The document discusses Amazon's use of AWS analytics services. It describes how Amazon has transitioned from a traditional data warehouse to using a data lake on AWS services like S3, Redshift, EMR and others. The data lake called "Andes" stores current and historical data from various sources for analytics. Teams can subscribe to data in Andes and use services like Redshift and EMR to analyze the data. This architecture scales with Amazon's business needs.
As the volume and types of data continues to grow, customers often have valuable data that is not easily discoverable and available for analytics. A common challenge for data engineering teams is architecting a data lake that can cater to the needs of diverse users - from developers to business analysts to data scientists. In this session, dive deep into building a data lake using Amazon S3, Amazon Kinesis, Amazon Athena and AWS Glue. Learn how AWS Glue crawlers can automatically discover your data, extracting and cataloguing relevant metadata to reduce operations in preparing your data for downstream consumers.
Building Serverless Analytics Pipelines with AWS Glue (ANT308) - AWS re:Inven...Amazon Web Services
Organizations need to gain insight and knowledge from a growing number of IoT, APIs, clickstreams, and unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, we cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL pipelines for your data lake. We also discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.
AWS delivers an integrated suite of services that provide everything needed to quickly and easily build and manage a data lake for analytics. AWS-powered data lakes can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot. In this session, we will show you how you can quickly build a data lake on AWS that ingests, catalogs and processes incoming data and makes it ready for analysis. Using a live demo, we demonstrate the capabilities of AWS provided analytical services such as AWS Glue, Amazon Athena and Amazon EMR and how to build a Data Lake on AWS step-by-step.
Learning Objectives:
- Learn the common use-cases for using Athena, AWS' interactive query service on S3
- Learn best practices for creating tables and partitions and performance optimizations
- Learn how Athena handles security, authorization, and authentication
As the volume and types of data continues to grow, customers often have valuable data that is not easily discoverable and available for analytics. A common challenge for data engineering teams is architecting a data lake that can cater to the needs of diverse users - from developers to business analysts to data scientists. In this session, dive deep into building a data lake using Amazon S3, Amazon Kinesis, Amazon Athena and AWS Glue. Learn how AWS Glue crawlers can automatically discover your data, extracting and cataloguing relevant metadata to reduce operations in preparing your data for downstream consumers.
Building Serverless Analytics Pipelines with AWS Glue (ANT308) - AWS re:Inven...Amazon Web Services
Organizations need to gain insight and knowledge from a growing number of IoT, APIs, clickstreams, and unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, we cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL pipelines for your data lake. We also discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.
AWS delivers an integrated suite of services that provide everything needed to quickly and easily build and manage a data lake for analytics. AWS-powered data lakes can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot. In this session, we will show you how you can quickly build a data lake on AWS that ingests, catalogs and processes incoming data and makes it ready for analysis. Using a live demo, we demonstrate the capabilities of AWS provided analytical services such as AWS Glue, Amazon Athena and Amazon EMR and how to build a Data Lake on AWS step-by-step.
Learning Objectives:
- Learn the common use-cases for using Athena, AWS' interactive query service on S3
- Learn best practices for creating tables and partitions and performance optimizations
- Learn how Athena handles security, authorization, and authentication
Data processing and analysis is where big data is most often consumed - driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing and Interactive analytics. AWS services to be covered include: Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
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.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch five years ago, AWS customers have launched more than 5.5 million Hadoop clusters.
In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Speakers:
Ian Meyers, AWS Solutions Architect
Ian McDonald, IT Director, SwiftKey
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.
Level: Intermediate
Speakers:
Ryan Malecky - Solutions Architect, EdTech, AWS
Rajakumar Sampathkumar - Sr. Technical Account Manager, AWS
Cloud Based Business Intelligence with Amazon QuickSight - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Connect QuickSight to your data (Redshift, Athena, S3, RDS, Private VPCs, On-Premise databases)
- Create interactive dashboards
- Publish reports and dashboards at scale (Row Level Security, AD integration, Groups, User Management)
Amazon Kinesis provides services for you to work with streaming data on AWS. Learn how to load streaming data continuously and cost-effectively to Amazon S3 and Amazon Redshift using Amazon Kinesis Firehose without writing custom stream processing code. Get an introduction to building custom stream processing applications with Amazon Kinesis Streams for specialized needs.
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
AWS Lambda enables developers to build scalable application components with minimal effort. With Step Functions, we can solve the challenge of building large distributed applications using visual workflows. In this session, learn how to get started with Step Functions, and understand how to use them to take your Lambda-based applications to the next level. We start with a few granular functions and stitch them up using Step Functions. As we build the application, we'll add monitoring to ensure that the changes we make result in improvements.
by Joyjeet Banerjee, Solutions Architect, AWS
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3. Level 200
In this session, we will show you how easy it is to start querying your data stored in Amazon S3, with Amazon Athena. First we will use Athena to create the schema for data already in S3. Then, we will demonstrate how you can run interactive queries through the built-in query editor. We will provide best practices and use cases for Athena. Then, we will talk about supported queries, data formats, and strategies to save costs when querying data with Athena.
Migrate from Oracle to Aurora PostgreSQL: Best Practices, Design Patterns, & ...Amazon Web Services
In this session, we show you how to set the source Oracle database environment, the target PostgreSQL environment, and parameter group configuration. We also recommended database parameters to disable foreign keys and triggers. Finally, we discuss best practices for using AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT) and show you how to choose the instance type and configure AWS DMS.
AWS Cost Management Workshop at the San Francisco Loft
AWS offers a number of products that allow you to access, organize, understand, optimize, and control your AWS costs and usage. This workshop will help you get started using AWS Cost Explorer to visualize your usage patterns and identify your underlying cost drivers. From there, you can take action on your insights by learning how to set custom cost and usage budgets and receive alerts via email or Amazon SNS topic using AWS Budgets.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
by Darin Briskman, Database, Analytics, and Machine Learning AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Zehra Syeda-Sarwat, Program Manager Strategist, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
Data processing and analysis is where big data is most often consumed - driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing and Interactive analytics. AWS services to be covered include: Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
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.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch five years ago, AWS customers have launched more than 5.5 million Hadoop clusters.
In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Speakers:
Ian Meyers, AWS Solutions Architect
Ian McDonald, IT Director, SwiftKey
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.
Level: Intermediate
Speakers:
Ryan Malecky - Solutions Architect, EdTech, AWS
Rajakumar Sampathkumar - Sr. Technical Account Manager, AWS
Cloud Based Business Intelligence with Amazon QuickSight - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Connect QuickSight to your data (Redshift, Athena, S3, RDS, Private VPCs, On-Premise databases)
- Create interactive dashboards
- Publish reports and dashboards at scale (Row Level Security, AD integration, Groups, User Management)
Amazon Kinesis provides services for you to work with streaming data on AWS. Learn how to load streaming data continuously and cost-effectively to Amazon S3 and Amazon Redshift using Amazon Kinesis Firehose without writing custom stream processing code. Get an introduction to building custom stream processing applications with Amazon Kinesis Streams for specialized needs.
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
AWS Lambda enables developers to build scalable application components with minimal effort. With Step Functions, we can solve the challenge of building large distributed applications using visual workflows. In this session, learn how to get started with Step Functions, and understand how to use them to take your Lambda-based applications to the next level. We start with a few granular functions and stitch them up using Step Functions. As we build the application, we'll add monitoring to ensure that the changes we make result in improvements.
by Joyjeet Banerjee, Solutions Architect, AWS
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3. Level 200
In this session, we will show you how easy it is to start querying your data stored in Amazon S3, with Amazon Athena. First we will use Athena to create the schema for data already in S3. Then, we will demonstrate how you can run interactive queries through the built-in query editor. We will provide best practices and use cases for Athena. Then, we will talk about supported queries, data formats, and strategies to save costs when querying data with Athena.
Migrate from Oracle to Aurora PostgreSQL: Best Practices, Design Patterns, & ...Amazon Web Services
In this session, we show you how to set the source Oracle database environment, the target PostgreSQL environment, and parameter group configuration. We also recommended database parameters to disable foreign keys and triggers. Finally, we discuss best practices for using AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT) and show you how to choose the instance type and configure AWS DMS.
AWS Cost Management Workshop at the San Francisco Loft
AWS offers a number of products that allow you to access, organize, understand, optimize, and control your AWS costs and usage. This workshop will help you get started using AWS Cost Explorer to visualize your usage patterns and identify your underlying cost drivers. From there, you can take action on your insights by learning how to set custom cost and usage budgets and receive alerts via email or Amazon SNS topic using AWS Budgets.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
by Darin Briskman, Database, Analytics, and Machine Learning AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Zehra Syeda-Sarwat, Program Manager Strategist, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
A Look Under the Hood – How Amazon.com Uses AWS Services for Analytics at Mas...Amazon Web Services
Amazon’s consumer business continues to grow, and so does the volume of data and the number and complexity of the analytics done in support of the business. In this session, we talk about how Amazon.com uses AWS technologies to build a scalable environment for data and analytics. We look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel, scalable compute engines such as Amazon EMR and Amazon Redshift.
MSC203_How Citrix Uses AWS Marketplace Solutions To Accelerate Analytic Workl...Amazon Web Services
Find out how Citrix built a solution using Matillion ETL for Amazon Redshift from AWS Marketplace to load all data into an Amazon Redshift cluster, allowing them to do their analytics on the entire environment at a single time. We’ll discuss the transition made to consolidate multiple disparate databases in order to run analytic workloads, get a holistic view of all their data sources, and prevent inconsistent data from being captured.
How Citrix Uses AWS Marketplace Solutions to Accelerate Analytic Workloads on...Amazon Web Services
Find out how Citrix built a solution using Matillion ETL for Amazon Redshift from AWS Marketplace to load all data into an Amazon Redshift cluster, allowing them to do their analytics on the entire environment at a single time. We’ll discuss the transition made to consolidate multiple disparate databases in order to run analytic workloads, get a holistic view of all their data sources, and prevent inconsistent data from being captured.
Increasingly, valuable customer data sources are dispersed among on-premises data centers, SaaS providers, partners, third-party data providers, and public datasets. Building a data lake on AWS offers a foundation for storing on-premises, third-party, and public datasets cost effectively with high performance. This workshop introduces AWS tools and technologies you can use to analyze and extract value from petabyte-scale datasets, including Amazon Athena and Amazon Redshift Spectrum.
Database Freedom. Database migration approaches to get to the Cloud - Marcus ...Amazon Web Services
Databases are at the heart of many of the software systems we build. Learn how to achieve the benefits of moving your core datastores to the AWS platform, by designing a migration model that retires tech debt and sets the platform for innovation. In this session we'll cover the different database options available on AWS, how you can start migrating your databases to the cloud, and cover tools like the AWS Database Migration Service (DMS) to largely automate this work for you.
Cloud computing gives you a number of advantages, such as the ability to scale your web application or website on demand. If you have a new web application and want to use cloud computing, you might be asking yourself, "Where do I start?" Join us in this session to understand best practices for scaling your resources from one to millions of users. We show you how to best combine different AWS services, how to make smarter decisions for architecting your application, and how to scale your infrastructure in the cloud.
This is your chance to learn directly from top CTOs and Cloud Architects from some of the most innovative AWS customers. In this lightning round session, we'll have an action-packed hour, jumping straight to the architecture and technical detail for some of the most innovative data storage solutions of 2017. Hear how Insitu collects and analyzes data from drone flights in the field with AWS Snowball Edge. See how iRobot collects and analyzes IoT data from their robotic vacuums, mops, and pool cleaners. Learn how Viber maintains a petabyte-scale data lake on Amazon S3. Understand how Alert Logic scales their massive SaaS cloud security solution on Amazon S3 & Amazon Glacier.
Learn how to reduce development time and innovate on AWS. In this webinar, Beachbody - sellers of fitness, weight loss, and muscle-building home-exercise videos - talks about their experience migrating to a data lake on Amazon Simple Storage Service (Amazon S3) using Talend. Beachbody will describe how they created an open enterprise data platform, giving their employees access to secure, well-governed data, and increasing DevOps efficiency across the entire company.
Automating Big Data Technologies for Faster Time-to-ValueAmazon Web Services
Big data technologies can be extremely complex and require manual operation. If you can intelligently automate your Big Data operations then you can lower your costs, make your team more productive, scale more efficiently, and lower the risk of failure. Demandbase, creator of a targeting and personalization platform for business-to-business (B2B) companies, uses Qubole and a data lake on AWS to reduce the management complexities and costs of processing and analyzing their data. Hear how Qubole empowers Demandbase to analyze trillions of rows of structured and unstructured data in real time, making their data scientists and data engineers productive since day one.
operations then you can lower your costs, make your team more productive, scale more efficiently, and lower the risk of failure. Demandbase, creator of a targeting and personalization platform for business-to-business (B2B) companies, uses Qubole and a data lake on AWS to reduce the management complexities and costs of processing and analyzing their data. Hear how Qubole empowers Demandbase to analyze trillions of rows of structured and unstructured data in real time, making their data scientists and data engineers productive since day one.
Migrating to 21st Century Analytics: Zopa Story
Speakers:
Shafreen Sayyed, Solution Architect, AWS
Varun Gangoor, Senior Big Data Engineer, Zopa
Data makes the world go around these days, and 21st Century Data Analytics means you can store, process and analyze massive amounts of data, often in real time, whilst making that data consumable across diverse groups in your organization. Many traditional tools lock data away in inflexible silos, making this impossible. This session will look at what is needed in a Financial Services organization to achieve a flexible and scalable data architecture, and we will also hear from Zopa, UK's first peer-to-peer lending company, about how they migrated their data analytics estate to AWS and look at what new insight that has given them.
I Want to Analyze and Visualize Website Access Logs, but Why Do I Need Server...Amazon Web Services
Nowadays, it’s common for a web server to be fronted by a global content delivery service, such as Amazon CloudFront, to accelerate delivery of websites, APIs, media content, and other web assets. Website administrators and developers want to generate insights in order to improve website availability through bot detection and mitigation, by optimizing web content based on the devices and browser used, by reducing perceived latency by caching a popular object closer to its viewer, and so on. In this session, we dive deep into building an end-to-end serverless analytics solution to analyze Amazon CloudFront access logs, both at rest and in transit, using Amazon Athena and Amazon Kinesis Analytics, respectively, and we generate visualization insights using Amazon QuickSight. Join a discussion with AWS solution architects to learn more about the various ways to generate insights to improve the overall perceived experience for your website users.
RET301-Build Single Customer View across Multiple Retail Channels using AWS S...Amazon Web Services
A challenge faced by many retailers is how to form an integrated single view of the customer across multiple retail channels to help you better understand purchasing behavior & patterns. In this session, we will present a solution that merges web analytics data with customer purchase history based on AWS API Gateway, Lambda and S3. Learn how to track customer purchase behaviors across different selling channels to better predict future needs and make relevant, intelligent recommendations.
One of the biggest tradeoffs customers usually make when deploying BI solutions at scale is agility versus governance. Large-scale BI implementations with the right governance structure can take months to design and deploy. In this session, learn how you can avoid making this tradeoff using Amazon QuickSight. Learn how to easily deploy Amazon QuickSight to thousands of users using Active Directory and Federated SSO, while securely accessing your data sources in Amazon VPCs or on-premises. We also cover how to control access to your datasets, implement row-level security, create scheduled email reports, and audit access to your data.
Migrating your traditional Data Warehouse to a Modern Data LakeAmazon Web Services
In this session, we discuss the latest features of Amazon Redshift and Redshift Spectrum, and take a deep dive into its architecture and inner workings. We share many of the recent availability, performance, and management enhancements and how they improve your end user experience. You also hear from 21st Century Fox, who presents a case study of their fast migration from an on-premises data warehouse to Amazon Redshift. Learn how they are expanding their data warehouse to a data lake that encompasses multiple data sources and data formats. This architecture helps them tie together siloed business units and get actionable 360-degree insights across their consumer base.
Similar to How Amazon.com Uses AWS Analytics: Data Analytics Week SF (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.