The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Moving from an on-premises environment into AWS is just the start of the journey towards cost optimisation. In this session we’ll look at a range of ways in which our customers can understand their costs and increase their return-on-investment: building the business case; selecting the right models for the right workloads; benefiting from tiered pricing aggregation; using data to drive the choice of AWS services; implementation of intelligent auto-scaling; and, where appropriate, re-platforming to make use of new architectural patterns such as Serverless.
Now that you have assembled the delivery team, it's time to gain insights from the methodology and the various tools that AWS uses to help customers migrate their Data Centres to AWS. This session highlights some of the key native AWS tools and services that organisations are using to migrate their DCs into the Cloud.
Speaker: Shane Baldacchino, Solutions Architect, Amazon Web Services
With AWS, you can choose the right storage service for the right use case. This session shows the range of AWS choices - object storage to block storage - that is available to you. We include specifics about real-world deployments from customers who are using Amazon S3, Amazon EBS, Amazon Glacier, and AWS Storage Gateway.
Moving from an on-premises environment into AWS is just the start of the journey towards cost optimisation. In this session we’ll look at a range of ways in which our customers can understand their costs and increase their return-on-investment: building the business case; selecting the right models for the right workloads; benefiting from tiered pricing aggregation; using data to drive the choice of AWS services; implementation of intelligent auto-scaling; and, where appropriate, re-platforming to make use of new architectural patterns such as Serverless.
Now that you have assembled the delivery team, it's time to gain insights from the methodology and the various tools that AWS uses to help customers migrate their Data Centres to AWS. This session highlights some of the key native AWS tools and services that organisations are using to migrate their DCs into the Cloud.
Speaker: Shane Baldacchino, Solutions Architect, Amazon Web Services
With AWS, you can choose the right storage service for the right use case. This session shows the range of AWS choices - object storage to block storage - that is available to you. We include specifics about real-world deployments from customers who are using Amazon S3, Amazon EBS, Amazon Glacier, and AWS Storage Gateway.
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum and optimizing your overall capital expense can be challenging. This session presents AWS features and services along with disaster recovery architectures that you can leverage when building highly available and disaster-resilient strategies.
AWS offers you the ability to add additional layers of security to your data at rest in the cloud, providing access control as well scalable and efficient encryption features. Flexible key management options allow you to choose whether to have AWS manage the encryption keys or to keep complete control over the keys yourself. In this session, you will learn how to secure data when using AWS services. We will discuss Key Management Service, S3, access controls, and database platform security features.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing and scale-out architecture to ensure compute resources grow with your dataset size, and columnar, direct-attached storage to dramatically reduce I/O time. Learn how top online retailer RetailMeNot moved their largest Vertica cluster on Amazon EC2 to Amazon Redshift. See how they gain insights from clickstream, location, merchant, marketing, and operational data across desktop and mobile properties.
Infographic: AWS vs Azure vs GCP: What's the best cloud platform for enterprise?Veritis Group, Inc
Infographic: AWS vs Azure vs GCP: What's the best cloud platform for enterprise?
Read more: https://www.veritis.com/blog/aws-vs-azure-vs-gcp-the-cloud-platform-of-your-choice/
For more training on AWS, visit: https://www.qa.com/amazon
AWS Loft | London - Deep Dive: Amazon RDS by Toby Knight, Manager Solutions Architecture, 18 April 2016
Cloud Migration, Application Modernization, and Security Tom Laszewski
As AWS continues to expand, enterprise customers are looking to our partner ecosystem to assist in migrating their workloads to the cloud. This session describes the challenges, lessons learned and best practices for large scale application migrations. We will use real examples from our consulting partners and AWS Professional Services to illustrate how to move workloads to the cloud while modernizing the associated applications to take advantage of AWS’ unique benefits. We will also dive into how to use an array of AWS services and features to improve a customer’s security posture as they are migrating and once they are up and running in the cloud
Learn the best practices and considerations for cost optimising your AWS environment. We will cover best practices for right sizing, scheduling instances to reduce costs, and finally, how you can save up to 75% on OnDemand costs using reserved instances.
(DVO315) Log, Monitor and Analyze your IT with Amazon CloudWatchAmazon Web Services
You may already know that you can use Amazon CloudWatch to view graphs of your AWS resources like Amazon Elastic Compute Cloud instances or Amazon Simple Storage Service. But, did you know that you can monitor your on-premises servers with Amazon CloudWatch Logs? Or, that you can integrate CloudWatch Logs with Elasticsearch for powerful visualization and analysis? This session will offer a tour of the latest monitoring and automation capabilities that we’ve added, how you can get even more done with Amazon CloudWatch.
Sensitive customer data needs to be protected throughout AWS. This session discusses the options available for encrypting data at rest in AWS. It focuses on several scenarios, including transparent AWS management of encryption keys on behalf of the customer to provide automated server-side encryption and customer key management using partner solutions or AWS CloudHSM. This session is helpful for anyone interested in protecting data stored in AWS.
講師: Xiaoyong Han, Solution Architect, AWS
Data collection and storage is a primary challenge for any big data architecture. In this webinar, gain a thorough understanding of AWS solutions for data collection and storage, and learn architectural best practices for applying those solutions to your projects. This session will also include a discussion of popular use cases and reference architectures. In this webinar, you will learn:
• Overview of the different types of data that customers are handling to drive high-scale workloads on AWS, and how to choose the best approach for your workload • Optimization techniques that improve performance and reduce the cost of data ingestion • Leveraging Amazon S3, Amazon DynamoDB, and Amazon Kinesis for storage and data collection
Big Data adoption success using AWS Big Data Services - Pop-up Loft TLV 2017Amazon Web Services
In today’s session we will share with you an overview of what the typical challenges when adoption Big Data are, and how the AWS Big Data platform allows you to tackle this challenges and leverage the right Analytical/Big Data solutions in order to become successful with your strategy (Whiteboard presentation)
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
In the event of a disaster, you need to be able to recover lost data quickly to ensure business continuity. For critical applications, keeping your time to recover and data loss to a minimum and optimizing your overall capital expense can be challenging. This session presents AWS features and services along with disaster recovery architectures that you can leverage when building highly available and disaster-resilient strategies.
AWS offers you the ability to add additional layers of security to your data at rest in the cloud, providing access control as well scalable and efficient encryption features. Flexible key management options allow you to choose whether to have AWS manage the encryption keys or to keep complete control over the keys yourself. In this session, you will learn how to secure data when using AWS services. We will discuss Key Management Service, S3, access controls, and database platform security features.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing and scale-out architecture to ensure compute resources grow with your dataset size, and columnar, direct-attached storage to dramatically reduce I/O time. Learn how top online retailer RetailMeNot moved their largest Vertica cluster on Amazon EC2 to Amazon Redshift. See how they gain insights from clickstream, location, merchant, marketing, and operational data across desktop and mobile properties.
Infographic: AWS vs Azure vs GCP: What's the best cloud platform for enterprise?Veritis Group, Inc
Infographic: AWS vs Azure vs GCP: What's the best cloud platform for enterprise?
Read more: https://www.veritis.com/blog/aws-vs-azure-vs-gcp-the-cloud-platform-of-your-choice/
For more training on AWS, visit: https://www.qa.com/amazon
AWS Loft | London - Deep Dive: Amazon RDS by Toby Knight, Manager Solutions Architecture, 18 April 2016
Cloud Migration, Application Modernization, and Security Tom Laszewski
As AWS continues to expand, enterprise customers are looking to our partner ecosystem to assist in migrating their workloads to the cloud. This session describes the challenges, lessons learned and best practices for large scale application migrations. We will use real examples from our consulting partners and AWS Professional Services to illustrate how to move workloads to the cloud while modernizing the associated applications to take advantage of AWS’ unique benefits. We will also dive into how to use an array of AWS services and features to improve a customer’s security posture as they are migrating and once they are up and running in the cloud
Learn the best practices and considerations for cost optimising your AWS environment. We will cover best practices for right sizing, scheduling instances to reduce costs, and finally, how you can save up to 75% on OnDemand costs using reserved instances.
(DVO315) Log, Monitor and Analyze your IT with Amazon CloudWatchAmazon Web Services
You may already know that you can use Amazon CloudWatch to view graphs of your AWS resources like Amazon Elastic Compute Cloud instances or Amazon Simple Storage Service. But, did you know that you can monitor your on-premises servers with Amazon CloudWatch Logs? Or, that you can integrate CloudWatch Logs with Elasticsearch for powerful visualization and analysis? This session will offer a tour of the latest monitoring and automation capabilities that we’ve added, how you can get even more done with Amazon CloudWatch.
Sensitive customer data needs to be protected throughout AWS. This session discusses the options available for encrypting data at rest in AWS. It focuses on several scenarios, including transparent AWS management of encryption keys on behalf of the customer to provide automated server-side encryption and customer key management using partner solutions or AWS CloudHSM. This session is helpful for anyone interested in protecting data stored in AWS.
講師: Xiaoyong Han, Solution Architect, AWS
Data collection and storage is a primary challenge for any big data architecture. In this webinar, gain a thorough understanding of AWS solutions for data collection and storage, and learn architectural best practices for applying those solutions to your projects. This session will also include a discussion of popular use cases and reference architectures. In this webinar, you will learn:
• Overview of the different types of data that customers are handling to drive high-scale workloads on AWS, and how to choose the best approach for your workload • Optimization techniques that improve performance and reduce the cost of data ingestion • Leveraging Amazon S3, Amazon DynamoDB, and Amazon Kinesis for storage and data collection
Big Data adoption success using AWS Big Data Services - Pop-up Loft TLV 2017Amazon Web Services
In today’s session we will share with you an overview of what the typical challenges when adoption Big Data are, and how the AWS Big Data platform allows you to tackle this challenges and leverage the right Analytical/Big Data solutions in order to become successful with your strategy (Whiteboard presentation)
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
The world is producing an ever-increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Database and Analytics on the AWS Cloud - AWS Innovate TorontoAmazon Web Services
Antoine Genereux, AWS Solutions Architect, takes us on a tour of database solutions available for the AWS Cloud, and powerful analytics and business intelligence reporting tools.
Antoine Genereux takes us on a detailed overview of the Database solutions available on the AWS Cloud, addressing the needs and requirements of customers at all levels. He also discusses Business Intelligence and Analytics solutions.
Big Data and Architectural Patterns on AWS - Pop-up Loft Tel AvivAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. John Pignata, AWS Startup Solutions Architect, will discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. He will provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
(BDT310) Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Presented by: Arie Leeuwesteijn, Principal Solutions Architect, Amazon Web Services
Customer Guest: Sander Kieft, Sanoma
February 2016 Webinar Series - Architectural Patterns for Big Data on AWSAmazon Web Services
With an ever-increasing set of technologies to process big data, organizations often struggle to understand how to build scalable and cost-effective big data applications.
In this webinar, we will simplify big data processing as a pipeline comprising various stages; and then show you how to choose the right technology for each stage based on criteria such as data structure, design patterns, and best practices.
Learning Objectives:
Understand key AWS Big Data services including S3, Amazon EMR, Kinesis, and Redshift
Learn architectural patterns for Big Data
Hear best practices for building Big Data applications on AWS
Who Should Attend:
Architects, developers and data scientists who are looking to start a Big Data initiative
AWS November Webinar Series - Architectural Patterns & Best Practices for Big...Amazon Web Services
The world is producing an ever-increasing volume, velocity, and variety of data. For many consumers, batch analytics is no longer enough; they need sub-second analysis on fast-moving data. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how?
If you missed this popular presentation at re:Invent, attend this webinar where we simplify big data processing as a pipeline comprising various stages: ingest, store, process, analyze & visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, and durability. Finally, we provide a reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems.
Learning Objectives:
Understand key AWS Big Data services including S3, Amazon EMR, Kinesis, and Redshift
Learn architectural patterns for Big Data
Hear best practices for building Big Data applications on AWS
Didn’t make it to re:Invent? Here’s another chance to attend this popular presentation
Who Should Attend:
Architects, developers and data scientists who are looking to start a Big Data initiative
AWS Webcast - Managing Big Data in the AWS Cloud_20140924Amazon Web Services
This presentation deck will cover specific services such as Amazon S3, Kinesis, Redshift, Elastic MapReduce, and DynamoDB, including their features and performance characteristics. It will also cover architectural designs for the optimal use of these services based on dimensions of your data source (structured or unstructured data, volume, item size and transfer rates) and application considerations - for latency, cost and durability. It will also share customer success stories and resources to help you get started.
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
2016 Utah Cloud Summit: Big Data Architectural Patterns and Best Practices on...1Strategy
In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Level: Intermediate
Speakers:
Tony Nguyen - Senior Consultant, ProServe, AWS
Hannah Marlowe - Consultant - Federal, AWS
Similar to AWS re:Invent 2016: Big Data Architectural Patterns and Best Practices on AWS (BDM201) (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. What to Expect from the Session
Big data challenges
Architectural principles
How to simplify big data processing
What technologies should you use?
• Why?
• How?
Reference architecture
Design patterns
8. Architectural Principles
Build decoupled systems
• Data → Store → Process → Store → Analyze → Answers
Use the right tool for the job
• Data structure, latency, throughput, access patterns
Leverage AWS managed services
• Scalable/elastic, available, reliable, secure, no/low admin
Use log-centric design patterns
• Immutable logs, materialized views
Be cost-conscious
• Big data ≠ big cost
9. Simplify Big Data Processing
COLLECT STORE PROCESS/
ANALYZE
CONSUME
Time to answer (Latency)
Throughput
Cost
13. Hot Warm Cold
Volume MB–GB GB–TB PB–EB
Item size B–KB KB–MB KB–TB
Latency ms ms, sec min, hrs
Durability Low–high High Very high
Request rate Very high High Low
Cost/GB $$-$ $-¢¢ ¢
Hot data Warm data Cold data
Data Characteristics: Hot, Warm, Cold
18. What About Amazon SQS?
• Decouple producers & consumers
• Persistent buffer
• Collect multiple streams
• No client ordering (Standard)
• FIFO queue preserves client
ordering
• No streaming MapReduce
• No parallel consumption
• Amazon SNS can publish to
multiple SNS subscribers
(queues or ʎ functions)
Publisher
Amazon SNS
topic
function
ʎ
AWS Lambda
function
Amazon SQS
queue
queue
Subscriber
Consumers
4 3 2 1
12344 3 2 1
1234
2134
13342
Standard
FIFO
19. Which Stream/Message Storage Should I Use?
Amazon
DynamoDB
Streams
Amazon
Kinesis
Streams
Amazon
Kinesis
Firehose
Apache
Kafka
Amazon
SQS
(Standard)
Amazon SQS
(FIFO)
AWS managed Yes Yes Yes No Yes Yes
Guaranteed ordering Yes Yes No Yes No Yes
Delivery (deduping) Exactly-once At-least-once At-least-once At-least-once At-least-once Exactly-once
Data retention period 24 hours 7 days N/A Configurable 14 days 14 days
Availability 3 AZ 3 AZ 3 AZ Configurable 3 AZ 3 AZ
Scale /
throughput
No limit /
~ table IOPS
No limit /
~ shards
No limit /
automatic
No limit /
~ nodes
No limits /
automatic
300 TPS /
queue
Parallel consumption Yes Yes No Yes No No
Stream MapReduce Yes Yes N/A Yes N/A N/A
Row/object size 400 KB 1 MB Destination
row/object size
Configurable 256 KB 256 KB
Cost Higher (table
cost)
Low Low Low (+admin) Low-medium Low-medium
Hot Warm
New
20. In-memory
COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
Database
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Search
Messaging
Message MESSAGES
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
Apache Kafka
Amazon Kinesis
Streams
Amazon Kinesis
Firehose
Amazon DynamoDB
Streams
Hot
Stream
Amazon S3
Amazon SQS
Message
Amazon S3
File
LoggingIoTApplicationsTransportMessaging
File Storage
21. Why Is Amazon S3 Good for Big Data?
• Natively supported by big data frameworks (Spark, Hive, Presto, etc.)
• No need to run compute clusters for storage (unlike HDFS)
• Can run transient Hadoop clusters & Amazon EC2 Spot Instances
• Multiple & heterogeneous analysis clusters can use the same data
• Unlimited number of objects and volume of data
• Very high bandwidth – no aggregate throughput limit
• Designed for 99.99% availability – can tolerate zone failure
• Designed for 99.999999999% durability
• No need to pay for data replication
• Native support for versioning
• Tiered-storage (Standard, IA, Amazon Glacier) via life-cycle policies
• Secure – SSL, client/server-side encryption at rest
• Low cost
22. What About HDFS & Data Tiering?
• Use HDFS for very frequently accessed
(hot) data
• Use Amazon S3 Standard for frequently
accessed data
• Use Amazon S3 Standard – IA for less
frequently accessed data
• Use Amazon Glacier for archiving cold data
23. In-memory
COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS Database
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Search
Messaging
Message MESSAGES
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
Apache Kafka
Amazon Kinesis
Streams
Amazon Kinesis
Firehose
Amazon DynamoDB
Streams
Hot
Stream
Amazon SQS
Message
Amazon S3
File
LoggingIoTApplicationsTransportMessaging
In-memory, Database,
Search
25. Best Practice: Use the Right Tool for the Job
Data Tier
Search
Amazon Elasticsearch
Service
In-memory
Amazon ElastiCache
Redis
Memcached
SQL
Amazon Aurora
Amazon RDS
MySQL
PostgreSQL
Oracle
SQL Server
NoSQL
Amazon DynamoDB
Cassandra
HBase
MongoDB
26. COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Messaging
Message MESSAGES
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
Apache Kafka
Amazon Kinesis
Streams
Amazon Kinesis
Firehose
Amazon DynamoDB
Streams
Hot
Stream
Amazon SQS
Message
Amazon Elasticsearch
Service
Amazon DynamoDB
Amazon S3
Amazon ElastiCache
Amazon RDS
SearchSQLNoSQLCacheFile
LoggingIoTApplicationsTransportMessaging
Amazon ElastiCache
• Managed Memcached or Redis service
Amazon DynamoDB
• Managed NoSQL database service
Amazon RDS
• Managed relational database service
Amazon Elasticsearch Service
• Managed Elasticsearch service
27. What Data Store Should I Use?
Data structure → Fixed schema, JSON, key-value
Access patterns → Store data in the format you will access it
Data characteristics → Hot, warm, cold
Cost → Right cost
28. Data Structure and Access Patterns
Access Patterns What to use?
Put/Get (key, value) In-memory, NoSQL
Simple relationships → 1:N, M:N NoSQL
Multi-table joins, transaction, SQL SQL
Faceting, search Search
Data Structure What to use?
Fixed schema SQL, NoSQL
Schema-free (JSON) NoSQL, Search
(Key, value) In-memory, NoSQL
30. Amazon ElastiCache Amazon
DynamoDB
Amazon
RDS/Aurora
Amazon
ES
Amazon S3 Amazon Glacier
Average
latency
ms ms ms, sec ms,sec ms,sec,min
(~ size)
hrs
Typical
data stored
GB GB–TBs
(no limit)
GB–TB
(64 TB max)
GB–TB MB–PB
(no limit)
GB–PB
(no limit)
Typical
item size
B-KB KB
(400 KB max)
KB
(64 KB max)
B-KB
(2 GB max)
KB-TB
(5 TB max)
GB
(40 TB max)
Request
Rate
High – very high Very high
(no limit)
High High Low – high
(no limit)
Very low
Storage cost
GB/month
$$ ¢¢ ¢¢ ¢¢ ¢ ¢4/10
Durability Low - moderate Very high Very high High Very high Very high
Availability High
2 AZ
Very high
3 AZ
Very high
3 AZ
High
2 AZ
Very high
3 AZ
Very high
3 AZ
Hot data Warm data Cold data
Which Data Store Should I Use?
31. Cost-Conscious Design
Example: Should I use Amazon S3 or Amazon DynamoDB?
“I’m currently scoping out a project. The design calls for
many small files, perhaps up to a billion during peak. The
total size would be on the order of 1.5 TB per month…”
Request rate
(Writes/sec)
Object size
(Bytes)
Total size
(GB/month)
Objects per month
300 2048 1483 777,600,000
37. Which Stream & Message Processing Technology Should I Use?
Amazon
EMR (Spark
Streaming)
Apache
Storm
KCL Application Amazon Kinesis
Analytics
AWS
Lambda
Amazon SQS
Application
AWS
managed
Yes (Amazon
EMR)
No (Do it
yourself)
No ( EC2 + Auto
Scaling)
Yes Yes No (EC2 + Auto
Scaling)
Serverless No No No Yes Yes No
Scale /
throughput
No limits /
~ nodes
No limits /
~ nodes
No limits /
~ nodes
Up to 8 KPU /
automatic
No limits /
automatic
No limits /
~ nodes
Availability Single AZ Configurable Multi-AZ Multi-AZ Multi-AZ Multi-AZ
Programming
languages
Java,
Python,
Scala
Almost any
language via
Thrift
Java, others via
MultiLangDaemon
ANSI SQL with
extensions
Node.js,
Java,
Python
AWS SDK
languages (Java,
.NET, Python, …)
Uses Multistage
processing
Multistage
processing
Single stage
processing
Multistage
processing
Simple
event-based
triggers
Simple event
based triggers
Reliability KCL and
Spark
checkpoints
Framework
managed
Managed by KCL Managed by
Amazon Kinesis
Analytics
Managed by
AWS
Lambda
Managed by SQS
Visibility Timeout
38. Which Analysis Tool Should I Use?
Amazon Redshift Amazon EMR
Presto Spark Hive
Use case Optimized for data warehousing Interactive
query
General purpose (iterative
ML, RT, ..)
Batch
Scale/throughput ~Nodes ~ Nodes
Storage Local storage Amazon S3, HDFS
Optimization Columnar storage, data
compression, and zone maps
Framework dependent
Metadata Amazon Redshift managed Hive Meta-store
BI tools supports Yes (JDBC/ODBC) Yes (JDBC/ODBC & Custom)
Access controls Users, groups, and access controls Integration with LDAP
UDF support Yes (Scalar) Yes
Slow
42. STORE CONSUMEPROCESS / ANALYZE
Amazon QuickSight
Apps & Services
Analysis&visualizationNotebooksIDEAPI
Applications & API
Analysis and visualization
Notebooks
IDE
Business
users
Data scientist,
developers
COLLECT ETL
53. Summary
Build decoupled systems
• Data → Store → Process → Store → Analyze → Answers
Use the right tool for the job
• Data structure, latency, throughput, access patterns
Leverage AWS managed services
• Scalable/elastic, available, reliable, secure, no/low admin
Use log-centric design patterns
• Immutable log, batch, interactive & real-time views
Be cost conscious
• Big data ≠ big cost