Recommendation engines help your prospects and customers find the most relevant offers and content. In this presentation, you will learn how to use AWS building blocks to build your own location-aware recommendation engine. You’ll see how to store real-time events using Amazon Kinesis and Amazon DynamoDB. See how to easily move data into Amazon Redshift using Kinesis Firehose. As your site or app rises in popularity, you’ll need to track a wider variety of events and scale to handle traffic and usage spikes. Learn architectural patterns for processing large datasets and high-request volume applications.
In this session, Amit Patel, General Manager of AWS Mobile Services, will share our vision, customer trends and the latest additions to AWS Mobile Services.
Bringing Governance to an Existing Cloud at NASA’s Jet Propulsion Laboratory ...Amazon Web Services
Amazon Web Services provides JPL with a vast array of capabilities to store, process, and analyze mission data. JPLers were early to adopt AWS services to build complex solutions, but quickly grew to over 50 AWS accounts, 80 IAM users, and hundreds of resources. To deal with this complexity, a team of engineers inside JPL's Office of the CIO developed a cloud governance model. The true challenge was implementing it on existing deployments. Learn about their model and how they overcame the challenges.
Database migration doesn’t need to be difficult or time-consuming. Learn about the AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and cloud environments to Amazon RDS, Amazon Redshift, Amazon Aurora, Amazon DynamoDB, and Amazon EC2. We discuss homogeneous (same database engine) and heterogeneous migrations, as well as migrations from data warehouse platforms. We’ll also talk about the AWS Schema Conversion Tool, which saves you development time when migrating your Oracle, SQL Server, and data warehouse schemas and procedural code and exporting your data to the cloud. You'll hear from GumGum, an artificial intelligence company with deep expertise in computer vision that uses DMS to replicate its dimension data from different sources into a cohesive data warehouse.
PCI compliance is a steep enough challenge, but what happens when your entire infrastructure is in AWS? Do the same concepts of network segmentation and separation apply, and if so how? At what point do AWS compliance efforts intersect with your compliance efforts? This session will cover how Warren Rogers Associates is using the Palo Alto Networks VM-Series for AWS to maintain separation of data and traffic in AWS to improve security and achieve PCI compliance.
Warren Rogers Associates pioneered the development of Statistical Inventory Reconciliation Analysis (SIRA) and Continual Reconciliation for monitoring underground fuel tanks and associated lines. These methods are certified in accordance with EPA requirements and have been used by petroleum marketers for more than 25 years. Today, Warren Rogers specializes in statistical analysis and precision fuel system diagnostics for the retail petroleum industry and develops innovative ways to identify and combat fuel shrinkage and theft. Session sponsored by Palo Alto Networks.
Serverless architectures allow you to build and run applications and services without having to manage infrastructure. With serverless architectures, your application still runs on servers, but all the server management is done by AWS. In this session, you will learn how to build applications and services using a serverless architecture. We will discuss how you can use AWS Lambda to run code for any type of application or backend service; Amazon DynamoDB to store application data with high scalability and redundancy; and Amazon API Gateway to create and manage secure API endpoints. We will run through a demo setting up a web application using this architecture, and we will discuss best practices and patterns used by our customers to run serverless applications.
In this session, Amit Patel, General Manager of AWS Mobile Services, will share our vision, customer trends and the latest additions to AWS Mobile Services.
Bringing Governance to an Existing Cloud at NASA’s Jet Propulsion Laboratory ...Amazon Web Services
Amazon Web Services provides JPL with a vast array of capabilities to store, process, and analyze mission data. JPLers were early to adopt AWS services to build complex solutions, but quickly grew to over 50 AWS accounts, 80 IAM users, and hundreds of resources. To deal with this complexity, a team of engineers inside JPL's Office of the CIO developed a cloud governance model. The true challenge was implementing it on existing deployments. Learn about their model and how they overcame the challenges.
Database migration doesn’t need to be difficult or time-consuming. Learn about the AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and cloud environments to Amazon RDS, Amazon Redshift, Amazon Aurora, Amazon DynamoDB, and Amazon EC2. We discuss homogeneous (same database engine) and heterogeneous migrations, as well as migrations from data warehouse platforms. We’ll also talk about the AWS Schema Conversion Tool, which saves you development time when migrating your Oracle, SQL Server, and data warehouse schemas and procedural code and exporting your data to the cloud. You'll hear from GumGum, an artificial intelligence company with deep expertise in computer vision that uses DMS to replicate its dimension data from different sources into a cohesive data warehouse.
PCI compliance is a steep enough challenge, but what happens when your entire infrastructure is in AWS? Do the same concepts of network segmentation and separation apply, and if so how? At what point do AWS compliance efforts intersect with your compliance efforts? This session will cover how Warren Rogers Associates is using the Palo Alto Networks VM-Series for AWS to maintain separation of data and traffic in AWS to improve security and achieve PCI compliance.
Warren Rogers Associates pioneered the development of Statistical Inventory Reconciliation Analysis (SIRA) and Continual Reconciliation for monitoring underground fuel tanks and associated lines. These methods are certified in accordance with EPA requirements and have been used by petroleum marketers for more than 25 years. Today, Warren Rogers specializes in statistical analysis and precision fuel system diagnostics for the retail petroleum industry and develops innovative ways to identify and combat fuel shrinkage and theft. Session sponsored by Palo Alto Networks.
Serverless architectures allow you to build and run applications and services without having to manage infrastructure. With serverless architectures, your application still runs on servers, but all the server management is done by AWS. In this session, you will learn how to build applications and services using a serverless architecture. We will discuss how you can use AWS Lambda to run code for any type of application or backend service; Amazon DynamoDB to store application data with high scalability and redundancy; and Amazon API Gateway to create and manage secure API endpoints. We will run through a demo setting up a web application using this architecture, and we will discuss best practices and patterns used by our customers to run serverless applications.
Creating a Data Driven Culture with Amazon QuickSight - Technical 201Amazon Web Services
Data drives good business decisions and a data-driven culture can help organisations increase profitability and reduce costs.
Amazon QuickSight is a very fast, cloud-powered Business Intelligence (BI) service that makes it easy for all employees to build visualisations, perform ad-hoc analysis, and quickly get business insights from their data.
Speaker: David McAmis, Consultant, Amazon Web Services
AWS re:Invent 2016: Workshop: Building Your First Big Data Application with A...Amazon Web Services
Want to get ramped up on how to use Amazon's big data web services and launch your first big data application on AWS? Join us in this workshop as we build a big data application in real time using Amazon EMR, Amazon Redshift, Amazon Kinesis, Amazon DynamoDB, and Amazon S3. We review architecture design patterns for big data solutions on AWS, and give you access to a take-home lab so that you can rebuild and customize the application yourself.
AWS re:Invent 2016: Driving Innovation with Big Data and IoT (GPSST304)Amazon Web Services
With unforeseen competitive threats and potential market disruptions, enterprises are seeking to innovate for the benefit of their customers. Business transformation in the digital age requires the successful use of new technologies including the cloud, IoT, and Big Data. Attend this session to learn more about how AWS can help organizations innovate faster around IoT and Big Data. We dive into specific opportunities and approaches for managing billions of connected devices and associated big data workloads on the cloud.
BDA402 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
Learn how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we’ll review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
Amazon Kinesis Platform – The Complete Overview - Pop-up Loft TLV 2017Amazon Web Services
Real-Time Streaming Analytics became popular amongst many verticals and use cases. In AdTech, Gaming, Financial Service and IoT, AWS customers are leveraging Amazon Kinesis platform to ingest billions of events every day and process them in real-time. In this session, we will discuss Amazon Kinesis Streams, Amazon Kinesis Firehose and Amazon Kinesis Analytics. We will show best practice and design patterns in integrating Amazon Kinesis platform with other services like Amazon EMR, Redshift, Amazon Elasticsearch and AWS lambda as well as 3rd party connectors like storm, Spark and more.
Modern data architectures for real time analytics and engagementAmazon Web Services
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 Real-time Analytics and Engagement'.
"Increasing demands to collect, store, and analyze massive amounts of data often means that the same tools and approaches that worked in the past, don't work anymore. That's why many organizations are shifting to a data lake architecture. 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 tech talk, we introduce key concepts for a data lake and present aspects related to its implementation. We highlight the core components of a data lake, such as storage, compute, analytics, databases, stream processing, data management, and security. We discuss how to choose the right technologies for each component of the data lake, based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. We also provide a reference architecture and recommendations to get started with a data lake implementation on AWS.
Learning Objectives:
Understand key concepts and architectural components of a data lake architecture
Describe how and when to use a broad set of analytic and data management tools in a data lake architecture
Get insights on how to get started with a data lake implementation on AWS"
ENT302 Deep Dive on AWS Management Tools and New LaunchesAmazon Web Services
As companies shift workloads into the cloud, IT organizations are required to manage an increasing number of cloud resources. AWS provides a broad set of services that help IT organizations with provisioning, tracking, auditing, configuration management, and cost management of their AWS resources. In this session, we will explore the AWS Management Tools suite of services that support the lifecycle management of AWS resources at scale and enable IT governance and compliance. The Deep Dive on AWS Management Tools session will benefit both new and experienced IT administrators, systems administrators, and developers operating infrastructure on AWS and interested in learning about the AWS resource management capabilities.
Amazon Web Services ofrece un amplio conjunto de productos globales basados en la nube, incluidas aplicaciones de informática, almacenamiento, bases de datos, análisis, redes, móviles, herramientas para desarrolladores, herramientas de administración, IoT, seguridad y empresariales.
Introducing “Well-Architected” For Developers - Technical 101Amazon Web Services
With the multitude of different software development platforms, tools, and methodologies, it can be daunting to get started and ensure you are on the right architectural track in the cloud. AWS understands architectural best practices for designing reliable, secure, efficient, and cost-effective systems in the AWS cloud. This session will introduce you to the "Well-Architected" framework along with a number of key takeaways on setting solid architectural foundations.
Speaker: Ben Potter, Security Consultant, Amazon Web Services
Featured Customer - Reckon
The technical advantages of a microservices architecture pattern are understood by many AWS customers. In this session, the innovation advantages of microservices are explored from a business perspective together with business agility lessons learned during an evolution from a single monolithic application to cloud based microservices.
Speaker: Craig Dickson, Solutions Architect, Amazon Web Services
AWS re:Invent 2016 was AWS’ largest event yet with over 32,000 attendees, 400 breakout sessions, and two keynotes of new product announcements. In this talk, we’ll explore the core themes of AWS re:Invent 2016 such as serverless and artificial intelligence. We will also drill down into several of the services and features unveiled including AWS Batch, AWS Shield, Aurora for Postgres, X-Ray, Polly, Lex, Rekognition, AWS Step Functions. Light appetizers and refreshments will be provided.
AWS re:Invent 2016: How Fulfillment by Amazon (FBA) and Scopely Improved Resu...Amazon Web Services
We’ll share an overview of leveraging serverless architectures to support high performance data intensive applications. Fulfillment by Amazon (FBA) built the Seller Inventory Authority Platform (IAP) using Amazon DynamoDB Streams, AWS Lambda functions, Amazon Elasticsearch Service, and Amazon Redshift to improve results and reduce costs. Scopely will share how they used a flexible logging system built on Kinesis, Lambda, and Amazon Elasticsearch to provide high-fidelity reporting on hotkeys in Memcached and DynamoDB, and drastically reduce the incidence of hotkeys. Both of these customers are using managed services and serverless architecture to build scalable systems that can meet the projected business growth without a corresponding increase in operational costs.
Real-Time Bidding (RTB) is a service offered by advertising networks to agencies. The agencies decide on the value of advertising opportunities in real-time and bid accordingly on behalf of their advertising clients. Typically the window of opportunity for bids to be calculated from provided consumer details (e.g. cookies) and then submitted is 100ms.
Business Objectives that analytics can achieve is Resource allocation,
Customer segmentation, competitive benchmarking, customer facing.
Speaker: Shailender Mathur, SVP, Progressive
Creating a Data Driven Culture with Amazon QuickSight - Technical 201Amazon Web Services
Data drives good business decisions and a data-driven culture can help organisations increase profitability and reduce costs.
Amazon QuickSight is a very fast, cloud-powered Business Intelligence (BI) service that makes it easy for all employees to build visualisations, perform ad-hoc analysis, and quickly get business insights from their data.
Speaker: David McAmis, Consultant, Amazon Web Services
AWS re:Invent 2016: Workshop: Building Your First Big Data Application with A...Amazon Web Services
Want to get ramped up on how to use Amazon's big data web services and launch your first big data application on AWS? Join us in this workshop as we build a big data application in real time using Amazon EMR, Amazon Redshift, Amazon Kinesis, Amazon DynamoDB, and Amazon S3. We review architecture design patterns for big data solutions on AWS, and give you access to a take-home lab so that you can rebuild and customize the application yourself.
AWS re:Invent 2016: Driving Innovation with Big Data and IoT (GPSST304)Amazon Web Services
With unforeseen competitive threats and potential market disruptions, enterprises are seeking to innovate for the benefit of their customers. Business transformation in the digital age requires the successful use of new technologies including the cloud, IoT, and Big Data. Attend this session to learn more about how AWS can help organizations innovate faster around IoT and Big Data. We dive into specific opportunities and approaches for managing billions of connected devices and associated big data workloads on the cloud.
BDA402 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
Learn how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we’ll review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
Amazon Kinesis Platform – The Complete Overview - Pop-up Loft TLV 2017Amazon Web Services
Real-Time Streaming Analytics became popular amongst many verticals and use cases. In AdTech, Gaming, Financial Service and IoT, AWS customers are leveraging Amazon Kinesis platform to ingest billions of events every day and process them in real-time. In this session, we will discuss Amazon Kinesis Streams, Amazon Kinesis Firehose and Amazon Kinesis Analytics. We will show best practice and design patterns in integrating Amazon Kinesis platform with other services like Amazon EMR, Redshift, Amazon Elasticsearch and AWS lambda as well as 3rd party connectors like storm, Spark and more.
Modern data architectures for real time analytics and engagementAmazon Web Services
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 Real-time Analytics and Engagement'.
"Increasing demands to collect, store, and analyze massive amounts of data often means that the same tools and approaches that worked in the past, don't work anymore. That's why many organizations are shifting to a data lake architecture. 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 tech talk, we introduce key concepts for a data lake and present aspects related to its implementation. We highlight the core components of a data lake, such as storage, compute, analytics, databases, stream processing, data management, and security. We discuss how to choose the right technologies for each component of the data lake, based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. We also provide a reference architecture and recommendations to get started with a data lake implementation on AWS.
Learning Objectives:
Understand key concepts and architectural components of a data lake architecture
Describe how and when to use a broad set of analytic and data management tools in a data lake architecture
Get insights on how to get started with a data lake implementation on AWS"
ENT302 Deep Dive on AWS Management Tools and New LaunchesAmazon Web Services
As companies shift workloads into the cloud, IT organizations are required to manage an increasing number of cloud resources. AWS provides a broad set of services that help IT organizations with provisioning, tracking, auditing, configuration management, and cost management of their AWS resources. In this session, we will explore the AWS Management Tools suite of services that support the lifecycle management of AWS resources at scale and enable IT governance and compliance. The Deep Dive on AWS Management Tools session will benefit both new and experienced IT administrators, systems administrators, and developers operating infrastructure on AWS and interested in learning about the AWS resource management capabilities.
Amazon Web Services ofrece un amplio conjunto de productos globales basados en la nube, incluidas aplicaciones de informática, almacenamiento, bases de datos, análisis, redes, móviles, herramientas para desarrolladores, herramientas de administración, IoT, seguridad y empresariales.
Introducing “Well-Architected” For Developers - Technical 101Amazon Web Services
With the multitude of different software development platforms, tools, and methodologies, it can be daunting to get started and ensure you are on the right architectural track in the cloud. AWS understands architectural best practices for designing reliable, secure, efficient, and cost-effective systems in the AWS cloud. This session will introduce you to the "Well-Architected" framework along with a number of key takeaways on setting solid architectural foundations.
Speaker: Ben Potter, Security Consultant, Amazon Web Services
Featured Customer - Reckon
The technical advantages of a microservices architecture pattern are understood by many AWS customers. In this session, the innovation advantages of microservices are explored from a business perspective together with business agility lessons learned during an evolution from a single monolithic application to cloud based microservices.
Speaker: Craig Dickson, Solutions Architect, Amazon Web Services
AWS re:Invent 2016 was AWS’ largest event yet with over 32,000 attendees, 400 breakout sessions, and two keynotes of new product announcements. In this talk, we’ll explore the core themes of AWS re:Invent 2016 such as serverless and artificial intelligence. We will also drill down into several of the services and features unveiled including AWS Batch, AWS Shield, Aurora for Postgres, X-Ray, Polly, Lex, Rekognition, AWS Step Functions. Light appetizers and refreshments will be provided.
AWS re:Invent 2016: How Fulfillment by Amazon (FBA) and Scopely Improved Resu...Amazon Web Services
We’ll share an overview of leveraging serverless architectures to support high performance data intensive applications. Fulfillment by Amazon (FBA) built the Seller Inventory Authority Platform (IAP) using Amazon DynamoDB Streams, AWS Lambda functions, Amazon Elasticsearch Service, and Amazon Redshift to improve results and reduce costs. Scopely will share how they used a flexible logging system built on Kinesis, Lambda, and Amazon Elasticsearch to provide high-fidelity reporting on hotkeys in Memcached and DynamoDB, and drastically reduce the incidence of hotkeys. Both of these customers are using managed services and serverless architecture to build scalable systems that can meet the projected business growth without a corresponding increase in operational costs.
Real-Time Bidding (RTB) is a service offered by advertising networks to agencies. The agencies decide on the value of advertising opportunities in real-time and bid accordingly on behalf of their advertising clients. Typically the window of opportunity for bids to be calculated from provided consumer details (e.g. cookies) and then submitted is 100ms.
Business Objectives that analytics can achieve is Resource allocation,
Customer segmentation, competitive benchmarking, customer facing.
Speaker: Shailender Mathur, SVP, Progressive
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we will review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
(BDT306) How Hearst Publishing Manages Clickstream Analytics with AWSAmazon Web Services
Hearst Corporation monitors trending content on 250+ sites worldwide, providing metrics to editors and promoting cross-platform content sharing. To facilitate this, Hearst built a clickstream analytics platform on AWS that transmits and processes over 30 TB of data a day using AWS resources such as AWS Elastic Beanstalk, Amazon Kinesis, Spark on Amazon EMR, Amazon S3, Amazon Redshift, and Amazon Elasticsearch. In this session, learn how Hearst designed their clickstream analytics application and how you can use the same architecture to build your own and be ready to handle the changing world of clickstream data. Dive into how to do Spark streaming from an Amazon Kinesis stream, use timestamps to cleanse and validate data coming from diverse sources, and see how the system has evolved as data types have change from HTTP GET to RESTful JSON requests. Finally, see how Hearst's data scientists interact with and use cleansed data provided by the platform to perform ad hoc analyses, develop home-grown algorithms, and create visualizations and dashboards that support Hearst business stakeholders.
An AI Use Case: Market Event Impact Determination via Sentiment and Emotion A...Databricks
News plays an important role and might have significant impact in a variety of markets. The volumes and sources of news are growing rapidly. It’s a challenge for investors and business decision makers quickly and effectively select and analyze the relevant news from the vast amounts available to them.
In this session, a use case to automatically monitor news and perform nature language analysis will be introduced. The solution bases on Spark to collect, extract, aggregate and accumulate relevant news regarding to customer interested topics as long as their topics news are reported day by day, such as stock, oil, etc. Then it leverages IBM Waston nature language analysis technology to categorize the news information and further process the textual input to determine quantitative sentiment and emotion scores.
News sentiment analysis can add an additional dimension and timing to market data to determine potential for market impact. With the accumulated historical news sentiment data in kinds of distributed databases, the customer can use machine learning algorithms, such as scikit-learn, to analysis the data to optimize the decision making.
For example, we can get the sentiment analysis results for a long time from a variety of RSS feeds regarding oil, then mine the relationship between the news sentiment and oil price, and give the insights on the oil price trend. When the data is bigger day by day, our AI model will be stronger and more effective.
Deep.bi - Real-time, Deep Data Analytics Platform For EcommerceDeep.BI
Deep.bi It helps ecommerce teams improve their performance by providing current and detailed insights.
It bring operational excellence and performance for:
- Category Managers / Merchandisers
- Marketers
- Customer service
- UX / Design Team
- Tech / IT
- Executives / Managers
Amazon Kinesis provides services for you to work with streaming data on AWS. Learn how to load streaming data continuously and cost-effectively to Amazon S3 and Amazon Redshift using Amazon Kinesis Firehose without writing custom stream processing code. Get an introduction to building custom stream processing applications with Amazon Kinesis Streams for specialized needs.
WSO2Con USA 2017: Analytics Patterns for Your Digital EnterpriseWSO2
The WSO2 analytics platform provides a high performance, lean, enterprise-ready, streaming solution to solve data integration and analytics challenges faced by connected businesses. This platform offers real-time, interactive, machine learning and batch processing technologies that empower enterprises to build a digital business, by connecting various enterprise data sources to enhance your experience in understanding the data and to increase internal productivity.
This session explores how to enable digital transformation by building a data analytics platform. It will discuss the follwoing topics:
WSO2 Data Analytics Server architecture
Understanding streaming constructs
Architectural styles for data integration
Debugging and troubleshooting your integration
Deployment
Performance tuning
Production hardening
Learn best practices for building a real-time streaming data architecture on AWS with Spark Streaming, Amazon Kinesis, and Amazon Elastic MapReduce (EMR). Get a closer look at how to ingest streaming data scalably and durably from data producers like mobile devices, servers, and even web browsers, and design a stream processing application with minimal data duplication and exactly-once processing.
Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services
Customer Guest: Harry Koch, Solutions Architecture, Philips
Path to the future #4 - Ingestão, processamento e análise de dados em tempo realAmazon Web Services LATAM
Nesta sessão faremos uma demonstração de controle e defesa de tráfego aéreo utilizando processamento em tempo real.
Trataremos das boas práticas para ingestão, armazenamento, processamento e visualização de dados através de serviços da AWS como Kinesis, DynamoDB, Lambda, Redshift, Quicksight e Amazon Machine Learning.
Fast Cycle, Multi-Terabyte Data Analysis with Amazon Redshift and ClearStory ...ClearStory Data
Organizations storing large volumes of data in Amazon Redshift rely on faster cycle analysis to quickly uncover actionable insights. Their challenge when data volumes grow in Redshift is finding an analysis solution that removes the headaches of tedious ETL, data wrangling and allows scalable, visual data analysis. These slides shared during the webinar demonstrates ClearStory Data’s solution for scalable, fast-cycle, visual data analysis, that is used by CPG, Retail, Consumer Internet companies on Redshift.
To watch the on-demand webinar, visit:
Darin Briskman, Amazon Web Services delivers a keynote at the Canadian Executive Cloud & DevOps Summit in Toronto on June 9, 2017 on the topic of Artificial Intelligence.
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Presented by: Matthew McClean, AWS Partner Solutions Architect, Amazon Web Services
Financial Services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, you'll hear how IHS Markit has used Machine Learning on AWS to help global banking institutions manage their commodities portfolios. You will also learn how the Amazon Machine Learning Service can take the hassle out of AI.
AWS Summit 2013 | Singapore - Big Data Analytics, Presented by AWS, Intel and...Amazon Web Services
Learn more about the tools, techniques and technologies for working productively with data at any scale. This session will introduce the family of data analytics tools on AWS which you can use to collect, compute and collaborate around data, from gigabytes to petabytes. We'll discuss Amazon Elastic MapReduce, Hadoop, structured and unstructured data, and the EC2 instance types which enable high performance analytics.
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce you to SPICE - a Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Similar to Building a Real-Time Geospatial-Aware Recommendation Engine (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
4. Location Awareness
• A ”location aware” app tailors it’s content to be relevant for a given
location.
• Location is derived from the geo-coordinates in the mobile device or
web browser.
• Location data is used to filter search results to those that have
proximity to the location:
• Local shops, merchants, or attractions
• News and events
• Location data also used to tag photos or social media posts so that
you can search for content by location:
• ”Show me the photos I took at dinner in San Francisco”
5. Recommendation Engine
• A recommendation engine supplies content to the app that it thinks
the user will like.
• Recommendations can be generated algorithmically:
• Recommendations can be generated by a predictive model:
if (time == 12:00PM) {
displayLocalRestaurants();
}
{gender: “m”, age: “18-25”, budget: “$10-20”,
restaurant: “Taco Time”}
{predictedClass: “3_star”, probability:
“0.478”}
7. App Requirements – Search and Location
• Users should be able to search for local businesses on
their mobile device. Required search terms:
• Name
• Address
• Description
• Keyword (tacos, beer, flowers, etc.)
8. App Requirements – Search and Location
• Search results should be filtered by location and
displayed on a map.
• Only show results on the rectangular map with geo-
coordinates between:
(x,y) top right
(x,y) bottom left
9. App Requirements - Recommendations
• Recommendations should be based on a predictive model.
• User registration form will collect minimal information:
• Email Address
• Gender
• Age Group (under 18, 18-25, over 65, etc.)
• Postal Code
• Recommendations will be offers from local merchants.
• Must be able to track user response to offers:
• Click-through
• Conversions
14. ER vs Document Models
ER Model:
• Normalized
• Transactional
• Search requires indexing all attributes
• Adding new attributes requires schema change
Document Model:
• Non-Normalized
• Attributes can be added without schema change
• Entire document can be indexed for search
18. Why Transactional Data Storage?
• High throughput
• Read, Write, Update intensive
• Thousands or Millions of Concurrent interactions
• Availability, Speed, Recoverability
19. Amazon DynamoDB
• Managed NoSQL database service
• Supports both document and key-value data models
• Highly scalable – no table size or throughput limits
• Consistent, single-digit millisecond latency at any scale
• Highly available—3x replication
• Simple and powerful API
20. DynamoDB Streams
Stream of updates to a table
Asynchronous
Exactly once
Strictly ordered
• Per item
Highly durable
• Scale with table
24-hour lifetime
Sub-second latency
23. Amazon Kinesis
Managed Service for streaming data ingestion, and processing
Amazon Web Services
AZ AZ AZ
Durable, highly consistent storage replicates data
across three data centers (availability zones)
Aggregate and
archive to S3
Millions of
sources producing
100s of terabytes
per hour
Front
End
Authentication
Authorization
Ordered stream
of events supports
multiple readers
Real-time
dashboards
and alarms
Machine learning
algorithms or
sliding window
analytics
Aggregate analysis
in Hadoop or a
data warehouse
Inexpensive: $0.028 per million puts
24. Kinesis Firehose
Makes stream processing even easier!
• Automatically delivers data to S3 and Redshift
• No Kinesis-Client-Library or Lambda functions required
• Handles all scaling of the Kinesis stream’s shards
• Near Real-time
• Data loaded into S3 or Redshift within 60 seconds of hitting
the stream
• Fully Managed
• No operational overhead of managing streams, shards, or
KCL applications
27. Machine Learning – Key Concepts
Segmentation:
• Divide the population into subgroups that have different values
for the target variable
No Yes Yes No No Yes
28. Machine Learning – Key Concepts
No YesYes No NoYes
No Yes Yes No No Yes
Pure Pure Mixed
29. Machine Learning – Key Concepts
Linear Classification:
• Linear function is used to segregate the dataset
30. Amazon Machine Learning
Easy to use, managed machine learning service built
for developers
Robust, powerful machine learning technology based
on Amazon’s internal systems
Create models using your data already stored in the
AWS cloud
Deploy models to production in seconds
31. Powerful Machine Learning Technology
Based on Amazon’s battle-hardened internal systems
Not just the algorithms:
• Smart data transformations
• Input data and model quality alerts
• Built-in industry best practices
Grows with your needs
• Train on up to 100 GB of data
• Generate billions of predictions
• Obtain predictions in batches or real-time
32. Machine Learning Workflow
Build & Train
model
Evaluate and
optimize
Retrieve
predictions
1 2 3
- Create a Datasource object pointing to your data
- Explore and understand your data
- Transform data and train your model
33. Add Predictions to Existing Data Flow
Your application
Amazon
DynamoDB
+
Trigger event with Lambda
+
Query for predictions with
Amazon ML real-time API