glauner, Philipp Plugmann, 2020-06-11 This book introduces the reader to the latest innovations in fields such as artificial intelligence, systems biology or surgery, and gives advice on what new technologies to consider for becoming a market leader of tomorrow. Companies generally acquire information on these fields from various sources such as market reports, scientific literature or conference events, but find it difficult to distinguish between mere hype and truly valuable innovations. This book offers essential guidance in the form of structured and authoritative contributions by experts in innovative technologies spanning from biology and medicine to augmented reality and smart power grids. The authors identify high-potential fields and demonstrate the impact of their technologies to create economic value in real-world applications. They also offer business leaders advice on whether and how to implement these new technologies and innovations in their companies or businesses. |
| data engineering side projects: Ultralearning Scott H. Young, 2019-08-06 Now a Wall Street Journal bestseller. Learn a new talent, stay relevant, reinvent yourself, and adapt to whatever the workplace throws your way. Ultralearning offers nine principles to master hard skills quickly. This is the essential guide to future-proof your career and maximize your competitive advantage through self-education. In these tumultuous times of economic and technological change, staying ahead depends on continual self-education—a lifelong mastery of fresh ideas, subjects, and skills. If you want to accomplish more and stand apart from everyone else, you need to become an ultralearner. The challenge of learning new skills is that you think you already know how best to learn, as you did as a student, so you rerun old routines and old ways of solving problems. To counter that, Ultralearning offers powerful strategies to break you out of those mental ruts and introduces new training methods to help you push through to higher levels of retention. Scott H. Young incorporates the latest research about the most effective learning methods and the stories of other ultralearners like himself—among them Benjamin Franklin, chess grandmaster Judit Polgár, and Nobel laureate physicist Richard Feynman, as well as a host of others, such as little-known modern polymath Nigel Richards, who won the French World Scrabble Championship—without knowing French. Young documents the methods he and others have used to acquire knowledge and shows that, far from being an obscure skill limited to aggressive autodidacts, ultralearning is a powerful tool anyone can use to improve their career, studies, and life. Ultralearning explores this fascinating subculture, shares a proven framework for a successful ultralearning project, and offers insights into how you can organize and exe - cute a plan to learn anything deeply and quickly, without teachers or budget-busting tuition costs. Whether the goal is to be fluent in a language (or ten languages), earn the equivalent of a college degree in a fraction of the time, or master multiple tools to build a product or business from the ground up, the principles in Ultralearning will guide you to success. |
| data engineering side projects: Engineering Project Appraisal Martin Rogers, Aidan Duffy, 2012-07-03 In most cases of civil engineering development, a range of alternative schemes meeting project goals are feasible, so some form of evaluation must be carried out to select the most appropriate to take forward. Evaluation criteria usually include the economic, environmental and social contexts of a project as well as the engineering challenges, so engineers must be familiar with the processes and tools used. The second edition of Engineering Project Appraisal equips students with the understanding and analytical tools to carry out effective appraisals of alternative development schemes, using both economic and non-economic criteria. The building blocks of economic appraisal are covered early, leading to techniques such as net present worth, internal rate of return and annual worth. Cost Benefit Analysis is dealt with in detail, together with related methods such as Cost Effectiveness and the Goal Achievement Matrix. The text also details three multi-criteria models which have proved useful in the evaluation of proposals in the transportation, solid waste, energy and water resources fields: the Simple Additive Weighting (SAW) Model, the Analytic Hierarchy Process (AHP) technique and Concordance Analysis. There is a full discussion dealing with risk and uncertainty in these models. With many worked examples and case studies, Engineering Project Appraisal is an essential text for both undergraduate and postgraduate students on professional civil engineering courses, and it is expected that students on planning and construction management courses will find it a valuable addition to their reading. |
| data engineering side projects: Data Pipelines with Apache Airflow Bas P. Harenslak, Julian de Ruiter, 2021-04-27 This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. -- |
| data engineering side projects: Implementing Domain-driven Design Vaughn Vernon, 2013 Vaughn Vernon presents concrete and realistic domain-driven design (DDD) techniques through examples from familiar domains, such as a Scrum-based project management application that integrates with a collaboration suite and security provider. Each principle is backed up by realistic Java examples, and all content is tied together by a single case study of a company charged with delivering a set of advanced software systems with DDD. |
| data engineering side projects: Chemical Engineering Design Gavin Towler, Ray Sinnott, 2012-01-25 Chemical Engineering Design, Second Edition, deals with the application of chemical engineering principles to the design of chemical processes and equipment. Revised throughout, this edition has been specifically developed for the U.S. market. It provides the latest US codes and standards, including API, ASME and ISA design codes and ANSI standards. It contains new discussions of conceptual plant design, flowsheet development, and revamp design; extended coverage of capital cost estimation, process costing, and economics; and new chapters on equipment selection, reactor design, and solids handling processes. A rigorous pedagogy assists learning, with detailed worked examples, end of chapter exercises, plus supporting data, and Excel spreadsheet calculations, plus over 150 Patent References for downloading from the companion website. Extensive instructor resources, including 1170 lecture slides and a fully worked solutions manual are available to adopting instructors. This text is designed for chemical and biochemical engineering students (senior undergraduate year, plus appropriate for capstone design courses where taken, plus graduates) and lecturers/tutors, and professionals in industry (chemical process, biochemical, pharmaceutical, petrochemical sectors). New to this edition: - Revised organization into Part I: Process Design, and Part II: Plant Design. The broad themes of Part I are flowsheet development, economic analysis, safety and environmental impact and optimization. Part II contains chapters on equipment design and selection that can be used as supplements to a lecture course or as essential references for students or practicing engineers working on design projects. - New discussion of conceptual plant design, flowsheet development and revamp design - Significantly increased coverage of capital cost estimation, process costing and economics - New chapters on equipment selection, reactor design and solids handling processes - New sections on fermentation, adsorption, membrane separations, ion exchange and chromatography - Increased coverage of batch processing, food, pharmaceutical and biological processes - All equipment chapters in Part II revised and updated with current information - Updated throughout for latest US codes and standards, including API, ASME and ISA design codes and ANSI standards - Additional worked examples and homework problems - The most complete and up to date coverage of equipment selection - 108 realistic commercial design projects from diverse industries - A rigorous pedagogy assists learning, with detailed worked examples, end of chapter exercises, plus supporting data and Excel spreadsheet calculations plus over 150 Patent References, for downloading from the companion website - Extensive instructor resources: 1170 lecture slides plus fully worked solutions manual available to adopting instructors |
| data engineering side projects: Spark in Action Jean-Georges Perrin, 2020-05-12 Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Foreword by Rob Thomas. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment |
| data engineering side projects: Agile Analytics Ken Collier, 2012 Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier's techniques offer optimal value whether your projects involve back-end data management, front-end business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now--whether you're an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results--and have fun along the way. |
| data engineering side projects: Project Engineering Frederick Plummer, 2011-04-08 For newly hired young engineers assigned to their first real 'project', there has been little to offer in the way of advice on 'where to begin', 'what to look out for and avoid', and 'how to get the job done right'. This book gives this advice from an author with long experience as senior engineer in government and industry (U.S. Army Corps of Engineers and Exxon-Mobil). Beginning with guidance on understanding the typical organizational structure of any type of technical firm or company, author Plummer incorporates numerous hands-on examples and provides help on getting started with a project team, understanding key roles, and avoiding common pitfalls. In addition, he offers unique help on first-time experiences of working in other countries with engineering cultures that can be considerably different from the US. - Reviews essentials of management for any new engineer suddenly thrust into responsibility - Emphasizes skills that can get you promoted—and pitfalls that can get you fired - Expanded case study to show typical evolution of a new engineer handed responsibility for a major design project |
| data engineering side projects: Requirements in Engineering Projects João M. Fernandes, Ricardo J. Machado, 2015-07-18 This book focuses on various topics related to engineering and management of requirements, in particular elicitation, negotiation, prioritisation, and documentation (whether with natural languages or with graphical models). The book provides methods and techniques that help to characterise, in a systematic manner, the requirements of the intended engineering system. It was written with the goal of being adopted as the main text for courses on requirements engineering, or as a strong reference to the topics of requirements in courses with a broader scope. It can also be used in vocational courses, for professionals interested in the software and information systems domain. Readers who have finished this book will be able to: - establish and plan a requirements engineering process within the development of complex engineering systems; - define and identify the types of relevant requirements in engineering projects; - choose and apply the most appropriate techniques to elicit the requirements of a given system; - conduct and manage negotiation and prioritisation processes for the requirements of a given engineering system; - document the requirements of the system under development, either in natural language or with graphical and formal models. Each chapter includes a set of exercises. |
| data engineering side projects: Building Mobile Apps at Scale Gergely Orosz, 2021-04-06 While there is a lot of appreciation for backend and distributed systems challenges, there tends to be less empathy for why mobile development is hard when done at scale. This book collects challenges engineers face when building iOS and Android apps at scale, and common ways to tackle these. By scale, we mean having numbers of users in the millions and being built by large engineering teams. For mobile engineers, this book is a blueprint for modern app engineering approaches. For non-mobile engineers and managers, it is a resource with which to build empathy and appreciation for the complexity of world-class mobile engineering. The book covers iOS and Android mobile app challenges on these dimensions: Challenges due to the unique nature of mobile applications compared to the web, and to the backend. App complexity challenges. How do you deal with increasingly complicated navigation patterns? What about non-deterministic event combinations? How do you localize across several languages, and how do you scale your automated and manual tests? Challenges due to large engineering teams. The larger the mobile team, the more challenging it becomes to ensure a consistent architecture. If your company builds multiple apps, how do you balance not rewriting everything from scratch while moving at a fast pace, over waiting on centralized teams? Cross-platform approaches. The tooling to build mobile apps keeps changing. New languages, frameworks, and approaches that all promise to address the pain points of mobile engineering keep appearing. But which approach should you choose? Flutter, React Native, Cordova? Native apps? Reuse business logic written in Kotlin, C#, C++ or other languages? What engineering approaches do world-class mobile engineering teams choose in non-functional aspects like code quality, compliance, privacy, compliance, or with experimentation, performance, or app size? |
| data engineering side projects: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| data engineering side projects: Righting Software Juval Löwy, 2019-11-27 Right Your Software and Transform Your Career Righting Software presents the proven, structured, and highly engineered approach to software design that renowned architect Juval Löwy has practiced and taught around the world. Although companies of every kind have successfully implemented his original design ideas across hundreds of systems, these insights have never before appeared in print. Based on first principles in software engineering and a comprehensive set of matching tools and techniques, Löwy’s methodology integrates system design and project design. First, he describes the primary area where many software architects fail and shows how to decompose a system into smaller building blocks or services, based on volatility. Next, he shows how to flow an effective project design from the system design; how to accurately calculate the project duration, cost, and risk; and how to devise multiple execution options. The method and principles in Righting Software apply regardless of your project and company size, technology, platform, or industry. Löwy starts the reader on a journey that addresses the critical challenges of software development today by righting software systems and projects as well as careers—and possibly the software industry as a whole. Software professionals, architects, project leads, or managers at any stage of their career will benefit greatly from this book, which provides guidance and knowledge that would otherwise take decades and many projects to acquire. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data engineering side projects: Data Teams Jesse Anderson, 2020 |
| data engineering side projects: Data Engineering for Machine Learning Pipelines Pavan Kumar Narayanan, |
| data engineering side projects: A Philosophy of Software Design John K. Ousterhout, 2021 This book addresses the topic of software design: how to decompose complex software systems into modules (such as classes and methods) that can be implemented relatively independently. The book first introduces the fundamental problem in software design, which is managing complexity. It then discusses philosophical issues about how to approach the software design process and it presents a collection of design principles to apply during software design. The book also introduces a set of red flags that identify design problems. You can apply the ideas in this book to minimize the complexity of large software systems, so that you can write software more quickly and cheaply.--Amazon. |
| data engineering side projects: Introduction to Machine Learning with Python Andreas C. Müller, Sarah Guido, 2016-09-26 Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills |
| data engineering side projects: Kill It with Fire Marianne Bellotti, 2021-04-06 Kill It with Fire chronicles the challenges of dealing with aging computer systems, along with sound modernization strategies. How to survive a legacy apocalypse “Kill it with fire,” the typical first reaction to a legacy system falling into obsolescence, is a knee-jerk approach that often burns through tons of money and time only to result in a less efficient solution. This book offers a far more forgiving modernization framework, laying out smart value-add strategies and proven techniques that work equally well for ancient systems and brand-new ones. Renowned for restoring some of the world’s oldest, messiest computer networks to operational excellence, software engineering expert Marianne Bellotti distills key lessons and insights from her experience into practical, research-backed guidance to help you determine when and how to modernize. With witty, engaging prose, Bellotti explains why new doesn’t always mean better, weaving in illuminating case studies and anecdotes from her work in the field. You’ll learn: Where to focus your maintenance efforts for maximum impact and value How to pick the right modernization solutions for your specific needs and keep your plans on track How to assess whether your migrations will add value before you invest in them What to consider before moving data to the cloud How to determine when a project is finished Packed with resources, exercises, and flexible frameworks for organizations of all ages and sizes, Kill It with Fire will give you a vested interest in your technology’s future. |
| data engineering side projects: Mechanism Analysis Lyndon O. Barton, 2016-04-19 This updated and enlarged Second Edition provides in-depth, progressive studies of kinematic mechanisms and offers novel, simplified methods of solving typical problems that arise in mechanisms synthesis and analysis - concentrating on the use of algebra and trigonometry and minimizing the need for calculus.;It continues to furnish complete coverag |
| data engineering side projects: Agile Data Science Russell Jurney, 2013-10-15 Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track |
| data engineering side projects: Drawdown Paul Hawken, 2017-04-18 • New York Times bestseller • The 100 most substantive solutions to reverse global warming, based on meticulous research by leading scientists and policymakers around the world “At this point in time, the Drawdown book is exactly what is needed; a credible, conservative solution-by-solution narrative that we can do it. Reading it is an effective inoculation against the widespread perception of doom that humanity cannot and will not solve the climate crisis. Reported by-effects include increased determination and a sense of grounded hope.” —Per Espen Stoknes, Author, What We Think About When We Try Not To Think About Global Warming “There’s been no real way for ordinary people to get an understanding of what they can do and what impact it can have. There remains no single, comprehensive, reliable compendium of carbon-reduction solutions across sectors. At least until now. . . . The public is hungry for this kind of practical wisdom.” —David Roberts, Vox “This is the ideal environmental sciences textbook—only it is too interesting and inspiring to be called a textbook.” —Peter Kareiva, Director of the Institute of the Environment and Sustainability, UCLA In the face of widespread fear and apathy, an international coalition of researchers, professionals, and scientists have come together to offer a set of realistic and bold solutions to climate change. One hundred techniques and practices are described here—some are well known; some you may have never heard of. They range from clean energy to educating girls in lower-income countries to land use practices that pull carbon out of the air. The solutions exist, are economically viable, and communities throughout the world are currently enacting them with skill and determination. If deployed collectively on a global scale over the next thirty years, they represent a credible path forward, not just to slow the earth’s warming but to reach drawdown, that point in time when greenhouse gases in the atmosphere peak and begin to decline. These measures promise cascading benefits to human health, security, prosperity, and well-being—giving us every reason to see this planetary crisis as an opportunity to create a just and livable world. |
| data engineering side projects: The Toaster Project Thomas Thwaites, 2012-03-20 Hello, my name is Thomas Thwaites, and I have made a toaster. So begins The Toaster Project, the author's nine-month-long journey from his local appliance store to remote mines in the UK to his mother's backyard, where he creates a crude foundry. Along the way, he learns that an ordinary toaster is made up of 404 separate parts, that the best way to smelt metal at home is by using a method found in a fifteenth-century treatise, and that plastic is almost impossible to make from scratch. In the end, Thwaites's homemade toaster—a haunting and strangely beautiful object—cost 250 times more than the toaster he bought at the store and involved close to two thousand miles of travel to some of Britain's remotest locations. The Toaster Project may seem foolish, even insane. Yet, Thwaites's quixotic tale, told with self-deprecating wit, helps us reflect on the costs and perils of our cheap consumer culture, and in so doing reveals much about the organization of the modern world. |
30+ Data Science Mini Project Ideas For College Students
Data science mini projects are key to unlocking a world of possibilities for college students. These bite-sized adventures allow students to bridge the gap between theory and practice, providing …
FINAL YEAR PROJECT REPORT
Tools for reading and writing data between in-memory data structures and different file formats. Data alignment and integrated handling of missing data. Reshaping and pivoting of data sets. …
with Databricks Advanced Data Engineering
Learning Objectives 1. Design databases and pipelines optimized for the Databricks Lakehouse Platform. 2. Implement efficient incremental data processing to validate and enrich data …
Fundamentals of Data Engineering
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 …
Chapter 02 Process of Data Science Projects - GitHub Pages
Generic process for data science projects with six phases Discovery, data preparation, model planning, model building, communication of results, and operationalization
Side Projects - University of Waterloo
What Are Side Projects What Are They? Personal projects that we undertake outside of our school; As we are all from tech backgrounds, we usually refer side projects to apps, websites, …
Data Engineers’ Handbook - Software AG
For data engineers moving to the cloud means pipeline redesigns, migration projects, and shifts in data processing strategy. These four data pipeline patterns are the building blocks for …
Data Center Projects: Project Management - ICDST
This paper focuses on the particular project management roles needed for data center projects, and how those management responsibilities can be divided up and accounted for, in order to …
M.S. Data Analytics - Data Engineering Program Guide
Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, …
“Data Science and Analytics using python & R”
Exploratory Data Analysis: Performed initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary …
SCHOOL OF DATA SCIENCE Data Engineering with AWS
Overview Learn to design data models, build data warehouses and data lakes, automate data pipelines, and manage massive datasets.
Modern data engineering playbook - Thoughtworks
Learn how shifting to a data product mindset will help you build the right thing and build the thing right – and how to assemble the right team to make it happen. Explore practices and principles …
Welcome to Step by Step guide to Data Engineering.(Zero to …
Reality: There are many Data-engineering roles in the Industry. Some of them are Support role which does not need a lot of programming knowledge but that is not a development project.
AI Data Engineering Lifecycle Checklist - Cloudera
lytica.com The Data Lifecycle for AI Machine learning is powering most of the recent advancements in artificial intelligence including autonomous systems, computer vision, natural …
Data Engineering Teams
data engineering team needs to communicate its data products visually. Graphs and animations are often the best ways to show what’s happening with data, especially vast amounts of it, so …
Data Science for Engineering: Open thesis topics
This PDF file contains a list of open topics for Master (and possibly Bachelor) theses ofered by the Data Science for Engineering group at Paderborn university (www.cs.upb.de/dse).
Data Engineering Introduction and Epochs - Panoply
For data, the two primary staffing segments are data scientists and data engineers. Data scientists are the wizards that can look at buckets of raw data and turn them into beautiful …
1. Introduction to Data Engineering - GitHub
The goal of Data Engineering is to make quality data available for analytics and decision-making. And it does this by collecting raw source data, processing data so it becomes usable, storing …
SCHOOL OF DATA SCIENCE Data Engineering with Microsoft …
Learn to design data models, build data warehouses, build data lakes and lakehouse architecture, create data pipelines, and work with large datasets on the Azure platform using Azure …
SCHOOL OF DATA SCIENCE Data Engineering with Microsoft …
Learn to design data models, build data warehouses, build data lakes and lakehouse architecture, create data pipelines, and work with large datasets on the Azure platform using Azure …
30+ Data Science Mini Project Ideas For College Students
Data science mini projects are key to unlocking a world of possibilities for college students. These bite-sized adventures allow students to bridge the gap between theory and practice, providing …
FINAL YEAR PROJECT REPORT
Tools for reading and writing data between in-memory data structures and different file formats. Data alignment and integrated handling of missing data. Reshaping and pivoting of data sets. …
with Databricks Advanced Data Engineering
Learning Objectives 1. Design databases and pipelines optimized for the Databricks Lakehouse Platform. 2. Implement efficient incremental data processing to validate and enrich data …
Fundamentals of Data Engineering
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 …
Chapter 02 Process of Data Science Projects - GitHub Pages
Generic process for data science projects with six phases Discovery, data preparation, model planning, model building, communication of results, and operationalization
Side Projects - University of Waterloo
What Are Side Projects What Are They? Personal projects that we undertake outside of our school; As we are all from tech backgrounds, we usually refer side projects to apps, websites, …
Data Engineers’ Handbook - Software AG
For data engineers moving to the cloud means pipeline redesigns, migration projects, and shifts in data processing strategy. These four data pipeline patterns are the building blocks for …
Data Center Projects: Project Management - ICDST
This paper focuses on the particular project management roles needed for data center projects, and how those management responsibilities can be divided up and accounted for, in order to …
M.S. Data Analytics - Data Engineering Program Guide
Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, …
“Data Science and Analytics using python & R”
Exploratory Data Analysis: Performed initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary …
SCHOOL OF DATA SCIENCE Data Engineering with AWS
Overview Learn to design data models, build data warehouses and data lakes, automate data pipelines, and manage massive datasets.
Modern data engineering playbook - Thoughtworks
Learn how shifting to a data product mindset will help you build the right thing and build the thing right – and how to assemble the right team to make it happen. Explore practices and principles …
Welcome to Step by Step guide to Data Engineering.(Zero to …
Reality: There are many Data-engineering roles in the Industry. Some of them are Support role which does not need a lot of programming knowledge but that is not a development project.
AI Data Engineering Lifecycle Checklist - Cloudera
lytica.com The Data Lifecycle for AI Machine learning is powering most of the recent advancements in artificial intelligence including autonomous systems, computer vision, natural …
Data Engineering Teams
data engineering team needs to communicate its data products visually. Graphs and animations are often the best ways to show what’s happening with data, especially vast amounts of it, so …
Data Science for Engineering: Open thesis topics
This PDF file contains a list of open topics for Master (and possibly Bachelor) theses ofered by the Data Science for Engineering group at Paderborn university (www.cs.upb.de/dse).
Data Engineering Introduction and Epochs - Panoply
For data, the two primary staffing segments are data scientists and data engineers. Data scientists are the wizards that can look at buckets of raw data and turn them into beautiful …
1. Introduction to Data Engineering - GitHub
The goal of Data Engineering is to make quality data available for analytics and decision-making. And it does this by collecting raw source data, processing data so it becomes usable, storing …
SCHOOL OF DATA SCIENCE Data Engineering with …
Learn to design data models, build data warehouses, build data lakes and lakehouse architecture, create data pipelines, and work with large datasets on the Azure platform using Azure …
SCHOOL OF DATA SCIENCE Data Engineering with …
Learn to design data models, build data warehouses, build data lakes and lakehouse architecture, create data pipelines, and work with large datasets on the Azure platform using Azure …
