Data Pipelines Pocket Reference
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Data Pipelines Pocket Reference
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Author : James Densmore
language : en
Publisher: O'Reilly Media
Release Date : 2021-02-10
Data Pipelines Pocket Reference written by James Densmore and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-10 with Computers categories.
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 Pipelines Pocket Reference
DOWNLOAD eBooks
Author : James Densmore
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-02-10
Data Pipelines Pocket Reference written by James Densmore and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-10 with Computers categories.
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 Pipelines Pocket Reference
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Author : James Densmore
language : en
Publisher:
Release Date : 2021
Data Pipelines Pocket Reference written by James Densmore and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Data pipelines are the foundation for success in data analytics and machine learning. Moving data from many diverse sources and processing 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 data pipeline design patterns, data ingestion implementation, data transformation, the orchestration of pipelines, and build versus buy decision making. 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 machine learning and analytics needs Considerations for pipeline maintenance, testing, and alerting.
Machine Learning Pocket Reference
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Author : Matt Harrison
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-08-27
Machine Learning Pocket Reference written by Matt Harrison and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-27 with Computers categories.
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Data Pipelines With Apache Airflow
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Author : Bas P. Harenslak
language : en
Publisher: Simon and Schuster
Release Date : 2021-04-27
Data Pipelines With Apache Airflow written by Bas P. Harenslak and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with Computers categories.
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 With Python
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Author : Paul Crickard
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-23
Data Engineering With Python written by Paul Crickard and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-23 with Computers categories.
Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key FeaturesBecome well-versed in data architectures, data preparation, and data optimization skills with the help of practical examplesDesign data models and learn how to extract, transform, and load (ETL) data using PythonSchedule, automate, and monitor complex data pipelines in productionBook Description Data 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 learnUnderstand how data engineering supports data science workflowsDiscover how to extract data from files and databases and then clean, transform, and enrich itConfigure processors for handling different file formats as well as both relational and NoSQL databasesFind out how to implement a data pipeline and dashboard to visualize resultsUse staging and validation to check data before landing in the warehouseBuild real-time pipelines with staging areas that perform validation and handle failuresGet to grips with deploying pipelines in the production environmentWho 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.
97 Things Every Data Engineer Should Know
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Author : Tobias Macey
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-11
97 Things Every Data Engineer Should Know written by Tobias Macey and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Computers categories.
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
Pytorch Pocket Reference
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Author : Joe Papa
language : en
Publisher: O'Reilly Media
Release Date : 2021-09-14
Pytorch Pocket Reference written by Joe Papa and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-14 with categories.
This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development--from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, GCP, or Azure, and your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem
Kafka The Definitive Guide
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Author : Neha Narkhede
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-08-31
Kafka The Definitive Guide written by Neha Narkhede and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-31 with Computers categories.
Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems
Data Warehousing And Analytics
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Author : David Taniar
language : en
Publisher: Springer Nature
Release Date : 2022-02-04
Data Warehousing And Analytics written by David Taniar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Computers categories.
This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics). This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.