Data Engineering on Azure

Data Engineering on Azure
Author: Vlad Riscutia
Publisher: Simon and Schuster
Total Pages: 334
Release: 2021-08-17
Genre: Computers
ISBN: 1617298921
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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

Data Pipelines Pocket Reference

Data Pipelines Pocket Reference
Author: James Densmore
Publisher: O'Reilly Media
Total Pages: 277
Release: 2021-02-10
Genre: Computers
ISBN: 1492087807
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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

Perspectives on Data Science for Software Engineering

Perspectives on Data Science for Software Engineering
Author: Tim Menzies
Publisher: Morgan Kaufmann
Total Pages: 410
Release: 2016-07-14
Genre: Computers
ISBN: 0128042613
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Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains

Big Data in Engineering Applications

Big Data in Engineering Applications
Author: Sanjiban Sekhar Roy
Publisher: Springer
Total Pages: 381
Release: 2018-05-02
Genre: Technology & Engineering
ISBN: 9811084769
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This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.

97 Things Every Data Engineer Should Know

97 Things Every Data Engineer Should Know
Author: Tobias Macey
Publisher: "O'Reilly Media, Inc."
Total Pages: 263
Release: 2021-06-11
Genre: Computers
ISBN: 1492062383
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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 with Python

Data Engineering with Python
Author: Paul Crickard
Publisher: Packt Publishing Ltd
Total Pages: 357
Release: 2020-10-23
Genre: Computers
ISBN: 1839212306
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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.

Streaming Systems

Streaming Systems
Author: Tyler Akidau
Publisher: "O'Reilly Media, Inc."
Total Pages: 391
Release: 2018-07-16
Genre: Computers
ISBN: 1491983825
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Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra

Data Engineering

Data Engineering
Author: Olaf Wolkenhauer
Publisher: John Wiley & Sons
Total Pages: 296
Release: 2004-04-07
Genre: Technology & Engineering
ISBN: 0471464104
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Although data engineering is a multi-disciplinary field withapplications in control, decision theory, and the emerging hot areaof bioinformatics, there are no books on the market that make thesubject accessible to non-experts. This book fills the gap in thefield, offering a clear, user-friendly introduction to the maintheoretical and practical tools for analyzing complex systems. Anftp site features the corresponding MATLAB and Mathematical toolsand simulations. Market: Researchers in data management, electrical engineering,computer science, and life sciences.

Data Engineering and Data Science

Data Engineering and Data Science
Author: Kukatlapalli Pradeep Kumar
Publisher: John Wiley & Sons
Total Pages: 367
Release: 2023-08-29
Genre: Mathematics
ISBN: 1119841976
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DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

Modern Data Engineering with Apache Spark

Modern Data Engineering with Apache Spark
Author: Scott Haines
Publisher: Apress
Total Pages: 585
Release: 2022-03-23
Genre: Computers
ISBN: 9781484274514
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Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow. Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compile reusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes. ​Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production. ​ What You Will Learn Simplify data transformation with Spark Pipelines and Spark SQL Bridge data engineering with machine learning Architect modular data pipeline applications Build reusable application components and libraries Containerize your Spark applications for consistency and reliability Use Docker and Kubernetes to deploy your Spark applications Speed up application experimentation using Apache Zeppelin and Docker Understand serializable structured data and data contracts Harness effective strategies for optimizing data in your data lakes Build end-to-end Spark structured streaming applications using Redis and Apache Kafka Embrace testing for your batch and streaming applications Deploy and monitor your Spark applications Who This Book Is For Professional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness and use Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world