Data Wrangling On Aws
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Data Wrangling On Aws
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Author : Navnit Shukla
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-07-31
Data Wrangling On Aws written by Navnit Shukla 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 2023-07-31 with Computers categories.
Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databases Implement effective Pandas data operation with data wrangler Integrate pipelines with AWS data services Book DescriptionData wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.What you will learn Explore how to write simple to complex transformations using AWS data wrangler Use abstracted functions to extract and load data from and into AWS datastores Configure AWS Glue DataBrew for data wrangling Develop data pipelines using AWS data wrangler Integrate AWS security features into Data Wrangler using identity and access management (IAM) Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.
Data Wrangling On Aws
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Author : Navnit Shukla
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-07-31
Data Wrangling On Aws written by Navnit Shukla 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 2023-07-31 with Computers categories.
Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databases Implement effective Pandas data operation with data wrangler Integrate pipelines with AWS data services Book DescriptionData wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.What you will learn Explore how to write simple to complex transformations using AWS data wrangler Use abstracted functions to extract and load data from and into AWS datastores Configure AWS Glue DataBrew for data wrangling Develop data pipelines using AWS data wrangler Integrate AWS security features into Data Wrangler using identity and access management (IAM) Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.
Data Wrangling With Python
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Author : Jacqueline Kazil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-02-04
Data Wrangling With Python written by Jacqueline Kazil 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 2016-02-04 with Computers categories.
How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process
Data Science On Aws
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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07
Data Science On Aws written by Chris Fregly 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 2021-04-07 with Computers categories.
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
The Definitive Guide To Machine Learning Operations In Aws
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Author : Neel Sendas
language : en
Publisher: Springer Nature
Release Date : 2025-01-03
The Definitive Guide To Machine Learning Operations In Aws written by Neel Sendas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
Foreword by Dr. Shreyas Subramanian, Principal Data Scientist, Amazon This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOps tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost Who this book is for: This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.
Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions
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Author : Matt Taddy
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-08-23
Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions written by Matt Taddy and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Business & Economics categories.
Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.
Data Science Mit Aws
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Author : Chris Fregly
language : de
Publisher: O'Reilly
Release Date : 2022-04-13
Data Science Mit Aws written by Chris Fregly and has been published by O'Reilly this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-13 with Computers categories.
Von der ersten Idee bis zur konkreten Anwendung: Ihre Data-Science-Projekte in der AWS-Cloud realisieren Der US-Besteller zu Amazon Web Services jetzt auf Deutsch Beschreibt alle wichtigen Konzepte und die wichtigsten AWS-Dienste mit vielen Beispielen aus der Praxis Deckt den kompletten End-to-End-Prozess von der Entwicklung der Modelle bis zum ihrem konkreten Einsatz ab Mit Best Practices für alle Aspekte der Modellerstellung einschließlich Training, Deployment, Sicherheit und MLOps Mit diesem Buch lernen Machine-Learning- und KI-Praktiker, wie sie erfolgreich Data-Science-Projekte mit Amazon Web Services erstellen und in den produktiven Einsatz bringen. Es bietet einen detaillierten Einblick in den KI- und Machine-Learning-Stack von Amazon, der Data Science, Data Engineering und Anwendungsentwicklung vereint. Chris Fregly und Antje Barth beschreiben verständlich und umfassend, wie Sie das breite Spektrum an AWS-Tools nutzbringend für Ihre ML-Projekte einsetzen. Der praxisorientierte Leitfaden zeigt Ihnen konkret, wie Sie ML-Pipelines in der Cloud erstellen und die Ergebnisse dann innerhalb von Minuten in Anwendungen integrieren. Sie erfahren, wie Sie alle Teilschritte eines Workflows zu einer wiederverwendbaren MLOps-Pipeline bündeln, und Sie lernen zahlreiche reale Use Cases zum Beispiel aus den Bereichen Natural Language Processing, Computer Vision oder Betrugserkennung kennen. Im gesamten Buch wird zudem erläutert, wie Sie Kosten senken und die Performance Ihrer Anwendungen optimieren können.
Data Engineering With Aws
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Author : Gareth Eagar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-12-29
Data Engineering With Aws written by Gareth Eagar 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 2021-12-29 with Computers categories.
The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert Book DescriptionWritten by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What you will learn Understand data engineering concepts and emerging technologies Ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Who this book is for This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.
Aws Glue For Data Engineers
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-02-02
Aws Glue For Data Engineers written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-02 with Computers categories.
"AWS Glue for Data Engineers: Serverless ETL Made Easy" is an indispensable resource for data engineers seeking to master the art of efficient data integration and transformation in the cloud. This comprehensive guide provides an in-depth exploration of AWS Glue, a powerful tool that streamlines the extract, transform, and load (ETL) processes. Whether you are a novice or an experienced professional, this book is structured to enhance your understanding, covering everything from setup and configuration to advanced features and integrations with other AWS services. Within its pages, readers will discover seamless ways to optimize workflows, harness the full potential of serverless computing, and ensure robust data security and compliance. The book artfully combines practical insights with best practices, guiding you through the complexities of ETL with clear, step-by-step instructions. With real-world use cases and practical examples, it provides a robust framework for leveraging AWS Glue’s capabilities to drive your data engineering tasks, offering solutions to common challenges faced in modern data ecosystems. "AWS Glue for Data Engineers" is not just a technical manual; it’s a strategic roadmap for data professionals striving to enhance their skills in the rapidly evolving field of cloud computing. By adopting its methodologies, you can optimize your ETL workflows, reduce costs, and increase efficiency. Equip yourself with the knowledge to transform your data management practices and create scalable, dynamic systems that meet today’s business demands. Let this book be your guide to unlocking new efficiencies and innovations in your data engineering journey.
Advanced Data Engineering With Aws Building Scalable And Reliable Data Pipelines 2025
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Author : AUTHOR :1- GAYATRI TAVVA, AUTHOR :2 - DR PRIYANKA KAUSHIK
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Advanced Data Engineering With Aws Building Scalable And Reliable Data Pipelines 2025 written by AUTHOR :1- GAYATRI TAVVA, AUTHOR :2 - DR PRIYANKA KAUSHIK and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
PREFACE The exponential growth of data has redefined the way organizations operate, compete, and innovate. In today’s digital era, businesses are no longer just consumers of data but active participants in building complex, scalable ecosystems that collect, process, store, and derive value from massive data streams. Amazon Web Services (AWS), as the world’s leading cloud platform, offers a robust suite of tools and services that empower enterprises to transform raw data into actionable insights with unprecedented speed and reliability. This book, Advanced Data Engineering on AWS: Building Scalable, Secure, and Intelligent Pipelines, is designed to guide readers through the essential foundations and evolving innovations in data engineering using AWS. It systematically covers the principles and practices needed to architect high-performance data pipelines that can handle modern business demands. The journey begins with establishing the Foundations of Data Engineering in the AWS Ecosystem, helping readers understand how AWS services interplay to create a seamless environment for data management. We then explore Designing Data Pipelines for Scalability and Reliability, focusing on the architectural patterns that ensure resilience and flexibility in an unpredictable data landscape. As data sources become increasingly diverse and dynamic, mastering Data Ingestion Techniques on AWS is critical. We delve into both batch and real-time ingestion strategies, enabling efficient collection of high-velocity data. Coupled with this is Data Storage Optimization using services like S3, Redshift, and Beyond, ensuring that storage solutions align with both performance and cost-efficiency goals. Understanding ETL and ELT on AWS is pivotal for preparing data for downstream analytics and machine learning tasks. Subsequently, Real-Time Data Processing on AWS highlights how to transform and analyze data streams to deliver timely, business-critical insights. Automation becomes key as we address Data Orchestration and Workflow Automation, enabling complex pipelines to run with minimal human intervention. Ensuring trust in data requires rigorous focus on Data Quality and Governance, laying a strong foundation for secure, compliant, and high-fidelity analytics. We further extend this security narrative in Security and Compliance in AWS Data Pipelines, offering a deep dive into encryption, access controls, and regulatory alignment. No modern pipeline is complete without observability; hence, Monitoring, Logging, and Performance Tuning explores techniques to gain actionable insights into pipeline behavior, prevent failures, and optimize operations proactively. In an increasingly globalized world, Advanced Architectures: Multi-Region and Hybrid Pipelines prepares readers for designing architectures that span geographic—es and cloud environments, ensuring data availability and fault tolerance. Finally, we look ahead to Future Trends: AI/ML-Driven Data Engineering on AWS, where artificial intelligence automates data engineering tasks, adaptive pipelines become reality, and next-generation solutions redefine how businesses leverage data at scale. This book aims to serve data engineers, architects, cloud practitioners, and technical leaders who seek to not only build scalable AWS-based systems but also future-proof their architectures in an evolving technology landscape. Through a blend of foundational principles, hands-on techniques, best practices, and forward-looking insights, this book is your comprehensive guide to mastering advanced data engineering on AWS. We invite you to embark on this journey to build the data systems that will power the intelligent enterprises of tomorrow. Authors Gayatri Tavva Dr Priyanka Kaushik