The document discusses streaming data and Amazon Kinesis. It describes how streaming data is processed continuously in real-time compared to batch processing. It then provides an overview of the Amazon Kinesis portfolio including Kinesis Data Streams for building custom streaming applications, Kinesis Data Analytics for analyzing streaming data with SQL, and Kinesis Data Firehose for loading streaming data. Examples are given for processing web analytics, IoT sensor data, and CloudTrail logs in real-time.
by Jon Handler, Principal Solutions Architect and Sanjay Dhar, Solutions Architect, AWS
Nearly everything in IT - servers, applications, websites, connected devices, and other things - generate discrete, time-stamped records of events called logs. Processing and analyzing these logs to gain actionable insights is log analytics. We'll look at how to use centralized log analytics across multiple sources with Amazon Elasticsearch Service.
by Darin Briskman, Database, Analytics, and Machine Learning AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Darin Briskman, Technical Evangelist, AWS
We'll take a look at the fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. We'll show how you can use Amazon QuickSight to easily connect to your data, perform advanced analysis, and create stunning visualizations and rich dashboards that can be accessed from any browser or mobile device.
Building Data Lakes That Cost Less and Deliver Results Faster - AWS Online Te...Amazon Web Services
Learning Objectives:
- Get an inside look at Amazon S3 Select and how it helps to accelerate application performance
- Learn about how Amazon Glacier Select helps you extend your data lake to archival storage
- Understand how different applications can leverage these features
by Darin Briskman, Technical Evangelist, AWS
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
by Avijit Goswami, Sr. Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
How to Build a Data Lake in Amazon S3 & Amazon Glacier - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Understand the options for building an analytics platform that leverages Amazon S3 & Amazon Glacier
- Learn about the key considerations for ETL and other core analytics functions
- Determine if query-in-place capabilities like Amazon S3 Select, Amazon Glacier Select, Amazon Athena, and Amazon Redshift Spectrum are a good fit for your use case
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.
by Jon Handler, Principal Solutions Architect and Sanjay Dhar, Solutions Architect, AWS
Nearly everything in IT - servers, applications, websites, connected devices, and other things - generate discrete, time-stamped records of events called logs. Processing and analyzing these logs to gain actionable insights is log analytics. We'll look at how to use centralized log analytics across multiple sources with Amazon Elasticsearch Service.
by Darin Briskman, Database, Analytics, and Machine Learning AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
by Darin Briskman, Technical Evangelist, AWS
We'll take a look at the fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. We'll show how you can use Amazon QuickSight to easily connect to your data, perform advanced analysis, and create stunning visualizations and rich dashboards that can be accessed from any browser or mobile device.
Building Data Lakes That Cost Less and Deliver Results Faster - AWS Online Te...Amazon Web Services
Learning Objectives:
- Get an inside look at Amazon S3 Select and how it helps to accelerate application performance
- Learn about how Amazon Glacier Select helps you extend your data lake to archival storage
- Understand how different applications can leverage these features
by Darin Briskman, Technical Evangelist, AWS
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
by Avijit Goswami, Sr. Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
How to Build a Data Lake in Amazon S3 & Amazon Glacier - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Understand the options for building an analytics platform that leverages Amazon S3 & Amazon Glacier
- Learn about the key considerations for ETL and other core analytics functions
- Determine if query-in-place capabilities like Amazon S3 Select, Amazon Glacier Select, Amazon Athena, and Amazon Redshift Spectrum are a good fit for your use case
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.
by Lin Chunyong and Ryan Deivert, Airbnb
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.
Build Your First Big Data Application on AWS (ANT213-R1) - AWS re:Invent 2018Amazon Web Services
Do you want to increase your knowledge of AWS big data web services and launch your first big data application on the cloud? In this session, we walk you through simplifying big data processing as a data bus comprising ingest, store, process, and visualize. You will build a big data application using AWS managed services, including Amazon Athena, Amazon Kinesis, Amazon DynamoDB, and Amazon S3. Along the way, we review architecture design patterns for big data applications and give you access to a take-home lab so you can rebuild and customize the application yourself. To get the most from this session, bring your own laptop and have some familiarity with AWS services.
by Bill Baldwin, Global Enterprise Support Lead, 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 Andre Hass, Specialist Technical Account Manager, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Build Data Engineering Platforms with Amazon EMR (ANT204) - AWS re:Invent 2018Amazon Web Services
Amazon EMR provides a flexible range of service customization options, enabling customers to use it as a building block for their data platforms. In this session, AWS customers Salesforce.com and Vanguard discuss in detail how they use Amazon EMR to build a self-service, secure, and auditable data engineering platform. Customers who want to optimize their design and configurations should attend this session to learn best practices from customer experts. Topics include achieving cost-efficient scale, using notebooks, processing streaming data, rapid prototyping of applications and data pipelines, architecting for both transient and persistent clusters, setting up advanced security and authorization controls, and enabling easy self service for users.
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
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.
by Zehra Syeda-Sarwat, Program Manager Strategist, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
Amazon Athena: What's New and How SendGrid Innovates (ANT324) - AWS re:Invent...Amazon Web Services
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In this session, we live demo exciting new capabilities the team have been heads down building. SendGrid, a leader in trusted email delivery, discusses how they used Athena to reinvent a popular feature of their platform.
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.
ABD318_Architecting a data lake with Amazon S3, Amazon Kinesis, AWS Glue and ...Amazon Web Services
"Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users - from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways.
In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users."
Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
by Androski Spicer, 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.
Bridge OLTP and Stream Processing with Amazon Kinesis, AWS Lambda, & MongoDB ...Amazon Web Services
Seamlessly bridge your application’s real-time operational surfaces—which face customers, devices, and latency-sensitive components—to its data processing inner surfaces—which enable BI, machine learning, and other massive analytical workloads. Learn how to compose Amazon Kinesis, Amazon Redshift, Amazon S3, AWS Lambda, and MongoDB into a flexible, secure, and performant architectural core. Then learn through concrete examples how that core extends in order to handle a wide range of applications, from rich financial accounting platforms to cloud-based, open IoT operating systems. This session is brought to you by AWS partner, MongoDB.
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.
As the volume and types of data continues to grow, customers often have valuable data that is not easily discoverable and available for analytics. A common challenge for data engineering teams is architecting a data lake that can cater to the needs of diverse users - from developers to business analysts to data scientists. In this session, dive deep into building a data lake using Amazon S3, Amazon Kinesis, Amazon Athena and AWS Glue. Learn how AWS Glue crawlers can automatically discover your data, extracting and cataloguing relevant metadata to reduce operations in preparing your data for downstream consumers.
Build Your Own Log Analytics Solutions on AWS (ANT323-R) - AWS re:Invent 2018Amazon Web Services
With Amazon Elasticsearch Service's simplicity comes a multitude of opportunity to use it as a back end for real-time application and infrastructure monitoring. With this wealth of opportunities comes sprawl - developers in your organization are deploying Amazon Elasticsearch Service for many different workloads and many different purposes. Should you centralize into one Amazon Elasticsearch Service domain? What are the tradeoffs in scale and cost? How do you control access to the data and dashboards? How do you structure your indexes - single tenant or multi-tenant? In this session, we'll explore whether, when, and how to centralize logging across your organization to minimize cost and maximize value and learn how Autodesk has built a unified log analytics solution using Amazon Elasticsearch Service.
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Amazon Web Services
Most companies are overrun with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we discuss the most common use cases with Amazon Redshift, and we take an in-depth look at how modern data warehousing blends and analyzes all your data to give you deeper insights to run your business. Equinox Fitness Clubs joins us to share their journey from static reports, redundant data, and inefficient data intergration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
Level: Intermediate
Speaker: Bill Baldwin - Database Technical Evangelist, AWS
ABD301-Analyzing Streaming Data in Real Time with Amazon KinesisAmazon Web Services
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we present an end-to-end streaming data solution using Kinesis Streams for data ingestion, Kinesis Analytics for real-time processing, and Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system.
by Lin Chunyong and Ryan Deivert, Airbnb
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.
Build Your First Big Data Application on AWS (ANT213-R1) - AWS re:Invent 2018Amazon Web Services
Do you want to increase your knowledge of AWS big data web services and launch your first big data application on the cloud? In this session, we walk you through simplifying big data processing as a data bus comprising ingest, store, process, and visualize. You will build a big data application using AWS managed services, including Amazon Athena, Amazon Kinesis, Amazon DynamoDB, and Amazon S3. Along the way, we review architecture design patterns for big data applications and give you access to a take-home lab so you can rebuild and customize the application yourself. To get the most from this session, bring your own laptop and have some familiarity with AWS services.
by Bill Baldwin, Global Enterprise Support Lead, 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 Andre Hass, Specialist Technical Account Manager, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Build Data Engineering Platforms with Amazon EMR (ANT204) - AWS re:Invent 2018Amazon Web Services
Amazon EMR provides a flexible range of service customization options, enabling customers to use it as a building block for their data platforms. In this session, AWS customers Salesforce.com and Vanguard discuss in detail how they use Amazon EMR to build a self-service, secure, and auditable data engineering platform. Customers who want to optimize their design and configurations should attend this session to learn best practices from customer experts. Topics include achieving cost-efficient scale, using notebooks, processing streaming data, rapid prototyping of applications and data pipelines, architecting for both transient and persistent clusters, setting up advanced security and authorization controls, and enabling easy self service for users.
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
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.
by Zehra Syeda-Sarwat, Program Manager Strategist, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
Amazon Athena: What's New and How SendGrid Innovates (ANT324) - AWS re:Invent...Amazon Web Services
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In this session, we live demo exciting new capabilities the team have been heads down building. SendGrid, a leader in trusted email delivery, discusses how they used Athena to reinvent a popular feature of their platform.
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.
ABD318_Architecting a data lake with Amazon S3, Amazon Kinesis, AWS Glue and ...Amazon Web Services
"Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users - from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways.
In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users."
Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
by Androski Spicer, 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.
Bridge OLTP and Stream Processing with Amazon Kinesis, AWS Lambda, & MongoDB ...Amazon Web Services
Seamlessly bridge your application’s real-time operational surfaces—which face customers, devices, and latency-sensitive components—to its data processing inner surfaces—which enable BI, machine learning, and other massive analytical workloads. Learn how to compose Amazon Kinesis, Amazon Redshift, Amazon S3, AWS Lambda, and MongoDB into a flexible, secure, and performant architectural core. Then learn through concrete examples how that core extends in order to handle a wide range of applications, from rich financial accounting platforms to cloud-based, open IoT operating systems. This session is brought to you by AWS partner, MongoDB.
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.
As the volume and types of data continues to grow, customers often have valuable data that is not easily discoverable and available for analytics. A common challenge for data engineering teams is architecting a data lake that can cater to the needs of diverse users - from developers to business analysts to data scientists. In this session, dive deep into building a data lake using Amazon S3, Amazon Kinesis, Amazon Athena and AWS Glue. Learn how AWS Glue crawlers can automatically discover your data, extracting and cataloguing relevant metadata to reduce operations in preparing your data for downstream consumers.
Build Your Own Log Analytics Solutions on AWS (ANT323-R) - AWS re:Invent 2018Amazon Web Services
With Amazon Elasticsearch Service's simplicity comes a multitude of opportunity to use it as a back end for real-time application and infrastructure monitoring. With this wealth of opportunities comes sprawl - developers in your organization are deploying Amazon Elasticsearch Service for many different workloads and many different purposes. Should you centralize into one Amazon Elasticsearch Service domain? What are the tradeoffs in scale and cost? How do you control access to the data and dashboards? How do you structure your indexes - single tenant or multi-tenant? In this session, we'll explore whether, when, and how to centralize logging across your organization to minimize cost and maximize value and learn how Autodesk has built a unified log analytics solution using Amazon Elasticsearch Service.
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Amazon Web Services
Most companies are overrun with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we discuss the most common use cases with Amazon Redshift, and we take an in-depth look at how modern data warehousing blends and analyzes all your data to give you deeper insights to run your business. Equinox Fitness Clubs joins us to share their journey from static reports, redundant data, and inefficient data intergration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Amazon Kinesis Data Analytics gives us to tools to run SQL queries against active data streams. We'll look at how we can performance real-time log analytics and q build entire streaming applications using SQL, so that you can gain actionable insights promptly.
Level: Intermediate
Speaker: Bill Baldwin - Database Technical Evangelist, AWS
ABD301-Analyzing Streaming Data in Real Time with Amazon KinesisAmazon Web Services
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we present an end-to-end streaming data solution using Kinesis Streams for data ingestion, Kinesis Analytics for real-time processing, and Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system.
Real Time Data Ingestion & Analysis - AWS Summit Sydney 2018Amazon Web Services
Real Time Data Ingestion and Analysis
In this session you will learn how to perform real time data analytics on streaming data using Amazon Kinesis Streams and run prediction algorithms. Learn how to stream your Cloud Trail logs to Amazon Kinesis Streams and identify anomalies using Spark Stream analytics and Amazon Kinesis Data Analytics.
Ganesh Raja, Big Data Solutions Architect, Amazon Web Services
BDA307 Analyzing Data Streams in Real Time with Amazon KinesisAmazon Web Services
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we first present an end-to-end streaming data solution using Kinesis Data Streams for data ingestion, Kinesis Data Analytics for real-time processing, and Kinesis Data Firehose for persistence. Then, Zynga talks about how they analyze real-time game events that are triggered by player actions, and shares best practices for processing streaming data at scale.
Real-Time Web Analytics with Amazon Kinesis Data Analytics (ADT401) - AWS re:...Amazon Web Services
Knowing what users are doing on your websites in real time provides insights you can act on without waiting for delayed batch processing of clickstream data. Watching the immediate impact on user behavior after new releases, detecting and responding to anomalies, situational awareness, and evaluating trends are all benefits of real-time website analytics. In this workshop, we build a cost-optimized platform to capture web beacon traffic, analyze it for interesting metrics, and display it on a customized dashboard. We start by deploying the Web Analytics Solution Accelerator, then once the core is complete, we extend their solution to capture new and interesting metrics, process those with Amazon Kinesis Analytics, and display new graphs on their custom dashboard. Participants come away with a fully functional system for capturing, analyzing, and displaying valuable website metrics in real time.
Considerations for Building Your First Streaming Application (ANT359) - AWS r...Amazon Web Services
Do you want to increase your knowledge of AWS big data web services and launch your first big data application on the cloud? In this chalk talk, we provide an overview of many of the AWS analytics services, including Amazon EMR, Amazon Kinesis, Amazon Athena, and Amazon Redshift. We discuss how they are architected together to solve common big data problems, such as ingestion, ETL, and real-time analytics.
Running Your SQL Server Database on Amazon RDS (DAT329) - AWS re:Invent 2018Amazon Web Services
In this session, learn how to run SQL Server on Amazon RDS, best practices for migration from on-premises or Amazon EC2 into Amazon RDS, and the pros and cons of running in Amazon RDS for SQL Server today. We also discuss workarounds for customer needs that are not supported today with Amazon RDS.
Architecting for Real-Time Insights with Amazon Kinesis (ANT310) - AWS re:Inv...Amazon Web Services
Amazon Kinesis makes it easy to speed up the time it takes for you to get valuable, real-time insights from your streaming data. In this session, we walk through the most popular applications that customers implement using Amazon Kinesis, including streaming extract-transform-load, continuous metric generation, and responsive analytics. Our customer Autodesk joins us to describe how they created real-time metrics generation and analytics using Amazon Kinesis and Amazon Elasticsearch Service. They walk us through their architecture and the best practices they learned in building and deploying their real-time analytics solution.
Serverless Stream Processing Pipeline Best Practices (SRV316-R1) - AWS re:Inv...Amazon Web Services
Real-time analytics has traditionally been analyzed using batch processing in DWH/Hadoop environments. Common use cases use data lakes, data science, and machine learning (ML). Creating serverless data-driven architecture and serverless streaming solutions with services like Amazon Kinesis, AWS Lambda, and Amazon Athena can solve real-time ingestion, storage, and analytics challenges, and help you focus on application logic without managing infrastructure. In this session, we introduce design patterns, best practices, and share customer journeys from batch to real-time insights in building modern serverless data-driven architecture applications. Hear how Intel built the Intel Pharma Analytics Platform using a serverless architecture. This AI cloud-based offering enables remote monitoring of patients using an array of sensors, wearable devices, and ML algorithms to objectively quantify the impact of interventions and power clinical studies in various therapeutics conditions.
Analyzing Streaming Data in Real-time with Amazon KinesisAmazon Web Services
As more and more organizations strive to gain real-time insights into their business, streaming data has become ubiquitous. Typical streaming data analytics solutions require specific skills and complex infrastructure. However, with Amazon Kinesis Analytics, you can analyze streaming data in real-time with standard SQL—there is no need to learn new programming languages or processing frameworks.
In this session, we dive deep into the capabilities of Amazon Kinesis Analytics using real-world examples. We’ll present an end-to-end streaming data solution using Amazon Kinesis Streams for data ingestion, Amazon Kinesis Analytics for real-time processing, and Amazon Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Amazon Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system.
Keeping the Pace with Data Ingestion (GPSCT402) - AWS re:Invent 2018Amazon Web Services
Data is eating the world. Don't let it consume your organization! In this talk, we take a deep dive into the various methods of ingesting the ever-increasing amount of data that your organization is generating, and we discuss how to effectively leverage that data as part of your data analytics pipeline. We cover topics such as batch and real-time ingestion with AWS services and open source tools. We also dive into some reference architecture implementations of common ingestion patterns. Finally, we discuss how you can make this data useful through catalogs and governance.
Serverless on AWS: Architectural Patterns and Best PracticesVladimir Simek
When speaking about serverless on AWS, most people think about AWS Lambda. But there's more than than. AWS provides a set of fully managed services that you can use to build and run serverless applications. Serverless applications don’t require provisioning, maintaining, and administering servers for backend components such as compute, databases, storage, stream processing, message queuing, and more. You also no longer need to worry about ensuring application fault tolerance and availability. Instead, AWS handles all of these capabilities for you. This allows you to focus on product innovation while enjoying faster time-to-market.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. In this session, we’ll present an end-to-end streaming data solution including data ingestion, real-time processing, and persistence.
Analyzing Data Streams in Real Time with Amazon Kinesis: PNNL's Serverless Da...Amazon Web Services
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we first present an end-to-end streaming data solution using Amazon Kinesis Data Streams for data ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose for persistence. We review in detail how to write SQL queries for operational monitoring using Kinesis Data Analytics.
Learn how PNNL is building their ingestion flow into their Serverless Data Lake leveraging the Kinesis Platform. At times migrating existing NiFi Processes where applicable to various parts of the Kinesis Platform, replacing complex flows on Nifi to bundle and compress the data with Kinesis Firehose, leveraging Kinesis Streams for their enrichment and transformation pipelines, and using Kinesis Analytics to Filter, Aggregate, and detect anomalies.
SRV316 Serverless Data Processing at Scale: An Amazon.com Case StudyAmazon Web Services
Come to this session, and learn how Amazon takes advantage of AWS Lambda with Amazon Kinesis, Amazon Kinesis Data Firehose, and Amazon Kinesis Data Analytics to run a highly scalable, high-throughput pipeline to support its data processing needs. We cover different example architectures that handle such use cases as in-line process and data manipulation. We also discuss the advantages of using the AWS platform to manage different streams for data processing.
Get Started with Real-Time Streaming Data in Under 5 Minutes - AWS Online Tec...Amazon Web Services
Learning Objectives:
- Identify common problems that streaming data can help solve
- Understand the AWS services that are used to solve these problems, including Amazon Kinesis
- Try out one of 5+ different solutions powered by Amazon Kinesis through AWS CloudFormation templates
Don’t Wait Until Tomorrow: From Batch to Streaming (ANT360) - AWS re:Invent 2018Amazon Web Services
In recent years, there has been explosive growth in the number of connected devices and real-time data sources. Data is being produced continuously and its production rate is accelerating. Businesses can no longer wait for hours or days to use this data. To gain the most valuable insights, they must use this data immediately so they can react quickly to new information. In this chalk talk, we discuss how to take advantage of streaming data sources to analyze and react in near-real time. In addition, we present different options for how to solve a real-world scenario and walk through those solutions.
Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018Amazon Web Services
Amazon Kinesis Streams was born of necessity, to enable Amazon Web Services to capture millions of individual metering records per second from AWS services and EC2 instances and aggregate these records by account identifier to produce a customer billing record in real time. Today, Kinesis is a foundational service which over a dozen AWS and Amazon retail services use to capture and process streaming data.
Since the launch of Kinesis Streams, Amazon has worked with customers as their stream processing needs have evolved, subsequently launching Kinesis Firehose—which enables customers to capture streaming data, perform in-line processing to clean, format, and deliver this data to service destinations, such as S3, Redshift, Elasticsearch, and Lambda, in minutes—and Kinesis Analytics—which enables customers to process streaming data in real time using ANSI SQL..
Today, Kinesis data streaming services are foundational for business critical workflows, providing customers with a new way to process big data and extract actionable insights. In this session, we offer an overview of Kinesis, Amazon’s data streaming platform—highlighting key architectural features—and explains how customers have architected their applications using Kinesis services for low-latency and extreme scale. We will also demonstrate how to build an end-to-end solution using the latest and greatest AWS Data and analytics services like Athena, Glue, Lambda and more.
In this workshop you'll explore approaches for processing data using serverless architectures. You'll build processing infrastructure to enable operations personnel in Wild Rydes headquarters to monitor the health of the unicorn fleet. Each unicorn is equipped with a sensor that reports its location and vitals and you'll explore approaches for processing this data in batches and real-time.
To build this infrastructure, you will use AWS Lambda, Amazon S3, Amazon Kinesis, Amazon DynamoDB, and Amazon Athena. You'll create functions in Lambda to process files and streams, use DynamoDB to persist unicorn vitals, create a serverless application to aggregate these data points using Kinesis Analytics, archive the raw data using Kinesis Firehose and Amazon S3, and you'll use Amazon Athena to run ad-hoc queries against the raw data.
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. Lots of customer examples
1 billion events/wk from
connected devices | IoT
17 PB of game data per
season | Entertainment
80 billion ad
impressions/day, 30 ms
response time | Ad Tech
100 GB/day click streams
from 250+ sites |
Enterprise
50 billion ad
impressions/day sub-50
ms responses | Ad Tech
10 million events/day
| Retail
Amazon Kinesis as Databus -
Migrate from Kafka to Kinesis| Enterprise
Funnel all
production events
through Amazon
Kinesis