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Modern Data Architecture On Aws

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Modern Data Architecture On Aws

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Author : Behram Irani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-08-31

Modern Data Architecture On Aws written by Behram Irani 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 2023-08-31 with Computers categories.

Discover all the essential design and architectural patterns in one place to help you rapidly build and deploy your modern data platform using AWS services Key Features Learn to build modern data platforms on AWS using data lakes and purpose-built data services Uncover methods of applying security and governance across your data platform built on AWS Find out how to operationalize and optimize your data platform on AWS Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.What you will learn Familiarize yourself with the building blocks of modern data architecture on AWS Discover how to create an end-to-end data platform on AWS Design data architectures for your own use cases using AWS services Ingest data from disparate sources into target data stores on AWS Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services Find out how to implement data governance using AWS services Who this book is for This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud.

Aws Data Engineering For Modern Analytics

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Author : Frank Reiniger
language : en
Publisher: Independently Published
Release Date : 2025-11-05

Aws Data Engineering For Modern Analytics written by Frank Reiniger and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-05 with Computers categories.

AWS Data Engineering for Modern Analytics What if your data pipelines didn't break at scale, no surprise bills, no late-night firefights, no silent failures? In a world where cloud-native analytics defines competitive advantage, simply collecting data isn't enough. Enterprises need platforms that are secure, auditable, cost-efficient, and engineered to survive real-world complexity. This book is your practical blueprint for building production-ready data systems on AWS. It strips away hype and focuses on the reality facing modern data teams: how to architect lakes on S3 with intent, how to run Glue and EMR without waste, how to orchestrate with Step Functions and CI/CD instead of ad-hoc scripts, and how to design pipelines that evolve safely as your business grows. At its heart, this guide solves the biggest challenge in cloud data engineering-moving from prototypes that "work" to platforms you can trust with mission-critical workloads. You will learn how to: Structure S3 data lakes with the right formats, partitions, and lifecycle rules Build incremental ETL pipelines with Glue that handle schema changes and retries Implement real-time streaming with Kinesis and Flink for event-driven analytics Design secure, governed environments with IAM, Lake Formation, and encryption Deliver ML-ready feature pipelines and integrate with SageMaker Observe pipeline health, enforce SLAs, and prevent silent data drift Deploy reliable infrastructure using Terraform/CloudFormation and automated CICD Through hands-on labs and real deployment patterns, you'll master the engineering fundamentals behind cost control, operational resilience, metadata design, multi-environment workflows, disaster recovery, and future-proof storage formats like Apache Iceberg. If you're a data engineer, architect, analytics leader, or cloud practitioner committed to building systems that don't crumble under real workloads, this book will elevate your execution and confidence. Build with precision. Ship with certainty. Own your data platform, not the other way around. Get your copy and start engineering AWS pipelines the right way, today.

Amazon Redshift Cookbook

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Author : Shruti Worlikar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-07-23

Amazon Redshift Cookbook written by Shruti Worlikar 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 2021-07-23 with Computers categories.

Discover how to build a cloud-based data warehouse at petabyte-scale that is burstable and built to scale for end-to-end analytical solutions Key FeaturesDiscover how to translate familiar data warehousing concepts into Redshift implementationUse impressive Redshift features to optimize development, productionizing, and operations processesFind out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queriesBook Description Amazon Redshift is a fully managed, petabyte-scale AWS cloud data warehousing service. It enables you to build new data warehouse workloads on AWS and migrate on-premises traditional data warehousing platforms to Redshift. This book on Amazon Redshift starts by focusing on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll then learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. Because of the massive amount of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of Redshift's columnar architecture and managed services. As you advance, you'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, which help minimize the operational efforts that you have to invest in managing regular ETL pipelines and ensure the timely and accurate refreshing of your data warehouse. Finally, you'll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform. By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems. What you will learnUse Amazon Redshift to build petabyte-scale data warehouses that are agile at scaleIntegrate your data warehousing solution with a data lake using purpose-built features and services on AWSBuild end-to-end analytical solutions from data sourcing to consumption with the help of useful recipesLeverage Redshift's comprehensive security capabilities to meet the most demanding business requirementsFocus on architectural insights and rationale when using analytical recipesDiscover best practices for working with big data to operate a fully managed solutionWho this book is for This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, such as data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, and cloud concepts and familiarity with Redshift will be beneficial.

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.

Data Engineering With Aws

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Author : Gareth Eagar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-10-31

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Stay up to date with a comprehensive revised chapter on Data Governance Build modern data platforms with a new section covering transactional data lakes and data mesh Book DescriptionThis book, authored by a Senior Data Architect with 25 years of experience, helps you gain expertise in the AWS ecosystem for data engineering. This revised edition updates every chapter to cover the latest AWS services and features, provides a refreshed view on data governance, and introduces a new section on building modern data platforms. You will learn how to implement a data mesh, work with open-table formats such as Apache Iceberg, and apply DataOps practices for automation and observability. You will begin by exploring core concepts and essential AWS tools used by data engineers, along with modern data management approaches. You will then design and build data pipelines, review raw data sources, transform data, and understand how it is consumed by various stakeholders. The book also covers data governance, populating data marts and warehouses, and how a data lakehouse fits into the architecture. You will explore AWS tools for analysis, SQL queries, visualizations, and learn how AI and machine learning generate insights from data. Later chapters cover transactional data lakes, data meshes, and building a complete AWS data platform. By the end, you will be able to confidently implement data engineering pipelines on AWS. *Email sign-up and proof of purchase requiredWhat you will learn Seamlessly 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 Load data into a Redshift data warehouse and run queries with ease Visualize and explore data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Build transactional data lakes using Apache Iceberg with Amazon Athena Learn how a data mesh approach can be implemented on AWS 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.

Amazon Redshift Cookbook

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Author : Shruti Worlikar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-04-25

Set up a petabyte-scale, cloud-based data warehouse that is burstable and built to scale for end-to-end analytical solutions Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Learn how to translate familiar data warehousing concepts into Redshift implementation Use impressive Redshift features to optimize development, productionizing, and operation processes Find out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queries Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAmazon Redshift Cookbook offers comprehensive guidance for leveraging AWS's fully managed cloud data warehousing service. Whether you're building new data warehouse workloads or migrating traditional on-premises platforms to the cloud, this essential resource delivers proven implementation strategies. Written by AWS specialists, these easy-to-follow recipes will equip you with the knowledge to successfully implement Amazon Redshift-based data analytics solutions using established best practices. The book focuses on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. The book covers recipes to help you take full advantage of Redshift's columnar architecture and managed services. You'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, helping you minimize the operational effort that you invest in managing regular ETL pipelines and ensuring the timely and accurate refreshing of your data warehouse. By the end of the Redshift book, you'll be able to implement a Redshift-based data analytics solution by adopting best-practice approaches for solving commonly faced problems. *Email sign-up and proof of purchase requiredWhat you will learn Integrate data warehouses with data lakes using AWS features Create end-to-end analytical solutions from sourcing to consumption Utilize Redshift's security for strict business requirements Apply architectural insights with analytical recipes Discover big data best practices for managed solutions Enable data sharing for data mesh and hub-and-spoke architectures Explore Redshift ML and generative AI with Amazon Q Who this book is for This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, including data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, as well as cloud concepts and familiarity with Redshift is beneficial.

Actionable Insights With Amazon Quicksight

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Author : Manos Samatas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-01-28

Actionable Insights With Amazon Quicksight written by Manos Samatas 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 2022-01-28 with Computers categories.

Build interactive dashboards and storytelling reports at scale with the cloud-native BI tool that integrates embedded analytics and ML-powered insights effortlessly Key FeaturesExplore Amazon QuickSight, manage data sources, and build and share dashboardsLearn best practices from an AWS certified big data solutions architect Manage and monitor dashboards using the QuickSight API and other AWS services such as Amazon CloudTrailBook Description Amazon Quicksight is an exciting new visualization that rivals PowerBI and Tableau, bringing several exciting features to the table – but sadly, there aren't many resources out there that can help you learn the ropes. This book seeks to remedy that with the help of an AWS-certified expert who will help you leverage its full capabilities. After learning QuickSight's fundamental concepts and how to configure data sources, you'll be introduced to the main analysis-building functionality of QuickSight to develop visuals and dashboards, and explore how to develop and share interactive dashboards with parameters and on-screen controls. You'll dive into advanced filtering options with URL actions before learning how to set up alerts and scheduled reports. Next, you'll familiarize yourself with the types of insights before getting to grips with adding ML insights such as forecasting capabilities, analyzing time series data, adding narratives, and outlier detection to your dashboards. You'll also explore patterns to automate operations and look closer into the API actions that allow us to control settings. Finally, you'll learn advanced topics such as embedded dashboards and multitenancy. By the end of this book, you'll be well-versed with QuickSight's BI and analytics functionalities that will help you create BI apps with ML capabilities. What you will learnUnderstand the wider AWS analytics ecosystem and how QuickSight fits within itSet up and configure data sources with Amazon QuickSightInclude custom controls and add interactivity to your BI application using parametersAdd ML insights such as forecasting, anomaly detection, and narrativesExplore patterns to automate operations using QuickSight APIsCreate interactive dashboards and storytelling with Amazon QuickSightDesign an embedded multi-tenant analytics architectureFocus on data permissions and how to manage Amazon QuickSight operationsWho this book is for This book is for business intelligence (BI) developers and data analysts who are looking to create interactive dashboards using data from Lake House on AWS with Amazon QuickSight. It will also be useful for anyone who wants to learn Amazon QuickSight in depth using practical, up-to-date examples. You will need to be familiar with general data visualization concepts before you get started with this book, however, no prior experience with Amazon QuickSight is required.

Progressive Architecture

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Author :
language : en
Publisher:
Release Date : 1990

Progressive Architecture written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Architectural drawing categories.

Architecting A Modern Data Warehouse For Large Enterprises

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Author : Anjani Kumar
language : en
Publisher: Apress
Release Date : 2024-01-24

Architecting A Modern Data Warehouse For Large Enterprises written by Anjani Kumar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-24 with Computers categories.

Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines. What You Will Learn Understand the core concepts underlying modern data warehouses Design and build cloud-native data warehouses Gain a practical approach to architecting and building data warehouses on Azure and AWS Implement modern data warehousing components such as Data Mesh, Data Lake, Delta Lake, and Lakehouse Process data through pandas and evaluate your model’s performance using metrics such as F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Experienced developers, cloud architects, and technology enthusiasts looking to build cloud-based modern data warehouses using Azure and AWS

Learn Data Mesh

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Author : Diego Rodrigues
language : en
Publisher: Studiod21
Release Date : 2025-10-29

Learn Data Mesh written by Diego Rodrigues and has been published by Studiod21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-29 with Business & Economics categories.

LEARN DATA MESH Master Distributed Governance and Modern Data Architecture This book is recommended for students and professionals seeking practical mastery in Data Mesh, distributed data architecture, multi-cloud integration, governance, and data pipeline automation in modern corporate environments. The content covers everything from the fundamentals of Data Mesh to its implementation with leading frameworks such as AWS Lake Formation, Azure Synapse, Databricks, Google BigQuery, Kafka, Apache Spark, Kubernetes, Airflow, dbt, Snowflake, Terraform, Prometheus, Grafana, and ELK Stack. Explore strategies for data decentralization, domain-oriented architecture, data product management, CI/CD pipeline automation, multidisciplinary team integration, monitoring, security, and scalability for high-performance data environments. Learn to build robust solutions, integrate heterogeneous sources, implement data governance practices, S3, IAM, deployment automation, observability, and compliance with LGPD/GDPR. You will learn: * Essential concepts and pillars of Data Mesh: domains, data products, interoperability, and self-service platforms * Reference architecture with AWS, Azure, Google Cloud, Databricks, Snowflake, and Kubernetes * Orchestration of data pipelines with Apache Airflow, dbt, Spark, Kafka, and Terraform * Implementation of monitoring, observability, and automation with Prometheus, Grafana, and ELK Stack * Data integration in multi-cloud, container, and edge computing environments * Best practices in security, authentication, IAM, governance, and compliance * Deployment automation, backup, versioning, and troubleshooting in corporate environments By the end, you will be able to apply Data Mesh to create scalable, secure, auditable, and business-oriented data platforms, optimizing analytical and operational workflows in data-driven companies of any size. data mesh data architecture AWS Azure Google Cloud Databricks Snowflake pipelines Apache Airflow dbt Spark Kafka Kubernetes automation, CI/CD, data products observability grafana ELK LGPD GDPR security IAM integration backup governance compliance

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Modern Data Architecture On Aws

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