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.
Automate Business Insights on AWS - Simple, Fast, and Secure Analytics PlatformsAmazon Web Services
Business analysts require easy access to data from across different parts of the business. In this session, learn why more customers have adopted Amazon Redshift than any other cloud-native Data Warehouse, and how they are building a broader analytics capability with data lakes on AWS.
Understand how AWS built machine learning (ML) into the services, taking away many of the time-intensive tasks of building an analytics platform. We cover why these customers choose Amazon Redshift for the accessibility to analysts, business reporting, deep security, ability to scale from GB to PB, and integration with the broader platform.
Learn about these customers who are increasingly opening insights to data analysts for data discovery and data scientists for machine learning. We also share how the AWS services such as AWS Glue and the coming ML-enabled AWS Lake Formation take away most of the heavy lifting,
Understand how businesses around the world are running the infrastructure that supports their websites to lower costs, improve time-to-market, and enable rapid scalability matching resource to demands of users.
In the session, we shared the components of a web application such as web server, app server, database, components, application compute, database engine, storage and delivery.
Learn how to build a simple serverless web application using AWS Lambda, Amazon API Gateway, Amazon S3, Amazon DynamoDB, and Amazon Cognito application.
How To Deploy Your File Workloads Quickly & Easily with AWSAmazon Web Services
In this session, learn how to leverage the AWS hybrid storage solutions to move your file based unstructured storage to AWS. We will demystify the complexity in how the AWS services are used and also highlight the value of these services such as AWS Storage Gateway, AWS DataSync and AWS Data Transfer using Secure File Transfer Protocol (SFTP).
AWS Purpose-Built Database Strategy: The Right Tool For The Right JobAmazon Web Services
Learn why AWS is building a comprehensive database and analytics platform with purpose-built databases designed to solve specific customer problems.
We dive deeper into the operational database services that AWS offers, such as Amazon RDS, Amazon DynamoDB, and Amazon ElastiCache. Finally, through two demonstrations, you get to see how easy it is to create a MySQL database and migrating it to Amazon Aurora.
Need for Speed – Intro To Real-Time Data Streaming Analytics on AWS | AWS Sum...AWS Summits
AWS provides multiple ways to ingest and process real-time data generated from sources such as Edge device, logs, websites, mobile apps, IoT devices and more.
In this session we will compare the different tools and technologies and share best practices for when to use what.
The session will cover: Apache Kafka, Kinesis Data Streams/Firehose, MSK (Managed Kafka), Kinesis Data Analytics for SQL and Java (Flink), Apache Spark and more.
In this session, we cover the most common cloud security questions that we hear from customers. We provide detailed answers for each question, distilled from our practical experience working with organizations around the world. This session is for everyone who is curious about the cloud, cautious about the cloud, or excited about the cloud.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
Automate Business Insights on AWS - Simple, Fast, and Secure Analytics PlatformsAmazon Web Services
Business analysts require easy access to data from across different parts of the business. In this session, learn why more customers have adopted Amazon Redshift than any other cloud-native Data Warehouse, and how they are building a broader analytics capability with data lakes on AWS.
Understand how AWS built machine learning (ML) into the services, taking away many of the time-intensive tasks of building an analytics platform. We cover why these customers choose Amazon Redshift for the accessibility to analysts, business reporting, deep security, ability to scale from GB to PB, and integration with the broader platform.
Learn about these customers who are increasingly opening insights to data analysts for data discovery and data scientists for machine learning. We also share how the AWS services such as AWS Glue and the coming ML-enabled AWS Lake Formation take away most of the heavy lifting,
Understand how businesses around the world are running the infrastructure that supports their websites to lower costs, improve time-to-market, and enable rapid scalability matching resource to demands of users.
In the session, we shared the components of a web application such as web server, app server, database, components, application compute, database engine, storage and delivery.
Learn how to build a simple serverless web application using AWS Lambda, Amazon API Gateway, Amazon S3, Amazon DynamoDB, and Amazon Cognito application.
How To Deploy Your File Workloads Quickly & Easily with AWSAmazon Web Services
In this session, learn how to leverage the AWS hybrid storage solutions to move your file based unstructured storage to AWS. We will demystify the complexity in how the AWS services are used and also highlight the value of these services such as AWS Storage Gateway, AWS DataSync and AWS Data Transfer using Secure File Transfer Protocol (SFTP).
AWS Purpose-Built Database Strategy: The Right Tool For The Right JobAmazon Web Services
Learn why AWS is building a comprehensive database and analytics platform with purpose-built databases designed to solve specific customer problems.
We dive deeper into the operational database services that AWS offers, such as Amazon RDS, Amazon DynamoDB, and Amazon ElastiCache. Finally, through two demonstrations, you get to see how easy it is to create a MySQL database and migrating it to Amazon Aurora.
Need for Speed – Intro To Real-Time Data Streaming Analytics on AWS | AWS Sum...AWS Summits
AWS provides multiple ways to ingest and process real-time data generated from sources such as Edge device, logs, websites, mobile apps, IoT devices and more.
In this session we will compare the different tools and technologies and share best practices for when to use what.
The session will cover: Apache Kafka, Kinesis Data Streams/Firehose, MSK (Managed Kafka), Kinesis Data Analytics for SQL and Java (Flink), Apache Spark and more.
In this session, we cover the most common cloud security questions that we hear from customers. We provide detailed answers for each question, distilled from our practical experience working with organizations around the world. This session is for everyone who is curious about the cloud, cautious about the cloud, or excited about the cloud.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This presentation will guide you though the best practices for designing a robust data architecture, highlightning the benefits and typical challenges of data lakes and data warehouses. We will build a scalable solution based on managed services such as Amazon Athena, AWS Glue, and AWS Lake Formation.
Introducing Open Distro for Elasticsearch - ADB201 - Chicago AWS SummitAmazon Web Services
Open Distro for Elasticsearch is a 100% open-source distribution of Elasticsearch, the popular search and analytics engine. In this session, you explore its many new advanced features, previously available only in commercial software, including encryption in-transit, role-based access control (RBAC), event monitoring and alerting, SQL support, cluster diagnostics, and more. Open Distro for Elasticsearch is licensed under Apache 2.0, so you can view, use, modify, and distribute the code without any restrictions. We also show you how you can join the Open Distro for Elasticsearch community to accelerate open innovation for Elasticsearch.
AWS Purpose-Built Database Strategy: The Right Tool for The Right JobAmazon Web Services
Learn why AWS is building a comprehensive database and analytics platform with purpose-built databases designed to solve specific customer problems.
We dive deeper into the operational database services that AWS offers, such as Amazon RDS, Amazon DynamoDB, Amazon ElastiCache, and the new Amazon Neptune graph database. Finally, through a demonstration of Amazon RDS, you get to see how easy it is to use a managed database service.
No Hassle NoSQL - Amazon DynamoDB & Amazon DocumentDB | AWS Summit Tel Aviv ...AWS Summits
NoSQL databases are a great fit for many modern applications such as mobile, web, and gaming that require flexible, scalable, high-performance, and highly functional databases to provide great user experiences but they can be hard to manage and require high proficiency and attention.In this session we will present Amazon DynamoDB, a fully managed, multi-region, multi-master database that provides consistent single-digit millisecond latency in any scale.
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 165 fully featured services from data centers globally. Millions of customers —including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. This session covers the benefits of cloud computing; our shared responsibility model; and AWS services and infrastructure. Uncover how constant innovation at AWS empowers customers to transform their own organizations.
Learn how governments, research institutions, and private companies are using AWS to share massive amounts of data publicly. Discover best practices for sharing data in the cloud, how to find publicly available datasets through the Registry of Open Data on AWS, and how you can share your own data through AWS.
AWS Immersion Day - Image Data Insights & Analytics Specialist Session - June...Amazon Web Services
Learn how to incorporate video data and analytics into your data management and business decision process. Discover how industry leaders are using AWS to do the heavy lifting with image data and innovating quickly. Our specialists will cover common issues and provide best practices from using IoT devices to collect data to training a ML model to using these models on the edge without network connectivity.
Building with AWS Databases: Match Your Workload to the Right Database | AWS ...AWS Summits
In this session we will discuss the ideal use cases for relational and nonrelational data services, including Amazon ElastiCache for Redis, Amazon DynamoDB, Amazon Aurora, Amazon Neptune, Amazon ElasticSearch Service, Amazon TimeStream, Amazon QLDB, and Amazon DocumentDB. This session will focus on how to evaluate a new workload for the best managed database option.
Let the data decide!
Amazon Relational Database Service (RDS)
Demo - Deploy Multi-AZ database in VPC
Amazon DynamoDB (NoSQL)
Intro to AWS Athena and Redshift
Big Data on AWS - To infinity and beyond! - Tel Aviv Summit 2018Amazon Web Services
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.
In this session, we will introduce the Data Lake concept and its implementation on AWS.
We will explain the different roles our services play and how they fit into the Data Lake picture.
Learn about data lifecycle best practices in the AWS Cloud. Discover how to optimise performance and lower the costs of data ingestion, staging, storage, cleansing, analytics, visualisation, and archiving.
MassMutual Goes Cloud First with Hybrid Cloud on AWS (ENT210) - AWS re:Invent...Amazon Web Services
In this session, we discuss how MassMutual adopts a cloud-first strategy, and we outline their journey to hybrid cloud on AWS. Specifically, we cover four aspects of MassMutual's hybrid cloud on AWS architecture: First, we talk about the use of the AWS Well-Architected Framework to create MassMutual’s cloud minimal viable product (MVP) document. Next, we do a deep dive into MassMutual's multi-account, multi-region architecture. We discuss achieving cloud governance, risk, and compliance through tooling and automation. Finally, we demonstrate how MassMutual deploys fully compliant hybrid cloud environments in less than five minutes. We also showcase some of MassMutual's actual hybrid deployments and share the benefits of using AWS.
Using automation to drive continuous-compliance best practices - SEC208 - New...Amazon Web Services
Northwestern Mutual’s technology teams maintain a complex compliance environment for a diverse set of developers working within more than 100 AWS accounts. To drive best practices and ensure continuous compliance, the teams designed an AWS-based architecture using services such as AWS Lambda, Amazon DynamoDB, Amazon Simple Queue Service (Amazon SQS), and Amazon CloudWatch to auto-remediate misconfigurations. In this session, learn how these services help Northwestern Mutual swiftly correct configurations and integrate with tools like Slack and PagerDuty to create logs, notify developers and account owners of changes, and track trends in remediation.
How can you accelerate the delivery of new, high-quality services? How can you be able to experiment and get feedback quickly from your customers? To get the most out of the agility afforded by serverless and containers, it is essential to build CI/CD pipelines that help teams iterate on code and quickly release features. In this talk, we demonstrate how developers can build effective CI/CD release workflows to manage their serverless or containerized deployments on AWS. We cover infrastructure-as-code (IaC) application models, such as AWS Serverless Application Model (AWS SAM) and new imperative IaC tools. We also demonstrate how to set up CI/CD release pipelines with AWS CodePipeline and AWS CodeBuild, and we show you how to automate safer deployments with AWS CodeDeploy.
Optimizing data lakes with Amazon S3 - STG302 - New York AWS SummitAmazon Web Services
Data comes in many different forms that don’t easily fit into a traditional database structure. This is where data lakes help, enabling you to store vast amounts of data in its raw form. In this session, AWS experts dive into the benefits of Amazon S3 for building and managing data lakes in the AWS Cloud. Learn about the Amazon S3 integrations with the AWS analytics suite and Amazon FSx for Lustre. Also learn how to seamlessly run big data analytics, high performance computing applications, machine learning training models, media data processing workloads, and more, across your Amazon S3 data lakes.
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.
AWS Analytics Services - When to use what? | AWS Summit Tel Aviv 2019AWS Summits
AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost.
In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.
Introducing Open Distro for Elasticsearch - ADB201 - Chicago AWS SummitAmazon Web Services
Open Distro for Elasticsearch is a 100% open-source distribution of Elasticsearch, the popular search and analytics engine. In this session, you explore its many new advanced features, previously available only in commercial software, including encryption in-transit, role-based access control (RBAC), event monitoring and alerting, SQL support, cluster diagnostics, and more. Open Distro for Elasticsearch is licensed under Apache 2.0, so you can view, use, modify, and distribute the code without any restrictions. We also show you how you can join the Open Distro for Elasticsearch community to accelerate open innovation for Elasticsearch.
AWS Purpose-Built Database Strategy: The Right Tool for The Right JobAmazon Web Services
Learn why AWS is building a comprehensive database and analytics platform with purpose-built databases designed to solve specific customer problems.
We dive deeper into the operational database services that AWS offers, such as Amazon RDS, Amazon DynamoDB, Amazon ElastiCache, and the new Amazon Neptune graph database. Finally, through a demonstration of Amazon RDS, you get to see how easy it is to use a managed database service.
No Hassle NoSQL - Amazon DynamoDB & Amazon DocumentDB | AWS Summit Tel Aviv ...AWS Summits
NoSQL databases are a great fit for many modern applications such as mobile, web, and gaming that require flexible, scalable, high-performance, and highly functional databases to provide great user experiences but they can be hard to manage and require high proficiency and attention.In this session we will present Amazon DynamoDB, a fully managed, multi-region, multi-master database that provides consistent single-digit millisecond latency in any scale.
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 165 fully featured services from data centers globally. Millions of customers —including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. This session covers the benefits of cloud computing; our shared responsibility model; and AWS services and infrastructure. Uncover how constant innovation at AWS empowers customers to transform their own organizations.
Learn how governments, research institutions, and private companies are using AWS to share massive amounts of data publicly. Discover best practices for sharing data in the cloud, how to find publicly available datasets through the Registry of Open Data on AWS, and how you can share your own data through AWS.
AWS Immersion Day - Image Data Insights & Analytics Specialist Session - June...Amazon Web Services
Learn how to incorporate video data and analytics into your data management and business decision process. Discover how industry leaders are using AWS to do the heavy lifting with image data and innovating quickly. Our specialists will cover common issues and provide best practices from using IoT devices to collect data to training a ML model to using these models on the edge without network connectivity.
Building with AWS Databases: Match Your Workload to the Right Database | AWS ...AWS Summits
In this session we will discuss the ideal use cases for relational and nonrelational data services, including Amazon ElastiCache for Redis, Amazon DynamoDB, Amazon Aurora, Amazon Neptune, Amazon ElasticSearch Service, Amazon TimeStream, Amazon QLDB, and Amazon DocumentDB. This session will focus on how to evaluate a new workload for the best managed database option.
Let the data decide!
Amazon Relational Database Service (RDS)
Demo - Deploy Multi-AZ database in VPC
Amazon DynamoDB (NoSQL)
Intro to AWS Athena and Redshift
Big Data on AWS - To infinity and beyond! - Tel Aviv Summit 2018Amazon Web Services
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.
In this session, we will introduce the Data Lake concept and its implementation on AWS.
We will explain the different roles our services play and how they fit into the Data Lake picture.
Learn about data lifecycle best practices in the AWS Cloud. Discover how to optimise performance and lower the costs of data ingestion, staging, storage, cleansing, analytics, visualisation, and archiving.
MassMutual Goes Cloud First with Hybrid Cloud on AWS (ENT210) - AWS re:Invent...Amazon Web Services
In this session, we discuss how MassMutual adopts a cloud-first strategy, and we outline their journey to hybrid cloud on AWS. Specifically, we cover four aspects of MassMutual's hybrid cloud on AWS architecture: First, we talk about the use of the AWS Well-Architected Framework to create MassMutual’s cloud minimal viable product (MVP) document. Next, we do a deep dive into MassMutual's multi-account, multi-region architecture. We discuss achieving cloud governance, risk, and compliance through tooling and automation. Finally, we demonstrate how MassMutual deploys fully compliant hybrid cloud environments in less than five minutes. We also showcase some of MassMutual's actual hybrid deployments and share the benefits of using AWS.
Using automation to drive continuous-compliance best practices - SEC208 - New...Amazon Web Services
Northwestern Mutual’s technology teams maintain a complex compliance environment for a diverse set of developers working within more than 100 AWS accounts. To drive best practices and ensure continuous compliance, the teams designed an AWS-based architecture using services such as AWS Lambda, Amazon DynamoDB, Amazon Simple Queue Service (Amazon SQS), and Amazon CloudWatch to auto-remediate misconfigurations. In this session, learn how these services help Northwestern Mutual swiftly correct configurations and integrate with tools like Slack and PagerDuty to create logs, notify developers and account owners of changes, and track trends in remediation.
How can you accelerate the delivery of new, high-quality services? How can you be able to experiment and get feedback quickly from your customers? To get the most out of the agility afforded by serverless and containers, it is essential to build CI/CD pipelines that help teams iterate on code and quickly release features. In this talk, we demonstrate how developers can build effective CI/CD release workflows to manage their serverless or containerized deployments on AWS. We cover infrastructure-as-code (IaC) application models, such as AWS Serverless Application Model (AWS SAM) and new imperative IaC tools. We also demonstrate how to set up CI/CD release pipelines with AWS CodePipeline and AWS CodeBuild, and we show you how to automate safer deployments with AWS CodeDeploy.
Optimizing data lakes with Amazon S3 - STG302 - New York AWS SummitAmazon Web Services
Data comes in many different forms that don’t easily fit into a traditional database structure. This is where data lakes help, enabling you to store vast amounts of data in its raw form. In this session, AWS experts dive into the benefits of Amazon S3 for building and managing data lakes in the AWS Cloud. Learn about the Amazon S3 integrations with the AWS analytics suite and Amazon FSx for Lustre. Also learn how to seamlessly run big data analytics, high performance computing applications, machine learning training models, media data processing workloads, and more, across your Amazon S3 data lakes.
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.
AWS Analytics Services - When to use what? | AWS Summit Tel Aviv 2019AWS Summits
AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost.
In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.
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.
Build Data Lakes and Analytics on AWS: Patterns & Best PracticesAmazon Web Services
With over 90% of today’s data generated in the last two years, the rate of data growth is showing no sign of slowing down. In this session, we step through the challenges and best practices for capturing data, understanding what data you own, driving insights, and predicting the future using AWS services. We frame the session and demonstrations around common pitfalls of building data lakes and how to successfully drive analytics and insights from data. We also discuss the architecture patterns brought together key AWS services, including Amazon S3, AWS Glue, Amazon Athena, Amazon Kinesis, and Amazon Machine Learning. Discover the real-world application of data lakes for roles including data scientists and business users.
Stephen Moon, Sr. Solutions Architect, Amazon Web Services
James Juniper, Solution Architect for the Geo-Community Cloud, Natural Resources Canada
Build Data Lakes & Analytics on AWS: Patterns & Best PracticesAmazon Web Services
With over 90% of today’s data generated in the last two years, the rate of data growth is showing no sign of slowing down. In this session, we step through the challenges and best practices for capturing data, understanding what data you own, driving insights, and predicting the future using AWS services. We frame the session and demonstrations around common pitfalls of building data lakes and how to successfully drive analytics and insights from data. We also discuss the architecture patterns brought together key AWS services, including Amazon S3, AWS Glue, Amazon Athena, Amazon Kinesis, and Amazon Machine Learning. Discover the real-world application of data lakes for roles including data scientists and business users.
Stephen Moon, Sr. Solutions Architect, Amazon Web Services
James Juniper, Solution Architect for the Geo-Community Cloud, Natural Resources Canada
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.
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 Machine Learning (Amazon ML) services work together to build a successful data lake for various roles, including data scientists and business users.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Floor 28 - Building Data lake on AWSAdir Sharabi
AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session we will talk about how to improve over time using your data. How do you take your everyday data and build relevant business insights, to help and continuously improve your business processes, and keep your innovation going based on your data.
Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...Amazon Web Services
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.
Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...Amazon Web Services
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.
Learn about data lifecycle best practices in the AWS Cloud, so you can optimize performance and lower the costs of data ingestion, staging, storage, cleansing, analytics and visualization, and archiving.
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.
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.
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.
Come costruire un'architettura Serverless nel Cloud AWS
Building Data Lakes and Analytics on AWS
1. Build Data Lakes and Analytics on AWS
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
2. VisualizationVariability
Data Is Defined Many Different Ways
Volume Velocity Variety Veracity Value
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3. Data Is Changing à Analytics Are Adopting
Capture and store
new data at PB-EB
scale
Do new type of analytics
in a cost effective way
• Machine learning
• Big data processing
• Real-time analytics
• Full-text search
New types of
analytics
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
4. Public Sector entities that successfully generate value from their data will be
able to offer better citizen services and data driven decisions
Most Important: Driving Value from Data
What are those use cases?
Analytics
Smart Cities
AI/ML Data lake
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
5. *Aberdeen: Angling for Insight in Today’s Data Lake, Michael Lock, SVP Analytics and Business Intelligence
What is Data Lake?
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
6. Traditionally, Analytics Used to Look Like This
OLTP ERP CRM LOB
Data warehouse
Business intelligence • Relational data
• TBs–PBs scale
• Schema defined prior to data load
• Operational reporting and ad hoc
• Large initial CAPEX + $10K–$50K/TB/year
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
7. Data Lakes Extend the Traditional Approach
Data warehouse
Business intelligence
OLTP ERP CRM LOB
• Relational and nonrelational data
• TBs–EBs scale
• Diverse analytical engines
• Low-cost storage & analytics
Devices Web Sensors Social
Data lake
Big data processing,
real-time, machine learning
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
8. A Data Lake is not an Enterprise Data Warehouse
Complementary to EDW (not replacement) EDW can be sourced from Data Lake
Schema on read (no predefined schemas) Schema on write (predefined schemas)
Structured/semi-structured/Unstructured data Structured data only
Fast ingestion of new data/content Time consuming to introduce new content
Data Science + Prediction/Advanced Analytics + BI use
cases
BI use cases
Data at low level of detail/granularity Data at summary/aggregated level of detail
Loosely defined SLAs Tight SLAs (production schedules)
Flexibility in tools (open source/tools for advanced
analytics)
Limited flexibility in tools (SQL only)
Elastic storage and compute capacity – decoupled
Explicitly sized environments, compute and storage
scaled in linearly
Data Lake EDW
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
9. Data Lakes from AWS
Analytics
• Unmatched durability, and availability at EB scale
• Best security, compliance, and audit capabilities
• Object-level controls for fine-grain access
• Fastest performance by retrieving subsets of data
• The most ways to bring data in
• 2x as many integrations with partners
• Analyze with broadest set of analytics & ML services
Machine
learning
Real-time dataOn-premises
Data Lake
on AWS
movementdata movement
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
10. Managed ML Service
Deep Learning AMIs
Video and Image Recognition
Conversational Interfaces
Deep-Learning Video Camera
Natural Language Processing
Language Translation
Speech Recognition
Text-to-Speech
Interactive Analysis
Hadoop & Spark
Data Warehousing
Full-text search
Real-time analytics
Dashboards & Visualizations
Dedicated Network connection
Secure appliances
Ruggedized Shipping Container
Database migration
Connect Devices to AWS
Real-time Data Streams
Real-time Video Streams
Data Lake
on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine learning
Real-time dataOn-premises movementdata movement
Data Lakes, Analytics, and IoT Portfolio from AWS
Broadest, deepest set of analytic services
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
11. Data Lakes, Analytics, and IoT Portfolio from AWS
Broadest, deepest set of analytic services
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch Service
Amazon Kinesis
Amazon QuickSight
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data Lake
on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine learning
Real-time dataOn-premises movementdata movement
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
12. How do I ingest my data?
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
13. How do I drive value?
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch Service
Amazon Kinesis
Amazon QuickSight
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data Lake on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine learning
Real-time data movementTraditional data movement
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
14. Ingest data based on the type of data
Open and comprehensive
• Data movement from on-premises
datacenters
• Dedicated network connection
• Secure appliances
• Ruggedized shipping container
• Database migration
• Gateway that lets applications write to the cloud
• Data movement from real-time sources
• Connect devices to AWS
• Real-time data streams
• Real-time video streams
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS Storage Gateway
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data movement from
real-time sources
Data movement from
your datacenters
Amazon S 3
Amazon Gl ac ier
AWS Gl u e
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
15. Real-time data movement and data lakes on
AWS
Amazon
Kinesis Data
Firehose
AWS Glue
Data Catalog
Amazon
S3 Data
Data Lake
on AWS
Amazon
Kinesis Data
Streams
Data definitionKinesis Agent
Apache Kafka
AWS SDK
LOG4J
Flume
Fluentd
AWS Mobile SDK
Kinesis Producer Library
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
16. IMPORTANT: Ingest data in its raw form …
Open and comprehensive
Amazon S 3
Amazon Gl ac ier
AWS Gl u e
• Store the data in its raw form:
• BEFORE
• Transforming
• Analyzing
• Manipulating
• Doing … anything … to it
CSV
ORC
Grok
Avro
Parquet
JSON
• This becomes your source of record you can
always go back to …
• Lifecycle policies allow you to shift it to
warm and cold storage.
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
17. Preparing raw data for consumption
Raw data stored in Data Lake:
Preparation:
Normalized
Partitioned
Compressed
Storage Optimized
Extract – Load – Transform
Raw
Ingestion
Curated
DataSets
Data Catalog
ELT
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
18. Which tool should I use to
analyze my data?
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
19. How do I drive value?
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch Service
Amazon Kinesis
Amazon QuickSight
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data Lake
on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine Learning
Real-time dataTraditional movementdata movement
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
20. Different tools for different users…
Business
Reporting
Data
Catalog
Central
Storage
SagemakerMachine Learning/Deep Learning
Data Scientists
Data Engineer
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21. Amazon Athena – interactive analysis
Interactive query service to analyze data in Amazon S3 using standard SQL
No infrastructure to set up or manage and no data to load
Ability to run SQL queries on data archived in Amazon Glacier (coming soon)
$ SQL
Query instantly
Zero setup cost; just
point to Amazon S3
and start querying
Pay per query
Pay only for queries run;
save 30%–90% on per-
query costs through
compression
Open
ANSI SQL interface,
JDBC/ODBC drivers, multiple
formats, compression types,
and complex joins and data
types
Easy
Serverless: zero
infrastructure, zero
administration
Integrated with Amazon
QuickSight
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
22. Amazon EMR – big data processing
Analytics and ML at scale
19 open-source projects: Apache Hadoop, Spark, HBase, Presto, and more
Enterprise-grade security
$
Latest versions
Updated with the latest
open source frameworks
within 30 days of release
Low cost
Flexible billing with per-
second billing, Amazon
EC2 Spot, Reserved
Instances, and Auto
Scaling to reduce costs
50%-80%
Use Amazon S3 storage
Process data directly in
the Amazon S3 data lake
securely with high
performance using the
EMRFS connector
Easy
Launch fully managed
Hadoop & Spark in minutes;
no cluster setup, node
provisioning, cluster tuning
Data Lake
100110000100101011100
1010101110010101000
00111100101100101
010001100001
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
23. Amazon Redshift – data warehousing
Fast, powerful, simple, and fully managed data warehouse at 1/10 the cost
Massively parallel, scale from gigabytes to petabytes
Fast at scale
Columnar storage
technology to improve I/O
efficiency and scale query
performance
$
Inexpensive
As low as $1,000 per
terabyte per year, 1/10 the
cost of traditional data
warehouse solutions; start
at $0.25 per hour
Open file formats Secure
Audit everything; encrypt
data end-to-end;
extensive certification and
compliance
Analyze optimized data
formats on the latest SSD,
and all open data formats in
Amazon S3
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24. Machine Learning & Big Data
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25. Data Lakes driving Machine Learning
Better
Decisions
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Better
Products Machine Learning
Deep Learning/ AI
More
Users
More
Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data
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26. Agility in Machine Learning
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch Service
Amazon Kinesis
Amazon QuickSight
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data Lake
on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine Learning
Real-time dataOn-premises movementdata movement
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27. Varied ML Use Cases
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28. Broadest and deepest set of capabilities
The Amazon ML Stack
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29. Modern data architecture for media enrichment
Insights to enhance viewer engagement, personalization, monetization
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30. Activity Highlights and Suspicious Activity Pathing
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31. Sentiment analysis
Discover insights and relationships in text, Social media, etc..
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
32. In Summary…
• Data lakes and data warehouses complement each
other
• Loose coupling, but highly performant
• Storage, analytics, metadata management, etc..
• Future-proof your analytics
• Choosing the best tool for the job
• Elasticity and multiple clusters for dedicated purposes
• Don’t forget metadata management
@2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
33. *Aberdeen: Angling for Insight in Today’s Data Lake, Michael Lock, SVP Analytics and Business Intelligence
Do you want to build
Data Lake?
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