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Amazon Redshift Cookbook

Author: Shruti Worlikar
Publisher: Packt Publishing Ltd
ISBN: 1800561849
Category : Computers
Languages : en
Pages : 384

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Book Description
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.

Amazon Redshift Cookbook

Author: Shruti Worlikar
Publisher: Packt Publishing Ltd
ISBN: 1800561849
Category : Computers
Languages : en
Pages : 384

Get Book Here

Amazon Redshift Cookbook

Author: Shruti Worlikar
Publisher: Packt Publishing Ltd
ISBN: 1800561849
Category : Computers
Languages : en
Pages : 384

Get Book Here

Data Engineering with AWS Cookbook

Author: Trâm Ngọc Phạm
Publisher: Packt Publishing Ltd
ISBN: 1805126857
Category : Computers
Languages : en
Pages : 529

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Book Description
Master AWS data engineering services and techniques for orchestrating pipelines, building layers, and managing migrations Key Features Get up to speed with the different AWS technologies for data engineering Learn the different aspects and considerations of building data lakes, such as security, storage, and operations Get hands on with key AWS services such as Glue, EMR, Redshift, QuickSight, and Athena for practical learning Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPerforming data engineering with Amazon Web Services (AWS) combines AWS's scalable infrastructure with robust data processing tools, enabling efficient data pipelines and analytics workflows. This comprehensive guide to AWS data engineering will teach you all you need to know about data lake management, pipeline orchestration, and serving layer construction. Through clear explanations and hands-on exercises, you’ll master essential AWS services such as Glue, EMR, Redshift, QuickSight, and Athena. Additionally, you’ll explore various data platform topics such as data governance, data quality, DevOps, CI/CD, planning and performing data migration, and creating Infrastructure as Code. As you progress, you will gain insights into how to enrich your platform and use various AWS cloud services such as AWS EventBridge, AWS DataZone, and AWS SCT and DMS to solve data platform challenges. Each recipe in this book is tailored to a daily challenge that a data engineer team faces while building a cloud platform. By the end of this book, you will be well-versed in AWS data engineering and have gained proficiency in key AWS services and data processing techniques. You will develop the necessary skills to tackle large-scale data challenges with confidence.What you will learn Define your centralized data lake solution, and secure and operate it at scale Identify the most suitable AWS solution for your specific needs Build data pipelines using multiple ETL technologies Discover how to handle data orchestration and governance Explore how to build a high-performing data serving layer Delve into DevOps and data quality best practices Migrate your data from on-premises to AWS Who this book is for If you're involved in designing, building, or overseeing data solutions on AWS, this book provides proven strategies for addressing challenges in large-scale data environments. Data engineers as well as big data professionals looking to enhance their understanding of AWS features for optimizing their workflow, even if they're new to the platform, will find value. Basic familiarity with AWS security (users and roles) and command shell is recommended.

DynamoDB Cookbook

Author: Tanmay Deshpande
Publisher: Packt Publishing Ltd
ISBN: 1784391093
Category : Computers
Languages : en
Pages : 266

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Book Description
Over 90 hands-on recipes to design Internet scalable web and mobile applications with Amazon DynamoDB About This Book Construct top-notch mobile and web applications with the Internet scalable NoSQL database and host it on cloud Integrate your applications with other AWS services like AWS EMR, AWS S3, AWS Redshift, and AWS CloudSearch etc. in order to achieve a one-stop application stack Step-by-step implementation guide that provides real-world use with hands-on recipes Who This Book Is For This book is intended for those who have a basic understanding of AWS services and want to take their knowledge to the next level by getting their hands dirty with coding recipes in DynamoDB. What You Will Learn Design DynamoDB tables to achieve high read and write throughput Discover best practices like caching, exponential back-offs and auto-retries, storing large items in AWS S3, storing compressed data etc. Effectively use DynamoDB Local in order to make your development smooth and cost effective Implement cost effective best practices to reduce the burden of DynamoDB charges Create and maintain secondary indexes to support improved data access Integrate various other AWS services like AWS EMR, AWS CloudSearch, AWS Pipeline etc. with DynamoDB In Detail AWS DynamoDB is an excellent example of a production-ready NoSQL database. In recent years, DynamoDB has been able to attract many customers because of its features like high-availability, reliability and infinite scalability. DynamoDB can be easily integrated with massive data crunching tools like Hadoop /EMR, which is an essential part of this data-driven world and hence it is widely accepted. The cost and time-efficient design makes DynamoDB stand out amongst its peers. The design of DynamoDB is so neat and clean that it has inspired many NoSQL databases to simply follow it. This book will get your hands on some engineering best practices DynamoDB engineers use, which can be used in your day-to-day life to build robust and scalable applications. You will start by operating with DynamoDB tables and learn to manipulate items and manage indexes. You will also discover how to easily integrate applications with other AWS services like EMR, S3, CloudSearch, RedShift etc. A couple of chapters talk in detail about how to use DynamoDB as a backend database and hosting it on AWS ElasticBean. This book will also focus on security measures of DynamoDB as well by providing techniques on data encryption, masking etc. By the end of the book you'll be adroit in designing web and mobile applications using DynamoDB and host it on cloud. Style and approach An easy-to-follow guide, full of real-world examples, which takes you through the world of DynamoDB following a step-by-step, problem-solution based approach.

AWS Certified Database - Specialty (DBS-C01) Certification Guide

Author: Kate Gawron
Publisher: Packt Publishing Ltd
ISBN: 1803240059
Category : Computers
Languages : en
Pages : 472

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Book Description
Pass the AWS Certified Database- Specialty Certification exam with the help of practice tests Key Features • Understand different AWS database technologies and when to use them • Master the management and administration of AWS databases using both the console and command line • Complete, up-to-date coverage of DBS-C01 exam objectives to pass it on the first attempt Book Description The AWS Certified Database – Specialty certification is one of the most challenging AWS certifications. It validates your comprehensive understanding of databases, including the concepts of design, migration, deployment, access, maintenance, automation, monitoring, security, and troubleshooting. With this guide, you'll understand how to use various AWS databases, such as Aurora Serverless and Global Database, and even services such as Redshift and Neptune. You'll start with an introduction to the AWS databases, and then delve into workload-specific database design. As you advance through the chapters, you'll learn about migrating and deploying the databases, along with database security techniques such as encryption, auditing, and access controls. This AWS book will also cover monitoring, troubleshooting, and disaster recovery techniques, before testing all the knowledge you've gained throughout the book with the help of mock tests. By the end of this book, you'll have covered everything you need to pass the DBS-C01 AWS certification exam and have a handy, on-the-job desk reference guide. What you will learn • Become familiar with the AWS Certified Database – Specialty exam format • Explore AWS database services and key terminology • Work with the AWS console and command line used for managing the databases • Test and refine performance metrics to make key decisions and reduce cost • Understand how to handle security risks and make decisions about database infrastructure and deployment • Enhance your understanding of the topics you've learned using real-world hands-on examples • Identify and resolve common RDS, Aurora, and DynamoDB issues Who this book is for This AWS certification book is for database administrators and IT professionals who perform complex big data analysis as well as students looking to get AWS Database Specialty certified. A solid understanding of cloud computing, specifically AWS services, is a must. Knowledge of basic administration tasks such as logging in and running SQL queries will be helpful.

The Ultimate Power Query Cookbook for Power BI and Excel

Author: Dominick Raimato
Publisher: BPB Publications
ISBN: 9355517394
Category : Computers
Languages : en
Pages : 522

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Book Description
Novice or expert, learn to simplify and optimize data transformations KEY FEATURES ● Practical approaches to cleansing, connecting and transforming data in Power Query. ● Real-life examples that readers can apply to their own work. ● Master Power Query for Excel and Power BI with step-by-step recipes. DESCRIPTION “The Ultimate Power Query Cookbook for Power BI and Excel” serves up easy-to-follow recipes that transform data into meaningful insights. You will learn to clean messy files, combine datasets, and even use AI magic to Power BI and Excel. This book will walk you through the basics of getting connected to data with Power Query. You will understand how to ingest data from files, folders, databases, websites, APIs, and other third party sources. Once connected, you will learn how to transform the data so it is ready for your use. We will clean up columns, filter, replace, extract, and classify data in Power Query to meet your needs. The book offers over 100 practical recipes, ensuring you understand each step with clear explanations and examples. Lastly, we will go over advanced techniques to help optimize and simplify your transformations allowing fast refreshes all while helping you manage them in the future. This book will help you know how to apply these techniques and recipes to your data all while understanding the implications of making certain decisions. This will enable you to have better conversations with other data professionals who are providing data for your use. WHAT YOU WILL LEARN ● Learn to connect to files, databases, and third-party services. ● Manage data types and formats to optimize storage. ● Transform, create, and manipulate queries. ● Combine, merge, filter, and cleanse queries. ● Integrate artificial intelligence to accelerate insights. ● Perform complex and scalable transformations. WHO THIS BOOK IS FOR Novice or expert, this book is designed for all Excel users, data analysts, Power BI power users, business professionals and data enthusiasts to get the most out of your data solutions when transforming your data in Power Query. TABLE OF CONTENTS 1. Introduction to Power Query 2. Connect to File-Based Data Sources 3. Connect to Web-Based Data Sources 4. Connect to Database Sources 5. Connect to Third-Party Data Sources 6. Managing Data Types 7. Transforming Columns 8. Cleansing Columns 9. Creating New Columns 10. Combining and Manipulating Queries 11. Using Python, R, and AI 12. Indexing 13. Parameters 14. Functions 15. Advanced Web Connections 16. Manipulating Supporting Queries

Tableau 2019.x Cookbook

Author: Dmitry Anoshin
Publisher: Packt Publishing Ltd
ISBN: 1789535352
Category : Computers
Languages : en
Pages : 657

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Book Description
Perform advanced dashboard, visualization, and analytical techniques with Tableau Desktop, Tableau Prep, and Tableau Server Key FeaturesUnique problem-solution approach to aid effective business decision-makingCreate interactive dashboards and implement powerful business intelligence solutionsIncludes best practices on using Tableau with modern cloud analytics servicesBook Description Tableau has been one of the most popular business intelligence solutions in recent times, thanks to its powerful and interactive data visualization capabilities. Tableau 2019.x Cookbook is full of useful recipes from industry experts, who will help you master Tableau skills and learn each aspect of Tableau's ecosystem. This book is enriched with features such as Tableau extracts, Tableau advanced calculations, geospatial analysis, and building dashboards. It will guide you with exciting data manipulation, storytelling, advanced filtering, expert visualization, and forecasting techniques using real-world examples. From basic functionalities of Tableau to complex deployment on Linux, you will cover it all. Moreover, you will learn advanced features of Tableau using R, Python, and various APIs. You will learn how to prepare data for analysis using the latest Tableau Prep. In the concluding chapters, you will learn how Tableau fits the modern world of analytics and works with modern data platforms such as Snowflake and Redshift. In addition, you will learn about the best practices of integrating Tableau with ETL using Matillion ETL. By the end of the book, you will be ready to tackle business intelligence challenges using Tableau's features. What you will learnUnderstand the basic and advanced skills of Tableau DesktopImplement best practices of visualization, dashboard, and storytellingLearn advanced analytics with the use of build in statisticsDeploy the multi-node server on Linux and WindowsUse Tableau with big data sources such as Hadoop, Athena, and SpectrumCover Tableau built-in functions for forecasting using R packagesCombine, shape, and clean data for analysis using Tableau PrepExtend Tableau’s functionalities with REST API and R/PythonWho this book is for Tableau 2019.x Cookbook is for data analysts, data engineers, BI developers, and users who are looking for quick solutions to common and not-so-common problems faced while using Tableau products. Put each recipe into practice by bringing the latest offerings of Tableau 2019.x to solve real-world analytics and business intelligence challenges. Some understanding of BI concepts and Tableau is required.

Time Series Analysis with Python Cookbook

Author: Tarek A. Atwan
Publisher: Packt Publishing Ltd
ISBN: 1801071268
Category : Computers
Languages : en
Pages : 630

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Book Description
Perform time series analysis and forecasting confidently with this Python code bank and reference manual Key Features • Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms • Learn different techniques for evaluating, diagnosing, and optimizing your models • Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities Book Description Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch. Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book. What you will learn • Understand what makes time series data different from other data • Apply various imputation and interpolation strategies for missing data • Implement different models for univariate and multivariate time series • Use different deep learning libraries such as TensorFlow, Keras, and PyTorch • Plot interactive time series visualizations using hvPlot • Explore state-space models and the unobserved components model (UCM) • Detect anomalies using statistical and machine learning methods • Forecast complex time series with multiple seasonal patterns Who this book is for This book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

Learn Amazon SageMaker

Author: Julien Simon
Publisher: Packt Publishing Ltd
ISBN: 1801814155
Category : Computers
Languages : en
Pages : 554

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Book Description
Swiftly build and deploy machine learning models without managing infrastructure and boost productivity using the latest Amazon SageMaker capabilities such as Studio, Autopilot, Data Wrangler, Pipelines, and Feature Store Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerOptimize the accuracy, cost, and fairness of your modelsCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Book Description Amazon SageMaker enables you to quickly build, train, and deploy machine learning models at scale without managing any infrastructure. It helps you focus on the machine learning problem at hand and deploy high-quality models by eliminating the heavy lifting typically involved in each step of the ML process. This second edition will help data scientists and ML developers to explore new features such as SageMaker Data Wrangler, Pipelines, Clarify, Feature Store, and much more. You'll start by learning how to use various capabilities of SageMaker as a single toolset to solve ML challenges and progress to cover features such as AutoML, built-in algorithms and frameworks, and writing your own code and algorithms to build ML models. The book will then show you how to integrate Amazon SageMaker with popular deep learning libraries, such as TensorFlow and PyTorch, to extend the capabilities of existing models. You'll also see how automating your workflows can help you get to production faster with minimum effort and at a lower cost. Finally, you'll explore SageMaker Debugger and SageMaker Model Monitor to detect quality issues in training and production. By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learnBecome well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and natural language processing (NLP) models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is for This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.

Actionable Insights with Amazon QuickSight

Author: Manos Samatas
Publisher: Packt Publishing Ltd
ISBN: 1801072000
Category : Computers
Languages : en
Pages : 242

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Book Description
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.