The AWS Big Data services are inherently built to run at @scale. In this session, you will learn how to develop an enterprise scale big data application using AWS services such as Amazon EMR, Amazon Redshift & Redshift Spectrum, Amazon Athena, Amazon Elasticsearch Service, Amazon Kinesis, Amazon QuickSight and AWS Glue. This session will also cover different architectural patterns and customer use cases.
AWS-powered services for analytics can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches that will allow you to transform your data into a valuable corporate asset. In this session, AWS will provide an overview of the different AWS services available for your data analytics needs. You can combine these blocks to build data flows that will extend your organization’s agility, ability to derive more insights and value from its data, and capability to adopt more sophisticated analytics tools and processes as your needs evolve. In the second part of the session, Paddy Power Betfair’s Data team will discuss the adoption and large scale operation of a broad range of AWS services that make up PPB’s scalable, mixed workload, multi-brand data platform. The data capabilities developed by PPB and powered by AWS were implemented to enable low-latency, high-volume and near real-time advanced analytics use cases, in the highly regulated and fast-paced betting industry. This was only possible through a focus on automation, innovation and continuous improvement.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This session 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.
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.
Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...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.
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 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.
https://aws.amazon.com/webinars/anz-webinar-series/
AWS-powered services for analytics can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches that will allow you to transform your data into a valuable corporate asset. In this session, AWS will provide an overview of the different AWS services available for your data analytics needs. You can combine these blocks to build data flows that will extend your organization’s agility, ability to derive more insights and value from its data, and capability to adopt more sophisticated analytics tools and processes as your needs evolve. In the second part of the session, Paddy Power Betfair’s Data team will discuss the adoption and large scale operation of a broad range of AWS services that make up PPB’s scalable, mixed workload, multi-brand data platform. The data capabilities developed by PPB and powered by AWS were implemented to enable low-latency, high-volume and near real-time advanced analytics use cases, in the highly regulated and fast-paced betting industry. This was only possible through a focus on automation, innovation and continuous improvement.
Modern data is massive, quickly evolving, unstructured, and increasingly hard to catalog and understand from multiple consumers and applications. This session 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.
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.
Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...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.
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 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.
https://aws.amazon.com/webinars/anz-webinar-series/
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
Speakers:
Roger Dahlstrom - Solutions Architect, AWS
Bobby Malik - Sr. Technical Account Manager, AWS
Data Lake allows an organisation to store all of their data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. In this session we will explore the architecture of a Data Lake on AWS and cover topics such as storage, processing and security.
Speakers:
Tom McMeekin, Associate Solutions Architect, Amazon Web Services
Building with Purpose - Built Databases: Match Your Workloads to the Right Da...Amazon Web Services
In this session, Darin Briskman dives deep into what databases to use for which components of your application. Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, and more. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.
Darin Briskman, Chief Evangelist, Database, Analytics, & Machine Learning, Amazon Web Services
Data Analytics Week at the San Francisco Loft
Preparing Data for the Lake
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
Speakers:
John Mallory - Principal Business Development Manager Storage (Object), AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Amazon big success using big data analyticsKovid Academy
Today, Big Data is everywhere, but the key problem is – it is too big to tackle and, too complex to evaluate and draw insights from. Also, Big Data Analytics relatively being a state-of-the-art concept, there is a lack of copious knowledge and expertise in the field of Big Data, which is often leading most organizations to misuse their data.
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
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.
Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...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.
Come along and learn about the enhancements that we have made to Amazon Redshift, including features around performance, scalability and cluster management. We will also explore why a cloud native Data Warehouse solution allows AWS to innovate faster and deliver customer outcomes that are not possible in a more traditional on premises solution.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. In this session, we will introduce how to use S3 as a Data Lake to collect device information via AWS IoT, and then generate prediction for your application.
講師: Ivan Cheng, Solution Architect, AWS
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
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
(ISM213) Building and Deploying a Modern Big Data Architecture on AWSAmazon Web Services
"The AWS platform enables large enterprises to use data to solve business problems and uncover opportunities more easily and affordably than ever before. However, to truly take advantage of AWS, enterprises need a way to collect, store, process, analyze, and continually execute on their data.
Datapipe has been an AWS partner for more than five years. In that time, it has developed a proprietary process for the deployment of AWS environments, as well as the processing and evaluation of big data analytics to optimize these environments over time. This flexible solution includes automation tools, continuous monitoring, and cloud analytics. It protects against architectural sprawl and continually redesigns for scalability. This kind of continuous build environment allows Datapipe to examine the AWS environment as a complete picture and ensure the cloud environment is running as efficiently and effectively as possible, ultimately reducing overhead costs for the enterprise.
In this session, Jason Woodlee, Senior Director of Cloud Products at Datapipe, will discuss the technical details of designing and deploying a modern big data architecture on AWS, including application purpose and design, development environment and language overview, DevOps automation best practices, and continuous build and test frameworks. Session sponsored by Datapipe."
Introduction to Amazon Kinesis Firehose - AWS August Webinar SeriesAmazon Web Services
Streaming data applications can deliver compelling, near real-time user experiences, but building the back-end infrastructure to collect and process streaming data is difficult. Amazon Kinesis Firehose makes it easy for you to load streaming data into AWS without having to build custom stream processing applications. In this webinar, we will introduce Amazon Kinesis Firehose and discuss how to ingest streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service using Amazon Kinesis Firehose. We will also highlight key use cases based on real-world examples from IoT, AdTech, E-Commerce, and Gaming. Join us to: - Get an introduction to streaming data and an overview of Amazon Kinesis Firehose - Learn about common streaming data use cases from IoT, Ad Tech, E-Commerce, and Gaming - Understand how to use Amazon Kinesis Firehose to load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service Who should attend: Developers, data analysts, data engineers, architects
Using data lakes to quench your analytics fire - AWS Summit Cape Town 2018Amazon Web Services
Speaker: Shafreen Sayyed, AWS
Level: 200
Traditional data storage and analytic tools no longer provide the agility and flexibility required to deliver relevant business insights. We are seeing more and more organisations shift to a data lake solution. This approach allows you to store massive amounts of data in a central location so its readily available to be categorized, processed, analyzed, and consumed by diverse organizational groups. In this session, we’ll assemble a data lake using services such as Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, AWS Glue and integration with Amazon Redshift Spectrum.
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
Speakers:
Roger Dahlstrom - Solutions Architect, AWS
Bobby Malik - Sr. Technical Account Manager, AWS
Data Lake allows an organisation to store all of their data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. In this session we will explore the architecture of a Data Lake on AWS and cover topics such as storage, processing and security.
Speakers:
Tom McMeekin, Associate Solutions Architect, Amazon Web Services
Building with Purpose - Built Databases: Match Your Workloads to the Right Da...Amazon Web Services
In this session, Darin Briskman dives deep into what databases to use for which components of your application. Learn how to evaluate a new workload for the best managed database option based on specific application needs related to data shape, data size at limit, computational requirements, programmability, throughput and latency needs, and more. This session explains the ideal use cases for relational and non-relational database services, including Amazon Aurora, Amazon DynamoDB, Amazon ElastiCache for Redis, Amazon Neptune, and Amazon Redshift.
Darin Briskman, Chief Evangelist, Database, Analytics, & Machine Learning, Amazon Web Services
Data Analytics Week at the San Francisco Loft
Preparing Data for the Lake
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
Speakers:
John Mallory - Principal Business Development Manager Storage (Object), AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Amazon big success using big data analyticsKovid Academy
Today, Big Data is everywhere, but the key problem is – it is too big to tackle and, too complex to evaluate and draw insights from. Also, Big Data Analytics relatively being a state-of-the-art concept, there is a lack of copious knowledge and expertise in the field of Big Data, which is often leading most organizations to misuse their data.
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
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.
Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...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.
Come along and learn about the enhancements that we have made to Amazon Redshift, including features around performance, scalability and cluster management. We will also explore why a cloud native Data Warehouse solution allows AWS to innovate faster and deliver customer outcomes that are not possible in a more traditional on premises solution.
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. In this session, we will introduce how to use S3 as a Data Lake to collect device information via AWS IoT, and then generate prediction for your application.
講師: Ivan Cheng, Solution Architect, AWS
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
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
(ISM213) Building and Deploying a Modern Big Data Architecture on AWSAmazon Web Services
"The AWS platform enables large enterprises to use data to solve business problems and uncover opportunities more easily and affordably than ever before. However, to truly take advantage of AWS, enterprises need a way to collect, store, process, analyze, and continually execute on their data.
Datapipe has been an AWS partner for more than five years. In that time, it has developed a proprietary process for the deployment of AWS environments, as well as the processing and evaluation of big data analytics to optimize these environments over time. This flexible solution includes automation tools, continuous monitoring, and cloud analytics. It protects against architectural sprawl and continually redesigns for scalability. This kind of continuous build environment allows Datapipe to examine the AWS environment as a complete picture and ensure the cloud environment is running as efficiently and effectively as possible, ultimately reducing overhead costs for the enterprise.
In this session, Jason Woodlee, Senior Director of Cloud Products at Datapipe, will discuss the technical details of designing and deploying a modern big data architecture on AWS, including application purpose and design, development environment and language overview, DevOps automation best practices, and continuous build and test frameworks. Session sponsored by Datapipe."
Introduction to Amazon Kinesis Firehose - AWS August Webinar SeriesAmazon Web Services
Streaming data applications can deliver compelling, near real-time user experiences, but building the back-end infrastructure to collect and process streaming data is difficult. Amazon Kinesis Firehose makes it easy for you to load streaming data into AWS without having to build custom stream processing applications. In this webinar, we will introduce Amazon Kinesis Firehose and discuss how to ingest streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service using Amazon Kinesis Firehose. We will also highlight key use cases based on real-world examples from IoT, AdTech, E-Commerce, and Gaming. Join us to: - Get an introduction to streaming data and an overview of Amazon Kinesis Firehose - Learn about common streaming data use cases from IoT, Ad Tech, E-Commerce, and Gaming - Understand how to use Amazon Kinesis Firehose to load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service Who should attend: Developers, data analysts, data engineers, architects
Using data lakes to quench your analytics fire - AWS Summit Cape Town 2018Amazon Web Services
Speaker: Shafreen Sayyed, AWS
Level: 200
Traditional data storage and analytic tools no longer provide the agility and flexibility required to deliver relevant business insights. We are seeing more and more organisations shift to a data lake solution. This approach allows you to store massive amounts of data in a central location so its readily available to be categorized, processed, analyzed, and consumed by diverse organizational groups. In this session, we’ll assemble a data lake using services such as Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, AWS Glue and integration with Amazon Redshift Spectrum.
Data Lake Implementation: Processing and Querying Data in Place (STG204-R1) -...Amazon Web Services
Flexibility is key when building and scaling a data lake. The analytics solutions you use in the future will almost certainly be different from the ones you use today, and choosing the right storage architecture gives you the agility to quickly experiment and migrate with the latest analytics solutions. In this session, we explore best practices for building a data lake in Amazon S3 and Amazon Glacier for leveraging an entire array of AWS, open source, and third-party analytics tools. We explore use cases for traditional analytics tools, including Amazon EMR and AWS Glue, as well as query-in-place tools like Amazon Athena, Amazon Redshift Spectrum, Amazon S3 Select, and Amazon Glacier Select.
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
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.
Analyze your Data Lake, Fast @ Any Scale - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
-Learn how to automatically discover, catalog, and prepare your data for analytics
-Understand how to query data in your data lake without having to transform or load the data into your data warehouse
-See how to analyze data in both your data lake and data warehouse
What's New with Amazon Redshift ft. McDonald's (ANT350-R1) - AWS re:Invent 2018Amazon Web Services
Learn about the latest and hottest features of Amazon Redshift. We’ll deep dive into the architecture and inner workings of Amazon Redshift and discuss how the recent availability, performance, and manageability improvements we’ve made can significantly enhance your user experience. We’ll also share glimpse of what we are working on and our plans for the future. McDonald's will join us to share how they leverage a data lake powered by Redshift, Redshift spectrum and Athena to get quick insights.
Connecting the dots - How Amazon Neptune and Graph Databases can transform yo...Amazon Web Services
Amazon Neptune is the fully-managed graph database service that makes it easy to build and run applications for highly connected datasets. Come learn how to transform your business with Amazon Neptune and hear diverse use cases such as recommendation engines, knowledge graphs, fraud detection, social networks, network management and life sciences. Learn about using Amazon Neptune with Apache TinkerPop Gremlin traversals and RDF/SPARQL query processing and watch live how we derive valuable business insights, customer satisfaction by region, in a simple query.
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...Amazon Web Services
Whether you are part of a startup or a global enterprise, using a data lake to store and analyze data can help your business glean insights to evolve service offerings and capitalize on emerging market opportunities. In this workshop, AWS engineers and experts provide hands-on guidance for IT professionals looking to build a data lake for their organization. We provide overviews of Amazon S3, Amazon Glacier, and AWS query-in-place features and services, such as Amazon S3 Select, Amazon Glacier Select, Amazon Athena, and Amazon Redshift Spectrum. Attendees also learn how to use these services with third-party tools to build data lakes and other analytics solutions. Familiarity of with AWS object storage and analytics services is helpful but not required.
Leadership Session: AWS Database and Analytics (DAT206-L) - AWS re:Invent 2018Amazon Web Services
We’re witnessing an unprecedented growth in the amount of data collected and stored in the cloud. Getting insights from this data requires database and analytics services that scale and perform in ways not possible before. AWS offers the broadest set of database and analytics services to process, store, manage, and analyze all your data. In this session, we provide an overview of the database and analytics services at AWS, new services and features we launched this year, how customers are using these services, and our vision for continued innovation in this space.
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
by Avijit Goswami, Sr Solutions Architect 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.
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Your data has value for multiple business functions in your organization. Shorten your time to analytics and take faster, better decisions based on data.
In this session you will learn how you can access your data from a myriad of tools such as multiple EMR clusters, Athena & Redshift.
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.
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.
26. Official US Statistics
Collection and
dissemination:
mostly the same
since World War II
Multi-Agency
effort
Surveys are
dominant data
source
Administrative
records support
surveys
11
27. Users want more, faster, current…
27
Users
want
more:
• Timely and
detailed
estimates
• Statistics that
link with other
data
• Microdata
• Relevant data
28. Big Data Benefits for Census
28
Enhance current surveys
Reduce respondent burden
Improve timeliness of
release
Better information for
unique situations
Granularity enhanced
Optimize Data Quality
Process
29. Problem Statement
Today, the process surrounding data access for the Census’s MathStats and Data Scientists are manual,
cumbersome, and slow. Whether to gain access to data or to link the data across datasets (e.g., AdRecs, multi-survey
data, and multi-period data) for longitudinal or other studies, the Census’s data stewardship policies must be
respected. The resulting data may inherit controls from the source data (e.g., Title 13, Title 26, and more), and manual
efforts are currently required to track the data lineage from source to resulting data. Additionally, multiple IT
environments are installed to handle each project’s survey instance.
29
• Linking data across
surveys is difficult
• Sharing data is a manual
exercise
• Data is copied multiple
times
• Honoring data
stewardship policies
requires distributed
manual efforts
Decentralized Data
Management Limitations
• Controls must be
duplicated for every survey
system
• Governance and security
measures are cumbersome
• Auditing and monitoring
capabilities are
inconsistent
Security Control Limitations
• Data processing code is
inconsistently managed from
one group to the next
• Reproducing results from base
data is not feasible since data
lineage is not consistently
tracked
Processing Approach
Limitations
• Current approach
requires constant
acquisition of new
servers
• Technology is
inconsistent from one
group or survey to the
next
• Handling large datasets
with complex
calculations is
challenging
Technology Limitations
DEMOECON
S1 S
2
S
3
S
4
…
.M
1
M
2
M
3
…
.
Y
1
Y
2
Y
n
…
.
…
.
Survey Portfolio
Time
Period
Census Data Limitation
S
n
S
n
Sn
+1
01
0
3
04
0
2
31. Enterprise Data Lake (EDL) Solution Supports the Mission
31
Security as a Service
Analytics as a Service
Enterprise Data Lake
Data as a Service
Content
Repositories
Infrastructure & Operations as a Service
1
Data/Code
Repository
LEGEND
Cloud
Standardized Cloud
Services
Standardized EDL
Services
Component of EDL
Ecosystem Specific to the EDL
Computational
Environment
Data Ingestion Services
Transactional Systems /
Data Sources
The proposed EDL solutions will support the business process by storing and analyzing any data with associated code at anytime throughout
the lifecycle.
data
encryption key
permissions
monitoring
32. Proposed Enterprise Data Lake in the Cloud
32
The data lake will streamline time consuming tasks and simplify complex
processes to make the Business and IT users’ lives easier. MathStats and
Data Scientists will be able to focus on their data, models, and products rather
than on administrative tasks.
Security
Governance
Infrastructure
Management
Data
Management
Analytics
Security
Governance
Infrastructure
Management
Data
Management
Analytics
Security
Governance
Infrastructure
Management
Data
Management
Analytics
Survey N
DEMOGRAPHICS
DECENNIAL
OTHER PROGRAMS
Survey N + 1 Survey N + 1…
ECON
Enterprise
Directorate
Analytic
s
Directorate
Analytic
s
Directorate
Analytic
s
EDL Standard Services
Standardized Cloud Services
Standardized Census Data Services
Governance
Security