This document discusses Amazon Web Services' database and analytics services. It begins by noting that 85% of businesses want to be data-driven but only 37% have been successful. It then presents the "data flywheel" concept of breaking from legacy databases, modernizing data infrastructure and data warehouses, turning data into insights, and building data-driven applications to gain momentum with data. The document provides overviews and benefits of AWS services like Amazon Aurora, Athena, Redshift, RDS, DMS, and Elasticsearch. It also introduces new capabilities for these services like machine learning with Aurora, RDS on Outposts, UltraWarm storage for Elasticsearch, and materialized views in Redshift.
Si stima che i clienti abbiamo in totale 256 EB di file shares in locale. La gestione di questi file systems è onerosa e comporta problematiche sia di budget (CAPEX) che di operation (gestione, scalabilità, data protection). Tipicamente gli apparati NAS locali devono essere sostituiti ogni 3-5 anni, obbligando i clienti a fare un capacity planning pluriennale e richiedendo un progetto a sè stante per la migrazione dati.
Il passaggio al cloud di AWS consente ai clienti di pagare esattamente la quantità di spazio di archiviazione di file di cui hanno bisogno ora, senza costi o vincoli iniziali e ridimensionare la capacità necessaria durante la crescita dei dati senza dover stimare in anticipo di quanto avranno bisogno. Sfruttando soluzioni di file completamente gestite come Amazon FSx per Windows File Server, FSx Backup, i clienti non devono più preoccuparsi del sovraccarico amministrativo di impostazione, protezione, manutenzione e backup della propria infrastruttura di file.
La recente apertura della regione italiana MXP apre a nuovi scenari di hybrid cloud per la parte filesystem/SMB share.
A modern Big Data architecture involves extending your on-premises data management to AWS, implementing a data pipeline to stream real-time data into cloud data warehouse Amazon Redshift, perform data transformation, discovery, predictive analytics through machine learning, visualize complex information and be notified to respond to business events. This session is for APN Consulting Partners and organizations looking for ways to accelerate and modernize their Big Data projects. You will learn how to deploy and integrate AWS Services with Third-party Solutions in AWS Marketplace. Reduce your time to market by combining AWS services, open source software and ready-to-run on AWS solutions. Familiarity with Database technologies required. The session includes demonstrations and cooperative learning group activities.
Building Data Lakes and Analytics on AWS. IPExpo Manchester.javier ramirez
Over 90% of today's data was generated in the last 2 years, and the rate of data growth isn't slowing down. In this session, we'll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services.
We'll frame the session around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We'll show how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and even Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
Moving to the cloud requires proper planning. There are many reasons to move to the cloud but without starting with a solid plan it will be difficult to achieve your objectives.
Are you prepared for the cloud?
In this webinar, Portal Solutions’ Daniel Cohen-Dumani and Dale Tuttle discuss Making your Cloud Strategy Work in 2016, including:
- Learning the key steps to making a cloud strategy that works
- Determining how moving to the cloud impacts your staff & your business
- Learning how to align your goals with opportunities
Develop a Custom Data Solution Architecture with NorthBayAmazon Web Services
Organizations that have vast amounts of data in legacy applications often experience difficulties delivering that data to business unit end-users. Register to learn how Eliza Corporation and Scholastic overcame this challenge by leveraging a Data Lake solution from NorthBay on AWS to optimize data analytics and provide greater visibility. AWS and NorthBay will give you an in-depth overview of how you can use a Data Lake in conjunction with your existing on-premises or cloud-based Data Warehouse. NorthBay helps organizations scale their ETL and data warehousing workloads using Amazon EMR and Amazon Redshift. Join us to learn: • Best practices for using a Data Lake in conjunction with your existing data warehouse • The key aspects of introducing agile and scrum methodologies into an enterprise • The most impactful cost-savings levers that are addressed via a cloud data warehouse migration
Who should attend: Heads of Analytics, Heads of BI, Analytics Managers, BI Teams, Senior Analysts
By using a Data Lake, you no longer need to worry about structuring or transforming data before storing it. A Data Lake on AWS enables your organization to more rapidly analyze data, helping you quickly discover new business insights. Join us for our webinar to learn about the benefits of building a Data Lake on AWS and how your organization can begin reaping their rewards. In this webinar, select APN Partners will share their specific methodology for implementing a Data Lake on AWS and best practices for getting the most from your Data Lake.
Transform Your Business with VMware Cloud on AWS: Technical Overview Amazon Web Services
VMware Cloud on AWS is a jointly engineered service that brings VMware’s enterprise class software-defined data center (SDDC) technologies to run on next-generation bare-metal AWS infrastructure—delivered as a cloud service. With VMware Cloud on AWS, not only will you be able to consume VMware products on AWS, but you will also be able to leverage AWS native services from virtual machines running within VMware Cloud on AWS. Come learn about the latest features and how you can leverage the best of both VMware and AWS for your environment.
Si stima che i clienti abbiamo in totale 256 EB di file shares in locale. La gestione di questi file systems è onerosa e comporta problematiche sia di budget (CAPEX) che di operation (gestione, scalabilità, data protection). Tipicamente gli apparati NAS locali devono essere sostituiti ogni 3-5 anni, obbligando i clienti a fare un capacity planning pluriennale e richiedendo un progetto a sè stante per la migrazione dati.
Il passaggio al cloud di AWS consente ai clienti di pagare esattamente la quantità di spazio di archiviazione di file di cui hanno bisogno ora, senza costi o vincoli iniziali e ridimensionare la capacità necessaria durante la crescita dei dati senza dover stimare in anticipo di quanto avranno bisogno. Sfruttando soluzioni di file completamente gestite come Amazon FSx per Windows File Server, FSx Backup, i clienti non devono più preoccuparsi del sovraccarico amministrativo di impostazione, protezione, manutenzione e backup della propria infrastruttura di file.
La recente apertura della regione italiana MXP apre a nuovi scenari di hybrid cloud per la parte filesystem/SMB share.
A modern Big Data architecture involves extending your on-premises data management to AWS, implementing a data pipeline to stream real-time data into cloud data warehouse Amazon Redshift, perform data transformation, discovery, predictive analytics through machine learning, visualize complex information and be notified to respond to business events. This session is for APN Consulting Partners and organizations looking for ways to accelerate and modernize their Big Data projects. You will learn how to deploy and integrate AWS Services with Third-party Solutions in AWS Marketplace. Reduce your time to market by combining AWS services, open source software and ready-to-run on AWS solutions. Familiarity with Database technologies required. The session includes demonstrations and cooperative learning group activities.
Building Data Lakes and Analytics on AWS. IPExpo Manchester.javier ramirez
Over 90% of today's data was generated in the last 2 years, and the rate of data growth isn't slowing down. In this session, we'll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services.
We'll frame the session around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We'll show how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and even Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
Moving to the cloud requires proper planning. There are many reasons to move to the cloud but without starting with a solid plan it will be difficult to achieve your objectives.
Are you prepared for the cloud?
In this webinar, Portal Solutions’ Daniel Cohen-Dumani and Dale Tuttle discuss Making your Cloud Strategy Work in 2016, including:
- Learning the key steps to making a cloud strategy that works
- Determining how moving to the cloud impacts your staff & your business
- Learning how to align your goals with opportunities
Develop a Custom Data Solution Architecture with NorthBayAmazon Web Services
Organizations that have vast amounts of data in legacy applications often experience difficulties delivering that data to business unit end-users. Register to learn how Eliza Corporation and Scholastic overcame this challenge by leveraging a Data Lake solution from NorthBay on AWS to optimize data analytics and provide greater visibility. AWS and NorthBay will give you an in-depth overview of how you can use a Data Lake in conjunction with your existing on-premises or cloud-based Data Warehouse. NorthBay helps organizations scale their ETL and data warehousing workloads using Amazon EMR and Amazon Redshift. Join us to learn: • Best practices for using a Data Lake in conjunction with your existing data warehouse • The key aspects of introducing agile and scrum methodologies into an enterprise • The most impactful cost-savings levers that are addressed via a cloud data warehouse migration
Who should attend: Heads of Analytics, Heads of BI, Analytics Managers, BI Teams, Senior Analysts
By using a Data Lake, you no longer need to worry about structuring or transforming data before storing it. A Data Lake on AWS enables your organization to more rapidly analyze data, helping you quickly discover new business insights. Join us for our webinar to learn about the benefits of building a Data Lake on AWS and how your organization can begin reaping their rewards. In this webinar, select APN Partners will share their specific methodology for implementing a Data Lake on AWS and best practices for getting the most from your Data Lake.
Transform Your Business with VMware Cloud on AWS: Technical Overview Amazon Web Services
VMware Cloud on AWS is a jointly engineered service that brings VMware’s enterprise class software-defined data center (SDDC) technologies to run on next-generation bare-metal AWS infrastructure—delivered as a cloud service. With VMware Cloud on AWS, not only will you be able to consume VMware products on AWS, but you will also be able to leverage AWS native services from virtual machines running within VMware Cloud on AWS. Come learn about the latest features and how you can leverage the best of both VMware and AWS for your environment.
Analyzing your web and application logs with the Amazon Elasticsearch Service...javier ramirez
This presentation shows how you can use the Amazon Elasticsearch Service in general, and in particular I showcase how you can host a static website, use CloudFront as a global CDN, and process HTTP logs using serverless functions with Lambda to ingest into ElasticSearch. I finish by creating a Kibana dashboard. Live demo was done in Stockholm and Oslo, and screenshots are included. Also included the URL for you to build the demo by yourself
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.
SAP provides software that many organizations continue to use to manage core workload requirements, with a significant number of these organizations running their SAP workloads on AWS. However, SAP doesn’t need to be operated similarly in the cloud as it is on premise. AWS can still drive innovation, even in operating solutions from companies such as SAP. Join us to see how organizations who have a strong leverage on AWS, carry over their platform knowledge to SAP workloads. They can improve the efficiency of their application teams - from easier migrations, to reducing time spent in operations, and overall improving the flexibility of delivering SAP workloads.
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Enterprises that are embracing cloud computing are interested in driving fundamental changes in their business so they can compete in the future. IT transformation, enabled by cloud adoption, is a key component of this future success—from tighter alignment with business unit stakeholders to increased agility and pace of innovation. In this session, we explore the potential for transformation that comes with cloud adoption, and we discuss how some of the world’s leading enterprises were able to transform. We also explore organizational and technology best practices that you can implement to support transformation in your organization.
Avere & AWS Enterprise Solution with Special Bundle Pricing OfferAvere Systems
In this webinar, Sabina Joseph, AWS, and Mark Eastman, Avere, discuss the enterprise cloud NAS solution available using Avere FXT Edge Filers and Amazon Cloud Services. Special limited-time bundle pricing is available and will be reviewed at the end.
Enterprises that are embracing cloud computing are interested in driving fundamental changes in their business so they can compete in the future. IT transformation, enabled by cloud adoption, is a key component of this future success—from tighter alignment with business unit stakeholders to increased agility and pace of innovation. In this session, we explore the potential for transformation that comes with cloud adoption and discuss how some of the world’s leading enterprises were able to transform. We also explore organizational and technology best practices that you can implement to support transformation in your organization.
This intends to help start-ups, ISV’s, SI’s and other organisations understand the security and assurance requirements needed to provide services for the UK public sector.
There is a large number of legacy enterprise Microsoft applications (HR, Finance, CMS, BPM apps) still running on premises. This session will focus on retiring technical debt and bringing some of those applications into AWS. You will learn why it's important to go cloud, how easy it is to run & optimize Microsoft applications on AWS, the different approaches to maximize server utilization and save money.
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.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
Analyzing your web and application logs with the Amazon Elasticsearch Service...javier ramirez
This presentation shows how you can use the Amazon Elasticsearch Service in general, and in particular I showcase how you can host a static website, use CloudFront as a global CDN, and process HTTP logs using serverless functions with Lambda to ingest into ElasticSearch. I finish by creating a Kibana dashboard. Live demo was done in Stockholm and Oslo, and screenshots are included. Also included the URL for you to build the demo by yourself
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.
SAP provides software that many organizations continue to use to manage core workload requirements, with a significant number of these organizations running their SAP workloads on AWS. However, SAP doesn’t need to be operated similarly in the cloud as it is on premise. AWS can still drive innovation, even in operating solutions from companies such as SAP. Join us to see how organizations who have a strong leverage on AWS, carry over their platform knowledge to SAP workloads. They can improve the efficiency of their application teams - from easier migrations, to reducing time spent in operations, and overall improving the flexibility of delivering SAP workloads.
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Enterprises that are embracing cloud computing are interested in driving fundamental changes in their business so they can compete in the future. IT transformation, enabled by cloud adoption, is a key component of this future success—from tighter alignment with business unit stakeholders to increased agility and pace of innovation. In this session, we explore the potential for transformation that comes with cloud adoption, and we discuss how some of the world’s leading enterprises were able to transform. We also explore organizational and technology best practices that you can implement to support transformation in your organization.
Avere & AWS Enterprise Solution with Special Bundle Pricing OfferAvere Systems
In this webinar, Sabina Joseph, AWS, and Mark Eastman, Avere, discuss the enterprise cloud NAS solution available using Avere FXT Edge Filers and Amazon Cloud Services. Special limited-time bundle pricing is available and will be reviewed at the end.
Enterprises that are embracing cloud computing are interested in driving fundamental changes in their business so they can compete in the future. IT transformation, enabled by cloud adoption, is a key component of this future success—from tighter alignment with business unit stakeholders to increased agility and pace of innovation. In this session, we explore the potential for transformation that comes with cloud adoption and discuss how some of the world’s leading enterprises were able to transform. We also explore organizational and technology best practices that you can implement to support transformation in your organization.
This intends to help start-ups, ISV’s, SI’s and other organisations understand the security and assurance requirements needed to provide services for the UK public sector.
There is a large number of legacy enterprise Microsoft applications (HR, Finance, CMS, BPM apps) still running on premises. This session will focus on retiring technical debt and bringing some of those applications into AWS. You will learn why it's important to go cloud, how easy it is to run & optimize Microsoft applications on AWS, the different approaches to maximize server utilization and save money.
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.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
FSI201 FINRA’s Managed Data Lake – Next Gen Analytics in the CloudAmazon Web Services
FINRA’s Data Lake unlocks the value in its data to accelerate analytics and machine learning at scale. FINRA's Technology group has changed its customer's relationship with data by creating a Managed Data Lake that enables discovery on Petabytes of capital markets data, while saving time and money over traditional analytics solutions. FINRA’s Managed Data Lake includes a centralized data catalog and separates storage from compute, allowing users to query from petabytes of data in seconds. Learn how FINRA uses Spot instances and services such as Amazon S3, Amazon EMR, Amazon Redshift, and AWS Lambda to provide the 'right tool for the right job' at each step in the data processing pipeline. All of this is done while meeting FINRA’s security and compliance responsibilities as a financial regulator.
This is the complete deck presented at the Westin Calgary Hotel, on August 16th, 2016.
It covers the current state of the AWS Big Data Solution set. Contains several use cases of Big Data, Machine Learning, and a tutorial on how to implement and use Big Data on the AWS Cloud Platform.
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
What’s New in Amazon RDS for Open-Source and Commercial DatabasesAmazon Web Services
In the past year, Amazon Relational Database Service has continued to expand functionality, scalability, availability and ease of use for all supported database engines (PostgreSQL, MySQL, MariaDB, Oracle and Microsoft SQL Server). We’ll take a close look at RDS use cases and new capabilities, splitting the time between open-source and commercial database engines.
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
This presentation summarizes Amazon Redshift data warehouse service, its architecture and best practices for application development using Amazon Redshift.
What’s New in Amazon RDS for Open-Source and Commercial DatabasesAmazon Web Services
In the past year, Amazon RDS has continued to expand functionality, scalability, availability and ease of use for all supported database engines: PostgreSQL, MySQL, MariaDB, Oracle and Microsoft SQL Server. We’ll take a close look at RDS use cases and new capabilities, splitting the time between open-source and commercial database engines.
What is Innovation? How can cloud computing help you innovate? How can you make your applications smarter? Predictive? How can you interpret data and anticipate trends? With AWS Artificial Intelligence Solutions: Machine Learning, Rekognition, Polly; with serverless - Lambda, Step Functions.
Best Practices Using Big Data on AWS | AWS Public Sector Summit 2017Amazon Web Services
Join us for this general session where AWS big data experts present an in-depth look at the current state of big data. Learn about the latest big data trends and industry use cases. Hear how other organizations are using the AWS big data platform to innovate and remain competitive. Take a look at some of the most recent AWS big data developments. Learn More: https://aws.amazon.com/government-education/
Similar to State of the Union: Database & Analytics (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.
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.
Durante i laboratori pratici, gli esperti AWS ti mostrano quali strumenti aiutano a sviluppare le applicazioni Serverless in locale e nel cloud AWS e ti aiuteranno a programmare i prossimi passi per iniziare ad utilizzare questa tecnologia nella tua azienda.
AWS Serverless per startup: come innovare senza preoccuparsi dei serverAmazon Web Services
La tecnologia Serverless sembra essere perfetta per startup. Il modello di pricing “pay-per-use” e l’infrastruttura che non genera alcun costo in mancanza di traffico rende la struttura di costi estremamente economica ed efficiente per le startup. Inoltre, le architetture Serverless sono pienamente gestite e scalano automaticamente, per questo i team non devono preoccuparsi di improvvise crescite del traffico che, per esempio, possono derivare da campagne marketing di successo. Questi sono solo alcuni dei motivi per cui molte startup hanno deciso di costruire architetture Serverless a supporto del proprio business. Durante il webinar, approfondiremo i servizi Serverless di AWS e come le startup possono utilizzarli per aumentare agilità e innovazione. Approfondiremo il servizio AWS Lambda che permette di eseguire codice per qualunque tipologia di applicazione, senza alcun lavoro di “amministrazione”. Durante la sessione, condivideremo inoltre casi d’uso di startup che hanno implementato con successo la tecnologia Serverless.
4. 010010010
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100010100
Data
1 Break free from
legacy databases
Move to managed2
Save time and cost
Remove undifferentiated heavy lifting
Turn data to insights5
Better experiences
Deeper engagement
Efficient processes
Build
data-driven apps
4
Modernize your
data warehouse
3
Agility
Global distribution
Performance at scale
Increase scale
Improve performance
Lower cost
Better and faster insights
Broader access to analytics
How do you build momentum?
5. 010010010
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100010100
1 Break free from
legacy databases
Move to managed2
Turn data to insights5
Build
data-driven apps
4
Modernize your
data warehouse
3
The Data Flywheel
Data
6. 010010010
01010001
100010100
Data
1 Break free from
legacy databases
Move to managed2
Turn data to insights5
Build
data-driven apps
4
Modernize your
data warehouse
3
The Data Flywheel
Modernize your data infrastructure
Get the most value from your data
7. 010010010
01010001
100010100
Data
1 Break free from
legacy databases
Move to managed2
Turn data to insights5
Build
data-driven apps
4
Modernize your
data warehouse
3
Modernizeyour
datainfrastructure
Getthemostvalue
fromyourdata
The Data Flywheel
9. Amazon Aurora
MySQL and PostgreSQL compatible relational database built for the cloud
Performance and availability of commercial-grade databases at 1/10th the cost
Performance
and scalability
5x throughput of MySQL
3x throughput of PostgreSQL
Up to15 read replicas
Scale out reads and writes
across multiple data centers
Fully managed
Managed by RDS:
no hardware provisioning,
software patching, setup,
configuration, or backups
Availability
and durability
Fault-tolerant self-healing storage
Six copies of data across three AZs
Continuous backup to S3
Single Global database with cross-
region replication
Network isolation
Encryption at rest/transit
Highly secure
10. Challenges with integrating ML with your database
Typical steps of incorporating ML into an application
Write application
code to read data
from the database
2
Query and format the
data for the ML
algorithm
3 Call an ML service to
run the algorithm4
Select and train
the model
1 Format the
output
5
Retrieve the
results back to
the application
6
11. Generate predictions directly from Aurora queries
Models run in SageMaker & Comprehend
Use standard SQL, no ML expertise required
Suitable for low-latency, high-volume use cases
Amazon
SageMaker
ML
Aurora
Database
Athena
Interactive
analytics
SQL
Select
From
Where
ML in Amazon Aurora and Athena
Bringing machine learning to data developers and data analysts
13. 010010010
01010001
100010100
Data
1 Break free from
legacy databases
Move to managed2
Turn data to insights5
Build
data-driven apps
4
Modernize your
data warehouse
3
The Data FlywheelModernizeyour
datainfrastructure
Getthemostvalue
fromyourdata
14. Hardware and software installation
Configuration, patching, and backups
Cluster setup and data replication for high availability
Capacity planning, and scaling clusters for compute and storage
Managing software on-premises
is time consuming and complex
15. Customers moving to fully managed services
Relational databases
Aurora RDS EMR
Hadoop
and Spark
Elasticsearch
Service
Operational
analytics
Managed
Streaming
for Kafka
Real-time
analytics
DynamoDB DocumentDB ElastiCache
Managed
Cassandra
Service
Non-relational databases
16. Amazon RDS
Managed relational database service with a choice of popular databases
Easy to administer
No infrastructure provisioning
No software installation and
patching
Built-in monitoring
Performant & scalable
Scale with an API call or a few
clicks
Read replicas for increased
throughput
Automatic Multi-AZ
data replication
Automated backup,
snapshots, and failover
Available & durable Secure and compliant
Encryption at rest and in transit
Network isolation and
resource-level permissions
17. How do you scale your relational database to support
tens of thousands of connections?
Serverless applications
open and close tens of
thousands of connections
within seconds
Leads to longer query
response times that limits
application scalability
A database proxy server
are difficult to deploy,
patch, and manage
18. Amazon RDS Proxy
Fully managed, highly available database proxy
Supports new scale of serverless application connections
Pools and shares database connections
Preserve connections during database failovers
Manages DB credentials with Secrets Manager and IAM
Fully managed—No provisioning, patching, management
RDS
Proxy
Applications
RDS
Database Instance
Connection Pooling
PREVIEW
NEW
19. Amazon RDS on AWS Outposts
RDS
MySQL, PostgreSQL,
AWS
Outposts
Launch RDS in your data centers with AWS Outpost
Integrate with on-premises databases and applications
Deploy secure, managed, RDS in minutes
Store data without moving to cloud
Automates provisioning, patching, backup, restoring,
scaling, and failover
PREVIEW
NEW
20. Operational Analytics: Amazon Elasticsearch Service
Fully managed, scalable, secure, Elasticsearch service
Open source Elasticsearch
APIs, Kibana, and
Logstash
Open-source Elasticsearch APIs
Managed Kibana
Integration with Logstash
Scale clusters up/down via a
single API call or a few clicks
Secured network isolation
with VPC, encrypt data
at-rest and in-transit
Compliant: HIPPA, PCI DSS,
and ISO
Scalable, secure,
and compliant
Pay only for
what you use
Cost-optimized workloads
No upfront fee or
usage requirement
Critical features built-in:
encryption, VPC support,
24x7 monitoring
Fully managed
Deploy Elasticsearch clusters
in minutes: simplified hardware
provisioning, software
installation/patching, failure
recovery, backups, and monitoring
21. Challenges with analyzing high volumes of data in real-time
Storing data is
expensive at scale
Limits the amount of
data retained for analysis
Miss out on
valuable insights
22. UltraWarm for Amazon Elasticsearch Service
A new warm storage tier for Elasticsearch service
Kibana
Dashboard
Amazon Elasticsearch Service domain
Application
Load
Balancer
Seamlessly extends Elasticsearch service
Reduces cost by 90% to store the same amount
of data
Scale up to 3 PB of log data per cluster
Analyze years of operational data
Amazon S3
UltraWarm
Node
UltraWarm
Node
UltraWarm
Node
Active
Master Node
Queries
PREVIEW
NEW
23. 010010010
01010001
100010100
Data
1 Break free from
legacy databases
Move to managed2
Turn data to insights5
Build
data-driven apps
4
Modernize your
data warehouse
3
The Data FlywheelModernizeyour
datainfrastructure
Getthemostvalue
fromyourdata
24. Data warehousing: Amazon Redshift
Best performance,
most scalable
3x faster with RA3*
10x faster with AQUA*
Adds unlimited compute capacity
on-demand to meet unlimited
concurrent access
Lowest cost
Cost-optimized workloads
by paying compute and
storage separately
1/10th cost of Traditional
DW at $1000/TB/year
Up to 75% less than other
cloud data warehouses &
predictable costs
Data lake &
AWS integration
Analyze exabytes of data across
data warehouse, data lakes, and
operational database
Query data across various
analytics services
Most secure
& compliant
AWS-grade security (eg. VPC,
encryption with KMS, CloudTrail)
All major certifications such
as SOC, PCI, DSS, ISO,
FedRAMP, HIPPA
First and most popular cloud data warehouse
*vs other cloud DWs
25. Most widely used Cloud Data Warehouse
Tens of thousands of customers use Redshift & process over 2EB of data per day
26. Robust result set
caching
Large # of tables support
~20000
Copy command support for ORC,
Parquet
IAM role chaining Elastic resize Groups
Redshift Spectrum: date formats,
scalar json and ION file formats
support, region expansion,
predicate filtering
Auto analyze
Health and performance
monitoring w/Amazon Cloud
watch
Automatic table
distribution style
Cloud watch support for
WLM queues
Performance enhancements—
hash join, vacuum, window
functions, resize ops,
aggregations, console, union all,
efficient compile code cache
Unload
to CSV
Auto WLM
~25 Query Monitoring
Rules (QMR) support
200+
new features in the past 18
months
AQUA
Concurrency Scaling DC1 migration to DC2
Resiliency of ROLLBACK
processing
Manage multi-part
query in AWS console
Auto analyze for
incremental changes on
table
Spectrum Request
Accelerator
Apply new distribution key
Redshift Spectrum: Row
group filtering in Parquet
and ORC, Nested data
support, Enhanced VPC
Routing, Multiple partitions
Faster Classic resize
with optimized data
transfer protocol
Performance: Bloom filters in
joins, complex queries that
create internal table,
communication layer
Redshift Spectrum:
Concurrency scaling
Amazon Lake Formation
integration
Auto-Vacuum sort,
Auto-Analyze and Auto
Table Sort
Auto WLM with query
priorities
Snapshot scheduler
Performance: join pushdowns
to subquery,, mixed workloads
temporary tables, rank functions,
null handling in join, single row
insert
Advisor recommendations
for distribution keys
AZ64 compression
encoding
Console redesign
Stored procedures
Spatial Processing
Column level access
control
with AWS lake formation
RA3
Performance of Inter-
Region Snapshot
Transfers
Federated
Query
Materialized
Views
Manual Pause and Resume
Amazon Redshift has been innovating quickly
27. Amazon Redshift Materialized Views
Defined by a SQL query, precomputed results, incrementally
refreshed
Orders-of-magnitude query acceleration
Recommended for predictable and repeated queries used in
dashboarding and interactive analysis
C1 C2 C3
R1
R2
R3
C1 C2 C3 C4
R1
R2
R3
C1
R1
R2
R9
C1 C2 C3
R1
R2
R3
C1
R1
R7
R9
Materialized Views
PREVIEW
NEW
28. Amazon Redshift Data Lake Export
Export data directly to Amazon S3 in Apache Parquet
Save results of data transformation into S3 data lake
Export with the UNLOAD command and specify Parquet
Redshift formats, partitions, and moves data into S3
Analyze with Amazon SageMaker, Athena, and EMR
S3
Redshift
NEW
29. Amazon Redshift Federated Query
Analyze data across data warehouse, data lakes, and operational database
Query across multiple systems from Redshift
Combine data warehouse and transactional data
Compatible with Amazon RDS and Aurora (PostgreSQL)
SQL
A M A Z O N
R D S
A M A Z O N
A U R O R A
A M A Z O N
R E D S H I F T
S 3 D A T A L A K E
PREVIEW
NEW
30. Amazon Redshift on RA3 instances
Optimize your data warehouse by paying for compute and storage separately
Delivers 3x the performance of existing cloud DWs
Automatically scales your DW storage capacity
DS2 customers can migrate and get 2x performance
and 2x storage for the same cost
Supports workloads up to 8 PB (compressed) for a
cluster
COMPUTE NODE
(RA3)
SSD Cache
S 3 S T O R A G E
COMPUTE NODE
(RA3)
SSD Cache
COMPUTE NODE
(RA3)
SSD Cache
COMPUTE NODE
(RA3)
SSD Cache
Managed storage
$/node/hour
$/TB/month
GA
NEW
31. AQUA
(Advanced Query Accelerator)
for Amazon Redshift
An innovative new hardware-accelerated cache that delivers up
to 10x better query performance than other cloud data
warehouses
NVMe SSDs
CUSTOM ANALYTICS PROCESSORS
AWS NITRO SYSTEM
COMING IN
2020
NEW
32. AQUA – Advanced Query Accelerator
Redshift runs 10x faster than any other cloud data warehouse without increasing cost
AQUA brings compute to the storage layer so data
doesn’t have to move back and forth
High-speed cache on top of S3 scales out to process
data
in parallel across many nodes
AWS custom-designed analytics processors accelerate
data compression, encryption, and data processing
100% compatible with the current version of RedshiftS 3 S T O R A G E
AQUA
ADVANCED QUERY ACCELERATOR
R A 3 C O M P U T E C L U S T E R
COMING IN
2020
NEW
33. 010010010
01010001
100010100
Data
1 Break free from
legacy databases
Move to managed2
Turn data to insights5
Build
data-driven apps
4
Modernize your
data warehouse
3
The Data FlywheelModernizeyour
datainfrastructure
Getthemostvalue
fromyourdata
34. Characteristics of modern applications
Internet-scale and transactional
Users: 1M+
Data volume: TB–PB–EB
Locality: Global
Performance: Milliseconds–microseconds
Request Rate: Millions
Access: Web, Mobile, IoT, devices
Scale: Up-down, Out-in
Economics: Pay for what you use
Developer access: Instant API accessSocial mediaRide hailing Media streaming Dating
35. Break complex apps into smaller pieces and pick the
best tool to solve each problem
This ensures that the apps are well architected and
scale effectively
Developers are now building highly distributed apps using
purpose-built databases and micro-services architecture
Developers are doing what they do best
37. Amazon Managed (Apache) Cassandra Service
Scalable, highly available, and managed Cassandra-compatible database service
No need to provision, configure,
and operate large Cassandra
clusters or add and remove
nodes manually
No servers to manage
Single-digit millisecond
performance
Scale tables up and down
automatically based on
application traffic
Virtually unlimited
throughput and storage
Single-digit millisecond
performance at scale
Apache
Cassandra-compatible
Use the same application code,
licensed drivers, and tools
built on Cassandra
Simple migration
Simple migration to Managed
Cassandra Service for
Cassandra databases on
premises or on EC2
PREVIEW
NEW
38. 010010010
01010001
100010100
Data
1 Break free from
legacy databases
Move to managed2
Turn data to insights5
Build
data-driven apps
4
Modernize your
data warehouse
3
The Data FlywheelModernizeyour
datainfrastructure
Getthemostvalue
fromyourdata
39. Customers moving to data lake architectures
Bringing together the best of both worlds
Extends or evolves DW architectures
Store any data in any format
Durable, available, and exabyte scale
Secure, compliant, auditable
Run any type of analytics from DW to Predictive
Data
Warehousing
Analytics Machine
Learning
Data lake
40. Any type of analytics on the data lake
Most comprehensive analytics platform
Amazon S3 | AWS Glue
Lake Formation
Data lake
Amazon
Redshift
Amazon
EMR
Amazon
Athena
Amazon
Elasticsearch
Service
Amazon
Kinesis
Amazon
MSK
Amazon
SageMaker
Amazon
Personalize
Amazon
QuickSight
AWS Data
Exchange
Data
Warehousing
Big Data
Processing
Interactive
Query
Operational
Analytics
Real time
Analytics
Predictive
Analytics
RecommendationsVisualizations
Data
Exchange
41. Amazon EMR
Easily Run Spark, Hadoop, Hive, Presto, HBase, and more big data apps on AWS
Low cost
50–80% reduction in costs with
EC2 Spot and Reserved Instances
Per-second billing for flexibility
Use S3 storage
Process data in S3
securely with high performance
using the EMRFS connector
Latest versions
Updated with latest open source
frameworks within 30 days
Fully managed no cluster
setup, node provisioning,
cluster tuning
Easy
42. Performance Improvements in Spark for Amazon EMR
Performance optimized runtime for Apache Spark, 2.6x faster performance at 1/10th the cost
*Based on TPC-DS 3 TB Benchmarking running 6 node
C4x8 extra large clusters and EMR 5.28, Spark 2.4
10,164
16,478
26,478
0 5,000 10,000 15,000 20,000 25,000 30,000
Spark with EMR (with runtime)
3rd party Managed Spark (with their
runtime)
Spark with EMR (without runtime)
Runtime total on 104 queries
(seconds—lower is better)
Runtime optimized for Apache Spark performance
100% compliant with Apache Spark APIs
Best performance
2.6x faster than Spark with EMR without runtime
1.6x faster than 3rd party Managed Spark (with their runtime)
Lowest price
1/10th the cost of 3rd party Managed Spark (with their runtime)
NEW
43. Amazon EMR on AWS Outposts
Launch EMR in your data centers with AWS Outpost
Integrate with existing on-premises Hadoop deployments
Deploy secure, managed, EMR clusters in minutes
Process and analyze data on-premises on AWS Outpost
EMR
Hadoop + Spark
AWS
Outposts
On-premises
Hadoop/Spark
GA
NEW
44. Amazon Athena
Pay per query
Pay only for queries run
Save 30–90% on per-query costs
through compression
Use S3 storage
ANSI SQL
JDBC/ODBC drivers
Multiple formats,
compression types, and
complex joins and data types
SQL
Serverless: zero infrastructure,
zero administration
Integrated with QuickSight
EasyQuery instantly
Zero setup cost
Point to S3 and start querying
Serverless, interactive query service
45. Amazon Athena Federated Query
Run SQL queries on data spanning multiple data stores
Redshift
Data warehousing
ElastiCache
Redis
Aurora
MySQL, PostgreSQL
DynamoDB
Key value, Document
DocumentDB
Document
S3/Glacier
Run connectors in AWS Lambda: no servers to manage
Run SQL queries on relational, non-relational, object,
or custom data sources; in the cloud or on-premises
Open Source Connectors for common data sources
Build connectors to custom data sources
PREVIEW
NEW
46. Amazon QuickSight
First BI service built for the cloud with pay-per-session pricing & ML insights for everyone
Elastic Scaling
Auto-scale 10 to 10K+
users in minutes
Pay-as-you-go
Serverless
Create dashboards in
minutes
Deploy globally
without provisioning a
single server
Deeply integrated
with AWS services
Secure, Private access to
AWS data
Integrated S3 data lake
permissions through AWS IAM
API Support
Programmatically onboard users
and manage content
Easily embed in your apps
NEW
47. ML predictions in Amazon QuickSight (preview)
AWS/On-premise data sources
• Excel
• CSV
• MySQL
• PostgreSQL
• Maria DB
• Presto
• Spark
• SQL Server
• Amazon
Redshift
• RDS
• S3
• Athena
• Aurora
• EMR
• Snowflake
• Teradata
• Salesforce
• Square
• Adobe
Analytics
• Jira
• ServiceNow
• Twitter
• GitHub
1 Connect to any data:
Data lakes, SQL engines, 3rd
party applications and on-
premises databases
2 Select an ML model:
Create models with Amazon
SageMaker AutoPilot, existing
custom models and packaged
models from AWS Marketplace.
Custom
Models
QuickSight
Amazon
SageMaker
AutoPilot
Models
AWS
Marketplace
3 Visualize and share:
Analyze results, create
visualizations, build dashboards
/ email reports and share to
business stakeholders
NEW
48. Data exchange: AWS Data Exchange
Easily find and subscribe to 3rd-party data in the cloud
Efficiently access
3rd party data
Simplifies access to data: No
need to receive physical media,
manage FTP credentials, or
integrate with different APIs
Minimize legal reviews and
negotiations
Quickly find diverse
data in one place
>1,000 data products
>80 data providers including
include Dow Jones, Change
Healthcare, Foursquare, Dun
& Bradstreet, Thomson
Reuters, Pitney Bowes, Lexis
Nexis, and Deloitte
Easily analyze data
Download or copy data to S3
Combine, analyze, and model
with existing data
Analyze data with EMR,
Redshift, Athena, and AWS
Glue
GA
NEW
49. Our portfolio
Broad and deep portfolio, purpose-built for builders
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams | Managed Streaming for Kafka
Data Movement
Data Lake
Business Intelligence & Machine Learning
Data Exchange
Data exchange
NEW
QuickSight
Visualizations
SageMaker
ML
Comprehend
NLP
Transcribe
Speech-to-text
Textract
Extract text
Personalize
Recommendation
Forecast
Forecasts
Translate
Translation
CodeGuru
Code reviews
Kendra
Enterprise search
NEW NEW
RDS
MySQL, PostgreSQL,
MariaDB, Oracle, SQL Server,
RDS on VMware
Aurora
MySQL, PostgreSQL
DynamoDB
Key value, Document
ElastiCache
Redis, Memcached
Neptune
Graph
Timestream
Time Series
QLDB
Ledger Database
Analytics Databases
Managed
Blockchain
Blockchain
Templates
Blockchain
Managed Apache
Cassandra Service
Wide column
NEW
DocumentDB
Document
Redshift
Data warehousing
EMR
Hadoop + Spark
Kinesis Data Analytics
Real time
Elasticsearch Service
Operational Analytics
Athena
Interactive analytics
NEW
NEW
NEW
NEW
NEW
AQUA EMR on Outposts
UltraWarm
RDS Proxy
RDS on Outposts
51. Data silos to
OLTP ERP CRM LOB
DW Silo 1
Business
Intelligence
Devices Web Sensors Social
DW Silo 2
Business
Intelligence Machine
learning
BI +
analytics
Data
warehousing
Data lakes
Open formats
Central catalog
Traditional data warehousing approaches don’t scale
52. It’s challenging to manage large Cassandra clusters
Specialized expertise to setup, configure, and maintain infrastructure and software
Scaling clusters is time-consuming, manual, and prone to over-provisioning
Manual backups and error-prone restore process to maintain integrity
Unreliable upgrades with clunky rollback and debugging capabilities