講師: Bob Yin, Senior Product Specialist, Informatica
These Informatica Cloud offerings are pre-built packages for quick time-to-value for customers looking to fast-track cloud data management initiatives. For example, customers can quickly kick start a new Amazon Redshift data warehouse project and use Informatica Cloud Connector for Amazon Redshift to load it with meaningful connected data from cloud sources such as Salesforce.com or on-premises sources such as relational databases -- all within hours, not months.
講師: Xiaoyong Han, Solution Architect, AWS
Data collection and storage is a primary challenge for any big data architecture. In this webinar, gain a thorough understanding of AWS solutions for data collection and storage, and learn architectural best practices for applying those solutions to your projects. This session will also include a discussion of popular use cases and reference architectures. In this webinar, you will learn:
• Overview of the different types of data that customers are handling to drive high-scale workloads on AWS, and how to choose the best approach for your workload • Optimization techniques that improve performance and reduce the cost of data ingestion • Leveraging Amazon S3, Amazon DynamoDB, and Amazon Kinesis for storage and data collection
講師: Yian Han, Senior Business Development Manager, AWS
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and text-to-speech (TTS) with Amazon Polly, visual search and image recognition with Amazon Rekognition, and developer-focused machine learning with Amazon Machine Learning. In this talk you will learn about these services and see demos of their capabilities
講師: George Chiu 邱志威, Sr. Industry Consultant, Teradata
Learn how Netflix engages customers by leveraging Teradata as a critical component of its data and analytics platform to create a data-driven, customer-focused business.
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.
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/
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting, in its original format and extract value. In this session learn how to architect and implement an Analytics Data Lake. Hear customer examples of best practices and learn from their architectural blueprints.
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
講師: Xiaoyong Han, Solution Architect, AWS
Data collection and storage is a primary challenge for any big data architecture. In this webinar, gain a thorough understanding of AWS solutions for data collection and storage, and learn architectural best practices for applying those solutions to your projects. This session will also include a discussion of popular use cases and reference architectures. In this webinar, you will learn:
• Overview of the different types of data that customers are handling to drive high-scale workloads on AWS, and how to choose the best approach for your workload • Optimization techniques that improve performance and reduce the cost of data ingestion • Leveraging Amazon S3, Amazon DynamoDB, and Amazon Kinesis for storage and data collection
講師: Yian Han, Senior Business Development Manager, AWS
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and text-to-speech (TTS) with Amazon Polly, visual search and image recognition with Amazon Rekognition, and developer-focused machine learning with Amazon Machine Learning. In this talk you will learn about these services and see demos of their capabilities
講師: George Chiu 邱志威, Sr. Industry Consultant, Teradata
Learn how Netflix engages customers by leveraging Teradata as a critical component of its data and analytics platform to create a data-driven, customer-focused business.
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.
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/
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting, in its original format and extract value. In this session learn how to architect and implement an Analytics Data Lake. Hear customer examples of best practices and learn from their architectural blueprints.
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
Big Data and Analytics – End to End on AWS – Russell NashAmazon Web Services
In this session we will look at the common patterns for the ingest, storage, processing and analysis of different types of data on the AWS platform and illustrate how you can harness the power and scale of the cloud to drive innovation in your own business.
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.
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.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Webcast - Managing Big Data in the AWS Cloud_20140924Amazon Web Services
This presentation deck will cover specific services such as Amazon S3, Kinesis, Redshift, Elastic MapReduce, and DynamoDB, including their features and performance characteristics. It will also cover architectural designs for the optimal use of these services based on dimensions of your data source (structured or unstructured data, volume, item size and transfer rates) and application considerations - for latency, cost and durability. It will also share customer success stories and resources to help you get started.
Structured, Unstructured and Streaming Big Data on the AWSAmazon Web Services
Using AWS has never been easier or more affordable to solve business problems and uncover new opportunities using data. Now, businesses of all sizes and across all industries can take advantage of big data technologies and easily collect, store, process, analyze, and share their data. Gain a thorough understanding of what AWS offers across the big data lifecycle and learn architectural best practices for applying these technologies to your projects. We will also deep dive into how to use AWS services such as Kinesis, DynamoDB, Redshift, and Quicksight to optimize logging, build real-time applications, and analyze and visualize data at any scale.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
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.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
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.
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting (in its original format) and extract value. In this session, learn how to architect and implement a data lake in the AWS Cloud. Learn about best practices as we walk through architectural blueprints.
(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."
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.
Taking the Performance of your Data Warehouse to the Next Level with Amazon R...Amazon Web Services
Amazon Redshift gives you fast SQL query performance on large data sets. We will discuss optimisation from end to end, all the way from loading through to querying to ensure your end users get the data they need, when they need it.
Speaker: Russell Nash, Solutions Architect, Amazon Web Services
Featured Customer - Domain
The AWS Workshop Series Online is a series of live webinars designed for IT professionals who are looking to leverage the AWS Cloud to build and transform their business, are new to the AWS Cloud or looking to further expand their skills and expertise. In this series, we will cover : 'Modern Data Architectures for Business Insights at Scale'.
AWS APAC Webinar Week - Big Data on AWS. RedShift, EMR, & IOTAmazon Web Services
The world is producing an ever-increasing volume, velocity, and variety of data including data from devices. As we step into the era of Internet of things (IOT), for many consumers, batch analytics is no longer enough; they need sub-second analysis on fast-moving data. AWS delivers many technologies for solving big data and IOT problems. But what services should you use, why, when, and how? In this webinar where we simplify big data processing as a pipeline comprising various stages: ingest, store, process, analyze & visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, and durability. Finally, we provide a reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems.
(BDT306) Mission-Critical Stream Processing with Amazon EMR and Amazon Kinesi...Amazon Web Services
Organizations processing mission critical high-volume data must be able to achieve high levels of throughput and durability in data processing workflows. In this session, we will learn how DataXu is using Amazon Kinesis, Amazon S3, and Amazon EMR for its patented approach to programmatic marketing. Every second, the DataXu Marketing Cloud processes over 1 Million ad requests and makes more than 40 billion decisions to select and bid on ad impressions that are most likely to convert. In addition to addressing the scalability and availability of the platform, we will explore Amazon Kinesis producer and consumer applications that support high levels of scalability and durability in mission-critical record processing.
by Mamoon Chowdry, Solutions Architect
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.
講師: Jhen-Wei Huang, Solution Architect, AWS
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
Big Data and Analytics – End to End on AWS – Russell NashAmazon Web Services
In this session we will look at the common patterns for the ingest, storage, processing and analysis of different types of data on the AWS platform and illustrate how you can harness the power and scale of the cloud to drive innovation in your own business.
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.
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.
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Webcast - Managing Big Data in the AWS Cloud_20140924Amazon Web Services
This presentation deck will cover specific services such as Amazon S3, Kinesis, Redshift, Elastic MapReduce, and DynamoDB, including their features and performance characteristics. It will also cover architectural designs for the optimal use of these services based on dimensions of your data source (structured or unstructured data, volume, item size and transfer rates) and application considerations - for latency, cost and durability. It will also share customer success stories and resources to help you get started.
Structured, Unstructured and Streaming Big Data on the AWSAmazon Web Services
Using AWS has never been easier or more affordable to solve business problems and uncover new opportunities using data. Now, businesses of all sizes and across all industries can take advantage of big data technologies and easily collect, store, process, analyze, and share their data. Gain a thorough understanding of what AWS offers across the big data lifecycle and learn architectural best practices for applying these technologies to your projects. We will also deep dive into how to use AWS services such as Kinesis, DynamoDB, Redshift, and Quicksight to optimize logging, build real-time applications, and analyze and visualize data at any scale.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
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.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
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.
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting (in its original format) and extract value. In this session, learn how to architect and implement a data lake in the AWS Cloud. Learn about best practices as we walk through architectural blueprints.
(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."
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.
Taking the Performance of your Data Warehouse to the Next Level with Amazon R...Amazon Web Services
Amazon Redshift gives you fast SQL query performance on large data sets. We will discuss optimisation from end to end, all the way from loading through to querying to ensure your end users get the data they need, when they need it.
Speaker: Russell Nash, Solutions Architect, Amazon Web Services
Featured Customer - Domain
The AWS Workshop Series Online is a series of live webinars designed for IT professionals who are looking to leverage the AWS Cloud to build and transform their business, are new to the AWS Cloud or looking to further expand their skills and expertise. In this series, we will cover : 'Modern Data Architectures for Business Insights at Scale'.
AWS APAC Webinar Week - Big Data on AWS. RedShift, EMR, & IOTAmazon Web Services
The world is producing an ever-increasing volume, velocity, and variety of data including data from devices. As we step into the era of Internet of things (IOT), for many consumers, batch analytics is no longer enough; they need sub-second analysis on fast-moving data. AWS delivers many technologies for solving big data and IOT problems. But what services should you use, why, when, and how? In this webinar where we simplify big data processing as a pipeline comprising various stages: ingest, store, process, analyze & visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, and durability. Finally, we provide a reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems.
(BDT306) Mission-Critical Stream Processing with Amazon EMR and Amazon Kinesi...Amazon Web Services
Organizations processing mission critical high-volume data must be able to achieve high levels of throughput and durability in data processing workflows. In this session, we will learn how DataXu is using Amazon Kinesis, Amazon S3, and Amazon EMR for its patented approach to programmatic marketing. Every second, the DataXu Marketing Cloud processes over 1 Million ad requests and makes more than 40 billion decisions to select and bid on ad impressions that are most likely to convert. In addition to addressing the scalability and availability of the platform, we will explore Amazon Kinesis producer and consumer applications that support high levels of scalability and durability in mission-critical record processing.
by Mamoon Chowdry, Solutions Architect
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.
講師: Jhen-Wei Huang, Solution Architect, AWS
Artificial Intelligence (AI) and deep learning are now ready to power your business, as it is powering most of the innovation of Amazon.com with autonomous drones, and robots, Amazon Alexa, Amazon Go, and many other hard and important business problems. Come and learn why and how to get started with deep learning, and what you can expect from a future with better AI in the cloud and on the edge.
View these slides if you're you new to cloud computing and would like to learn more about Amazon Web Services (AWS), if you intend to implement a project and would like to discover the basics of the AWS cloud or if you are a business looking to evaluate cloud computing.
In the webinar based on these slides, we answered the following questions:
• What is Cloud Computing with AWS and what benefits can it deliver?
• Who is using AWS and what are they using it for?
• How can I use AWS Services to run my workloads?
View the webinar recording on YouTube here: http://youtu.be/QROD20r6-sQ
Driving Business Outcomes with a Modern Data Architecture - Level 100Amazon Web Services
Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organisation. Increasingly though, this data is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Speaker: Craig Stires, APAC Business Development - Big Data & Analytics, Amazon Web Services
Driving Business Insights with a Modern Data Architecture AWS Summit SG 2017Amazon Web Services
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realize all of those goals. Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organization. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyze, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
SendGrid Improves Email Delivery with Hybrid Data WarehousingAmazon Web Services
When you received your Uber ‘Tuesday Evening Ride Receipt’ or Spotify’s ‘This Week’s New Music’ email, did you think about how they got there?
SendGrid’s reliable email platform delivers each month over 20 Billion transactional and marketing emails on behalf of many of your favorite brands, including Uber, Airbnb, Spotify, Foursquare and NextDoor.
SendGrid was looking to evolve its data warehouse architecture in order to improve decision making and optimize customer experience. They needed a scalable and reliable architecture that would allow them to move nimbly and efficiently with a relatively small IT organization, while supporting the needs of both business and technical users at SendGrid.
SendGrid’s Director of Enterprise Data Operations will be joining architects from Amazon Web Services (AWS) and Informatica to discuss SendGrid’s journey to a hybrid cloud architecture and how a hybrid data warehousing solution is optimized to support SendGrid’s analytics initiative. Speakers will also review common technologies and use cases being deployed in hybrid cloud today, common data management challenges in hybrid cloud and best practices for addressing these challenges.
Join us to learn:
• How to evolve to a hybrid data warehouse with Amazon Redshift for scalability, agility and cost efficiency with minimal IT resources
• Hybrid cloud data management use cases
• Best practices for addressing hybrid cloud data management challenges
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.
AWS re:Invent 2016: Visualizing Big Data Insights with Amazon QuickSight (BDM...Amazon Web Services
Amazon QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuckSight’s in-memory calculation engine – you can go from data to insights, faster than ever.
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
Partnering with ISV's and AWS Marketplace – Your Competitive Advantage in the...Amazon Web Services
One of the key advantages of delivering solutions on the AWS cloud is that you can avoid procuring and delivering an infrastructure stack as part of your offer. This increasingly applies also to the Software you include in your architectures, as many vendors are now targeting cloud deployment models with innovative new licensing and delivery models, along with guidance for how you might integrate their packages into your solution. In this session, we’ll provide and overview of AWS technology partner ecosystem as well as the rapidly growing AWS Marketplace.
AWS re:Invent 2016: Building Big Data Applications with the AWS Big Data Plat...Amazon Web Services
Building big data applications often requires integrating a broad set of technologies to store, process, and analyze the increasing variety, velocity, and volume of data being collected by many organizations. In this session, we show how you can build entire big data applications using a core set of managed services including Amazon S3, Amazon Kinesis, Amazon EMR, Amazon Elasticsearch Service, Amazon Redshift, and Amazon QuickSight.
We walk you through the steps of building and securing a big data application using the AWS Big Data Platform. We also share best practices and common use cases for AWS big data services, including tips to help you choose the best services for your specific application.
Traditional BI promises security and scale, but at what cost? Often, working with data, finding answers and sharing them can be laborious and time intensive. The rapid growth and maturation of cloud technologies offers an easier path.
With Tableau and AWS you can move your BI to the cloud and deliver the security and scale of your traditional BI, but with accessibility, flexibility, and speed. Take a closer look at the benefits of cloud BI, and how you can get started today.
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
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 QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuickSight’s in-memory calculation engine – you can go from data to insights, faster than ever.
AWS offers everything you need to deploy a secure and flexible data lake in the cloud. Discover how services like Amazon Simple Storage Service (Amazon S3) and Amazon Redshift can be used together to build and manage your own data lake, and how AWS Lake Formation makes it possible to set up a data lake in days. We walk through an example architecture together, covering everything from data storage to data analytics.
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.
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.
3. Informatica and AWS
GE
Transportation
Customers want to:
Migrate existing on-
premises data warehouse
and application workloads
Extend their current data
management capabilities to
the cloud
Develop new data-driven
applications
4. Informatica Data Management for AWS
On premise
Data
Warehouse
Firewall
On-premise
Data Warehouse
Other Databases
Applications
Files
Salesforce Wave
Tableau Online
Data
Security
Enterprise
Information
Catalog
MDM
Data
Quality
CONNECTIVITY (200+ Connectors)
Data
Integration
Application
Integration
Informatica Data Management Platform
Amazon RDS
Amazon Redshift Amazon EMR Amazon S3
5. Leverage Informatica Solutions On The AWS
Informatica Cloud on AWS Marketplace
SaaS Contract
Enterprise Information Catalog (EIC)
Quick Start on AWS
Informatica Data Quality on AWS
Marketplace 1-Click Deployment
PowerCenter on AWS Marketplace 1-
Click Deployment
PowerCenter Quick Start on AWS
Big Data Management Quick Start on
AWS
Big Data Management on AWS
Marketplace
6. Data Powers AWS Success, Informatica Powers Data
Scale
Unlock Big Data
Insight with
Data Lakes
Integrate
Jumpstart & Scale
Cloud Data
Warehouse
Migrate
Migrate On-Premise
Workloads & Data
7. Migrate Workloads and Data to AWS
Cloud
On premise
Amazon Redshift
On-premise
Data Warehouse
Other Databases
Firewall
Amazon RDS
Amazon Aurora
Informatica
Informatica
8. Migrate On-Premise Data to AWS with Native Connectors
DynamoDB S3 Redshift
RDS +
Aurora
EMR
Extensive Connectivity to On-Premise Sources
9.
10. Hundreds of Connectors, For Any Data Source
Sales & ServiceBig Data
Human Resources
Web Protocols & API
ERP & Financials
B2B
Marketing
Social
IT & Admin
Analytics
11. Comprehensive Solution for Data Lakes On AWS
INGEST CLEANSEPREPARE SECURE GOVERNCATALOGACQUIRE CONSUME
COMPREHENSIVE SUPPORT FOR DATA PROCESSING
Spark Blaze Tez MapReduce
Catalog SearchLineage Recommendations
METADATA INTELLIGENCE
Spark Streaming
COMPREHENSIVE SUPPORT FOR DATA INFRASTRUCTURE
Data
Preparation
Business
Glossary
Batch
Processing
Stream
Processing
Data
Profiling
Data
ProtectionData
Lineage
Data
Parsing Enterprise Data
Catalog
Big Data
Relationships
Broadest
Connectivity
Data
Quality
Informatica Data Lake Management
Relational
Social
Files
Device data
Weblogs
Applications
Amazon
Redshift
Business
Intelligence
EMR S3
12. Intelligent Data Lake: Discover & Prepare Data
▪ Intuitively and interactively prepare
data with guided intelligence and
recommendations
▪ Records steps to automatically
generate data flows for repeatable &
reliable delivery of data
▪ Organize data assets in projects
workspaces to facilitate analytic
team collaboration
▪ Recommends alternate and
additional data assets using machine
learning
13. Enterprise Information Catalog: Data Visibility & Governance
▪ Google-like search, with Amazon-like
crowd-sourcing (recommendations, ratings)
▪ Easily find more sources of data and
discover relationships that matter
▪ Improve search and discovery of data by
relating business terms to technical assets
▪ Smarter data recommendations and
enhanced faceted search
▪ Traceability of data movement with full
lineage including, detailed impact
analysis
▪ Resource-level read/write permissions to
catalog objects for enhanced auditing Comprehensive Discovery and Visibility
of all Data Assets Beyond “Hadoop”
14. Security
Protect Data in
Rest and In
Motion
Operational
Confidence
Monitor &
Manage
Production Data
Data Visibility
Enterprise-Wide
Metadata
Connectivity
Any Data In
Cloud and On-
Prem
Data Management Best Practices for AWS Cloud Journey