By the end of this AWS book, you’ll be able to build and deploy your own Kinesis data pipelines with Kinesis Data Streams (KDS), Kinesis Data Firehose (KFH), Kinesis Video Streams (KVS), and Kinesis Data Analytics (KDA).

Author: Tarik Makota

Publisher: Packt Publishing Ltd

ISBN: 9781800564336

Category: Computers

Page: 314

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Explore Kinesis managed services such as Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and Kinesis Video Streams with the help of practical use cases Key FeaturesGet well versed with the capabilities of Amazon KinesisExplore the monitoring, scaling, security, and deployment patterns of various Amazon Kinesis servicesLearn how other Amazon Web Services and third-party applications such as Splunk can be used as destinations for Kinesis dataBook Description Amazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services. This data streaming service provides APIs and client SDKs that enable you to produce and consume data at scale. Scalable Data Streaming with Amazon Kinesis begins with a quick overview of the core concepts of data streams, along with the essentials of the AWS Kinesis landscape. You'll then explore the requirements of the use case shown through the book to help you get started and cover the key pain points encountered in the data stream life cycle. As you advance, you'll get to grips with the architectural components of Kinesis, understand how they are configured to build data pipelines, and delve into the applications that connect to them for consumption and processing. You'll also build a Kinesis data pipeline from scratch and learn how to implement and apply practical solutions. Moving on, you'll learn how to configure Kinesis on a cloud platform. Finally, you’ll learn how other AWS services can be integrated into Kinesis. These services include Redshift, Dynamo Database, AWS S3, Elastic Search, and third-party applications such as Splunk. By the end of this AWS book, you’ll be able to build and deploy your own Kinesis data pipelines with Kinesis Data Streams (KDS), Kinesis Data Firehose (KFH), Kinesis Video Streams (KVS), and Kinesis Data Analytics (KDA). What you will learnGet to grips with data streams, decoupled design, and real-time stream processingUnderstand the properties of KFH that differentiate it from other Kinesis servicesMonitor and scale KDS using CloudWatch metricsSecure KDA with identity and access management (IAM)Deploy KVS as infrastructure as code (IaC)Integrate services such as Redshift, Dynamo Database, and Splunk into KinesisWho this book is for This book is for solutions architects, developers, system administrators, data engineers, and data scientists looking to evaluate and choose the most performant, secure, scalable, and cost-effective data streaming technology to overcome their data ingestion and processing challenges on AWS. Prior knowledge of cloud architectures on AWS, data streaming technologies, and architectures is expected.

The Kinesis Scaling Utility (https:// github.com/awslabs/amazon-kinesis-scaling-utils) is an open source, Java-based utility that scales Amazon Kinesis Data Streams shard counts up or down in a hands-off automated manner. As the stream ...

Author: Tarik Makota

Publisher: Packt Publishing Ltd

ISBN: 9781800564336

Category: Computers

Page: 314

View: 110

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Explore Kinesis managed services such as Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and Kinesis Video Streams with the help of practical use cases Key FeaturesGet well versed with the capabilities of Amazon KinesisExplore the monitoring, scaling, security, and deployment patterns of various Amazon Kinesis servicesLearn how other Amazon Web Services and third-party applications such as Splunk can be used as destinations for Kinesis dataBook Description Amazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services. This data streaming service provides APIs and client SDKs that enable you to produce and consume data at scale. Scalable Data Streaming with Amazon Kinesis begins with a quick overview of the core concepts of data streams, along with the essentials of the AWS Kinesis landscape. You'll then explore the requirements of the use case shown through the book to help you get started and cover the key pain points encountered in the data stream life cycle. As you advance, you'll get to grips with the architectural components of Kinesis, understand how they are configured to build data pipelines, and delve into the applications that connect to them for consumption and processing. You'll also build a Kinesis data pipeline from scratch and learn how to implement and apply practical solutions. Moving on, you'll learn how to configure Kinesis on a cloud platform. Finally, you’ll learn how other AWS services can be integrated into Kinesis. These services include Redshift, Dynamo Database, AWS S3, Elastic Search, and third-party applications such as Splunk. By the end of this AWS book, you’ll be able to build and deploy your own Kinesis data pipelines with Kinesis Data Streams (KDS), Kinesis Data Firehose (KFH), Kinesis Video Streams (KVS), and Kinesis Data Analytics (KDA). What you will learnGet to grips with data streams, decoupled design, and real-time stream processingUnderstand the properties of KFH that differentiate it from other Kinesis servicesMonitor and scale KDS using CloudWatch metricsSecure KDA with identity and access management (IAM)Deploy KVS as infrastructure as code (IaC)Integrate services such as Redshift, Dynamo Database, and Splunk into KinesisWho this book is for This book is for solutions architects, developers, system administrators, data engineers, and data scientists looking to evaluate and choose the most performant, secure, scalable, and cost-effective data streaming technology to overcome their data ingestion and processing challenges on AWS. Prior knowledge of cloud architectures on AWS, data streaming technologies, and architectures is expected.

... 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: 641

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Get more from your data with Amazon Athena's ease-of-use, interactive performance, and pay-per-query pricing Key FeaturesExplore the promising capabilities of Amazon Athena and Athena's Query Federation SDKUse Athena to prepare data for common machine learning activitiesCover best practices for setting up connectivity between your application and Athena and security considerationsBook 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 learnSecure and manage the cost of querying your dataUse Athena ML and User Defined Functions (UDFs) to add advanced features to your reportsWrite your own Athena Connector to integrate with a custom data sourceDiscover your datasets on S3 using AWS Glue CrawlersIntegrate Amazon Athena into your applicationsSetup Identity and Access Management (IAM) policies to limit access to tables and databases in Glue Data CatalogAdd an Amazon SageMaker Notebook to your Athena queriesGet to grips with using Athena for ETL pipelinesWho 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.

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: 881

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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 Description Written 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.

However, if operators maintain large state spaces, frequent checkpointing may significantly impact stream throughput. ... Danny Gagne, and Rajeev Chakrabarti, Scalable • Data Streaming with Amazon Kinesis (Packt, 2021) • Sean T. Allen, ...

Author: Ian Gorton

Publisher: "O'Reilly Media, Inc."

ISBN: 9781098106034

Category: Computers

Page: 339

View: 277

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In many systems, scalability becomes the primary driver as the user base grows. Attractive features and high utility breed success, which brings more requests to handle and more data to manage. But organizations reach a tipping point when design decisions that made sense under light loads suddenly become technical debt. This practical book covers design approaches and technologies that make it possible to scale an application quickly and cost-effectively. Author Ian Gorton takes software architects and developers through the foundational principles of distributed systems. You'll explore the essential ingredients of scalable solutions, including replication, state management, load balancing, and caching. Specific chapters focus on the implications of scalability for databases, microservices, and event-based streaming systems. You will focus on: Foundations of scalable systems: Learn basic design principles of scalability, its costs, and architectural tradeoffs Designing scalable services: Dive into service design, caching, asynchronous messaging, serverless processing, and microservices Designing scalable data systems: Learn data system fundamentals, NoSQL databases, and eventual consistency versus strong consistency Designing scalable streaming systems: Explore stream processing systems and scalable event-driven processing

Amazon Kinesis Data Streams is a scalable service that scales elastically for near-real-time processing of streaming big data. The service stores large streams of data (gigabytes of data per second) in durable, consistent storage, ...

Author: Asif Abbasi

Publisher: John Wiley & Sons

ISBN: 9781119819455

Category: Computers

Page: 416

View: 776

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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.

Amazon Elastic MapReduce (EMR) provides the Hadoop framework on Amazon EC2 and offers a wide range of Hadoop-related tools. • Amazon Kinesis is a managed service for real-time processing of streaming big data (throughput scaling from ...

Author: Hui-Huang Hsu

Publisher: Morgan Kaufmann

ISBN: 9780128096253

Category: Computers

Page: 326

View: 332

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Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Contains contributions from noted scholars in computer science and electrical engineering from around the globe Provides a broad overview of recent developments in sensor collected intelligence Edited by a team comprised of leading thinkers in big data analytics

Because of these scaling limits, AWS API Gateway and Lambda are not a good fit for APIs with extremely spiky traffic. ... 6.2.1 Kinesis Data Streams Amazon's Kinesis Data Streams is a fully managed and massively scalable service that ...

Author: Peter Sbarski

Publisher: Simon and Schuster

ISBN: 9781638354024

Category: Computers

Page: 256

View: 687

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Design low-maintenance systems using pre-built cloud services! Bring down costs, automate time-consuming ops tasks, and scale on demand. In Serverless Architectures on AWS, Second Edition you will learn: First steps with serverless computing The principles of serverless design Important patterns and architectures How successfully companies have implemented serverless Real-world architectures and their tradeoffs Serverless Architectures on AWS, Second Edition teaches you how to design serverless systems. You’ll discover the principles behind serverless architectures, and explore real-world case studies where companies used serverless architectures for their products. You won’t just master the technical essentials—the book contains extensive coverage of balancing tradeoffs and making essential technical decisions. This new edition has been fully updated with new chapters covering current best practice, example architectures, and full coverage of the latest changes to AWS. About the technology Maintaining server hardware and software can cost a lot of time and money. Unlike traditional data center infrastructure, serverless architectures offload core tasks like data storage and hardware management to pre-built cloud services. Better yet, you can combine your own custom AWS Lambda functions with other serverless services to create features that automatically start and scale on demand, reduce hosting cost, and simplify maintenance. About the book In Serverless Architectures with AWS, Second Edition you’ll learn how to design serverless systems using Lambda and other services on the AWS platform. You’ll explore event-driven computing and discover how others have used serverless designs successfully. This new edition offers real-world use cases and practical insights from several large-scale serverless systems. Chapters on innovative serverless design patterns and architectures will help you become a complete cloud professional. What's inside First steps with serverless computing The principles of serverless design Important patterns and architectures Real-world architectures and their tradeoffs About the reader For server-side and full-stack software developers. About the author Peter Sbarski is VP of Education and Research at A Cloud Guru. Yan Cui is an independent AWS consultant and educator. Ajay Nair is one of the founding members of the AWS Lambda team. Table of Contents PART 1 FIRST STEPS 1 Going serverless 2 First steps to serverless 3 Architectures and patterns PART 2 USE CASES 4 Yubl: Architecture highlights, lessons learned 5 A Cloud Guru: Architecture highlights, lessons learned 6 Yle: Architecture highlights, lessons learned PART 3 PRACTICUM 7 Building a scheduling service for ad hoc tasks 8 Architecting serverless parallel computing 9 Code Developer University PART 4 THE FUTURE 10 Blackbelt Lambda 11 Emerging practices

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: 685

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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 FeaturesBuild serverless applications with AWS Lambda, AWS CloudFormation and AWS CloudWatchPerform data analytics and natural language processing(NLP)on the AWS serverless platformExplore various design patterns and best practices involved in serverless computingBook 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 learnServerless computing in AWS and explore services with other cloudsDevelop full-stack apps with API Gateway, Cognito, Lambda and DynamoDBWeb hosting with S3, CloudFront, Route 53 and AWS Certificate ManagerSQS and SNS for effective communication between microservices Monitoring and troubleshooting with CloudWatch logs and metrics Explore Kinesis Streams, Amazon ML models and Alexa Skills KitWho 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.

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: 232

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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