Scalable Data Streaming with Amazon Kinesis

Scalable Data Streaming with Amazon Kinesis

This practical guide takes a hands-on approach to implementation and associated methodologies to have you up and running with all that Amazon Kinesis has to offer.

Author: Tarik Makota

Publisher: Packt Publishing Ltd

ISBN: 9781800564336

Category: Computers

Page: 314

View: 564

This practical guide takes a hands-on approach to implementation and associated methodologies to have you up and running with all that Amazon Kinesis has to offer. You’ll work with use cases and practical examples to be able to ingest, process, analyze, and stream real-time data in no time.
Categories: Computers

Scalable Data Streaming with Amazon Kinesis

Scalable Data Streaming with Amazon Kinesis

scaling. with. Amazon. Kinesis. Data. Streams. When we design the data pipelines, we want to make each stage of the pipeline reliable and scalable. As the volume or velocity of data spikes, the system should adapt and scale, so data ...

Author: Tarik Makota

Publisher: Packt Publishing Ltd

ISBN: 9781800564336

Category: Computers

Page: 314

View: 535

This practical guide takes a hands-on approach to implementation and associated methodologies to have you up and running with all that Amazon Kinesis has to offer. You’ll work with use cases and practical examples to be able to ingest, process, analyze, and stream real-time data in no time.
Categories: Computers

Data Engineering with AWS

Data Engineering with AWS

Scalable Data Streaming with Amazon Kinesis Tarik Makota, Brian Maguire, Danny Gagne, Rajeev Chakrabarti ISBN: 9781800565401 • Get to grips with data streams, decoupled design, and real-time stream processing • Understand the properties ...

Author: Gareth Eagar

Publisher: Packt Publishing Ltd

ISBN: 9781800569041

Category: Computers

Page: 482

View: 670

Start your AWS data engineering journey with this easy-to-follow, hands-on guide and get to grips with foundational concepts through to building data engineering pipelines using AWS 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 Book Description Knowing how to architect and implement complex data pipelines is a highly sought-after skill. Data engineers are responsible for building these pipelines that ingest, transform, and join raw datasets - creating new value from the data in the process. Amazon Web Services (AWS) offers a range of tools to simplify a data engineer's job, making it the preferred platform for performing data engineering tasks. This book will take you 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. The book also teaches you 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 who is new to data engineering and 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 is not needed. Familiarity with the AWS console and core services is also useful but not necessary.
Categories: Computers

Serverless Analytics with Amazon Athena

Serverless Analytics with Amazon Athena

... for scaling, model optimization, model debugging, and cost optimization • Automate deployment tasks in a variety of configurations using SDK and several automation tools Scalable Data Streaming with Amazon Kinesis Tarik Makota, ...

Author: Anthony Virtuoso

Publisher: Packt Publishing Ltd

ISBN: 9781800567863

Category: Computers

Page: 438

View: 417

Get more from your data with Amazon Athena's ease-of-use, interactive performance, and pay-per-query pricing Key Features Explore the promising capabilities of Amazon Athena and Athena's Query Federation SDK Use Athena to prepare data for common machine learning activities Cover best practices for setting up connectivity between your application and Athena and security considerations Book Description Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using SQL, without needing to manage any infrastructure. This book begins with an overview of the serverless analytics experience offered by Athena and teaches you how to build and tune an S3 Data Lake using Athena, including how to structure your tables using open-source file formats like Parquet. You'll learn how to build, secure, and connect to a data lake with Athena and Lake Formation. Next, you'll cover key tasks such as ad hoc data analysis, working with ETL pipelines, monitoring and alerting KPI breaches using CloudWatch Metrics, running customizable connectors with AWS Lambda, and more. Moving on, you'll work through easy integrations, troubleshooting and tuning common Athena issues, and the most common reasons for query failure. You will also review tips to help diagnose and correct failing queries in your pursuit of operational excellence. Finally, you'll explore advanced concepts such as Athena Query Federation and Athena ML to generate powerful insights without needing to touch a single server. By the end of this book, you'll be able to build and use a data lake with Amazon Athena to add data-driven features to your app and perform the kind of ad hoc data analysis that often precedes many of today's ML modeling exercises. What you will learn Secure and manage the cost of querying your data Use Athena ML and User Defined Functions (UDFs) to add advanced features to your reports Write your own Athena Connector to integrate with a custom data source Discover your datasets on S3 using AWS Glue Crawlers Integrate Amazon Athena into your applications Setup Identity and Access Management (IAM) policies to limit access to tables and databases in Glue Data Catalog Add an Amazon SageMaker Notebook to your Athena queries Get to grips with using Athena for ETL pipelines Who this book is for Business intelligence (BI) analysts, application developers, and system administrators who are looking to generate insights from an ever-growing sea of data while controlling costs and limiting operational burden, will find this book helpful. Basic SQL knowledge is expected to make the most out of this book.
Categories: Computers

Serverless Programming Cookbook

Serverless Programming Cookbook

Kinesis. data. stream. (AWS. CLI). In the previous chapter, we learned how we can use SQS for messaging. SQS is good for standard data transfer ... KDS is a highly scalable data streaming service that is used for such use cases.

Author: Heartin Kanikathottu

Publisher: Packt Publishing Ltd

ISBN: 9781788621533

Category: Computers

Page: 490

View: 380

Build, secure, and deploy real-world serverless applications in AWS and peek into the serverless cloud offerings from Azure, Google Cloud, and IBM Cloud Key Features Build serverless applications with AWS Lambda, AWS CloudFormation and AWS CloudWatch Perform data analytics and natural language processing(NLP)on the AWS serverless platform Explore various design patterns and best practices involved in serverless computing Book Description Managing physical servers will be a thing of the past once you’re able to harness the power of serverless computing. If you’re already prepped with the basics of serverless computing, Serverless Programming Cookbook will help you take the next step ahead. This recipe-based guide provides solutions to problems you might face while building serverless applications. You'll begin by setting up Amazon Web Services (AWS), the primary cloud provider used for most recipes. The next set of recipes will cover various components to build a Serverless application including REST APIs, database, user management, authentication, web hosting, domain registration, DNS management, CDN, messaging, notifications and monitoring. The book also introduces you to the latest technology trends such as Data Streams, Machine Learning and NLP. You will also see patterns and practices for using various services in a real world application. Finally, to broaden your understanding of Serverless computing, you'll also cover getting started guides for other cloud providers such as Azure, Google Cloud Platform and IBM cloud. By the end of this book, you’ll have acquired the skills you need to build serverless applications efficiently using various cloud offerings. What you will learn Serverless computing in AWS and explore services with other clouds Develop full-stack apps with API Gateway, Cognito, Lambda and DynamoDB Web hosting with S3, CloudFront, Route 53 and AWS Certificate Manager SQS and SNS for effective communication between microservices Monitoring and troubleshooting with CloudWatch logs and metrics Explore Kinesis Streams, Amazon ML models and Alexa Skills Kit Who this book is for For developers looking for practical solutions to common problems while building a serverless application, this book provides helpful recipes. To get started with this intermediate-level book, knowledge of basic programming is a must.
Categories: Computers

Optimization Learning Algorithms and Applications

Optimization  Learning Algorithms and Applications

3.2 Distributed Data Streaming To achieve full horizontal scalability, it is fundamental that the destination is not a ... [5] used Amazon Kinesis, another example of a distributed event stream, to process data on the fly, stating that ...

Author: Ana I. Pereira

Publisher: Springer Nature

ISBN: 9783030918859

Category: Computers

Page: 704

View: 409

This book constitutes selected and revised papers presented at the First International Conference on Optimization, Learning Algorithms and Applications, OL2A 2021, held in Bragança, Portugal, in July 2021. Due to the COVID-19 pandemic the conference was held online. The 39 full papers and 13 short papers were thoroughly reviewed and selected from 134 submissions. They are organized in the topical sections on optimization theory; robotics; measurements with the internet of things; optimization in control systems design; deep learning; data visualization and virtual reality; health informatics; data analysis; trends in engineering education.
Categories: Computers

The Machine Learning Solutions Architect Handbook

The Machine Learning Solutions Architect Handbook

You have Kinesis Data Streams, Kinesis Firehose, AWS Managed Streaming for Kafka, and AWS Glue streaming for ... Kinesis Firehose supports the key requirements for scalable data ingestion: • Support for different data sources such as ...

Author: David Ping

Publisher: Packt Publishing Ltd

ISBN: 9781801070416

Category: Computers

Page: 442

View: 674

Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book Description When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you'll need to become one. You'll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You'll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. And finally, you'll get acquainted with AWS AI services and their applications in real-world use cases. By the end of this book, you'll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.
Categories: Computers

Design Patterns for Cloud Native Applications

Design Patterns for Cloud Native Applications

achieves reliable processing by periodically checkpointing data into durable storage. It is an ideal choice when use ... Amazon. Kinesis. Kinesis is a fully managed scalable stream-processing offering from AWS. It supports SQL-based and ...

Author: Kasun Indrasiri

Publisher: "O'Reilly Media, Inc."

ISBN: 9781492090687

Category: Computers

Page: 314

View: 868

With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development. You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step. Learn the fundamentals of cloud native applications Explore key cloud native communication, connectivity, and composition patterns Learn decentralized data management techniques Use event-driven architecture to build distributed and scalable cloud native applications Explore the most commonly used patterns for API management and consumption Examine some of the tools and technologies you'll need for building cloud native systems
Categories: Computers

AWS Certified Data Analytics Study Guide with Online Labs

AWS Certified Data Analytics Study Guide with Online Labs

Kinesis Data Firehose You can use Kinesis Data Firehose to process data from an Amazon Kinesis data stream. ... AWS Lambda is a fully managed, scalable service where customers pay for the computation time with zero administration.

Author: Asif Abbasi

Publisher: John Wiley & Sons

ISBN: 9781119819455

Category: Computers

Page: 416

View: 328

Virtual, hands-on learning labs allow you to apply your technical skills in realistic environments. So Sybex has bundled AWS labs from XtremeLabs with our popular AWS Certified Data Analytics Study Guide to give you the same experience working in these labs as you prepare for the Certified Data Analytics Exam that you would face in a real-life application. These labs in addition to the book are a proven way to prepare for the certification and for work as an AWS Data Analyst. AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is intended for individuals who perform in a data analytics-focused role. This UPDATED exam validates an examinee's comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data. It assesses an examinee's ability to define AWS data analytics services and understand how they integrate with each other; and explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing, and visualization. The book focuses on the following domains: • Collection • Storage and Data Management • Processing • Analysis and Visualization • Data Security This is your opportunity to take the next step in your career by expanding and validating your skills on the AWS cloud. AWS is the frontrunner in cloud computing products and services, and the AWS Certified Data Analytics Study Guide: Specialty exam will get you fully prepared through expert content, and real-world knowledge, key exam essentials, chapter review questions, and much more. Written by an AWS subject-matter expert, this study guide covers exam concepts, and provides key review on exam topics. Readers will also have access to Sybex's superior online interactive learning environment and test bank, including chapter tests, practice exams, a glossary of key terms, and electronic flashcards. And included with this version of the book, XtremeLabs virtual labs that run from your browser. The registration code is included with the book and gives you 6 months of unlimited access to XtremeLabs AWS Certified Data Analytics Labs with 3 unique lab modules based on the book.
Categories: Computers

AWS Certified Developer Associate Guide

AWS Certified Developer     Associate Guide

Amazon Kinesis Data Streams is designed to collect and process large streams of data records in real time. ... Kinesis Data Analytics is a managed and scalable AWS service that processes and analyzes real-time streaming data, ...

Author: Vipul Tankariya

Publisher: Packt Publishing Ltd

ISBN: 9781789613711

Category: Computers

Page: 812

View: 943

Learn from the AWS subject-matter experts, explore real-world scenarios, and pass the AWS Certified Developer – Associate exam Key Features This fast-paced guide will help you clear the AWS Certified Developer – Associate (DVA-C01) exam with confidence Gain valuable insights to design, develop, and deploy cloud-based solutions using AWS Develop expert core AWS skills with practice questions and mock tests Book Description This book will focus on the revised version of AWS Certified Developer Associate exam. The 2019 version of this exam guide includes all the recent services and offerings from Amazon that benefits developers. AWS Certified Developer - Associate Guide starts with a quick introduction to AWS and the prerequisites to get you started. Then, this book will describe about getting familiar with Identity and Access Management (IAM) along with Virtual private cloud (VPC). Next, this book will teach you about microservices, serverless architecture, security best practices, advanced deployment methods and more. Going ahead we will take you through AWS DynamoDB A NoSQL Database Service, Amazon Simple Queue Service (SQS) and CloudFormation Overview. Lastly, this book will help understand Elastic Beanstalk and will also walk you through AWS lambda. At the end of this book, we will cover enough topics, tips and tricks along with mock tests for you to be able to pass the AWS Certified Developer - Associate exam and develop as well as manage your applications on the AWS platform. What you will learn Create and manage users, groups, and permissions using AWS IAM services Create a secured VPC with Public and Private Subnets, NAC, and Security groups Launching your first EC2 instance, and working with it Handle application traffic with ELB and monitor AWS resources with CloudWatch Work with AWS storage services such as S3, Glacier, and CloudFront Get acquainted with AWS DynamoDB a NoSQL database service Use SWS to coordinate work across distributed application components Who this book is for This book is for IT professionals and developers looking to clear the AWS Certified Developer Associate 2019 exam. Developers looking to develop and manage their applications on the AWS platform will also find this book useful. No prior AWS experience is needed.
Categories: Computers