Learn how to use Amazon ElastiCache with AWS IoT and AWS Lambda to create serverless solutions that let you rapidly make use of large and multisource data sets.
This is an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
Dive deep into some of the key innovations behind Amazon Aurora, discuss best practices and configurations, and share early customer experience from the field.
Deep Dive on Elastic File System - February 2017 AWS Online Tech TalksAmazon Web Services
Organizations face significant challenges moving their applications to the cloud when they require a standard file system interface for accessing their cloud data. In this technical session, we will explore the world’s first cloud-scale file system and its targeted use cases. Attendees will learn about the Amazon Elastic File System (EFS) features and benefits, how to identify applications that are appropriate for use with Amazon EFS, and details about its performance and security models. We will highlight and demonstrate how to deploy Amazon EFS in one of our most common use cases and will share tips for success throughout.
Learning Objectives:
• Recognize why and when to use Amazon EFS
• Understand key technical/security concepts
• Learn how to leverage EFS’s performance
• See a demo of EFS in action
• Review EFS’s economics
Database Migration – Simple, Cross-Engine and Cross-Platform MigrationAmazon Web Services
Learn about the new AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases.
Data-driven companies have a need to make their data easily accessible to those who analyze it. Many organizations have adopted the Looker application, LookML on AWS, a centralized analytical database with a user-friendly interface that allows employees to ask and answer their own questions to make informed business decisions.
Join our webinar to learn how our customer, Casper, an online mattress retailer, made the switch from a transactional database to Looker’s data analytics program on Amazon Redshift. Looker on Amazon Redshift can help you greatly reduce your analytics lifecycle with a simplified infrastructure and rapid cloud scaling.
Join us to learn:
• How to utilize LookML to build reusable definitions and logic for your data
• Best practices for architecting a centralized analytical database
• How Casper leveraged Looker and Amazon Redshift to provide all their employees access to their data and metrics
Who should attend: Heads of Analytics, Heads of BI, Analytics Managers, BI Teams, Senior Analysts
An overview of the Amazon ElastiCache managed service, with examples of how it can be used to increase performance, lower costs and augment other database services and databases to make things faster, easier and less expensive.
Learn the fundamentals of Amazon DynamoDB and see the DynamoDB console first-hand as we walk through a demo of building a serverless web application using this high-performance key-value and JSON document store.
This is an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
Dive deep into some of the key innovations behind Amazon Aurora, discuss best practices and configurations, and share early customer experience from the field.
Deep Dive on Elastic File System - February 2017 AWS Online Tech TalksAmazon Web Services
Organizations face significant challenges moving their applications to the cloud when they require a standard file system interface for accessing their cloud data. In this technical session, we will explore the world’s first cloud-scale file system and its targeted use cases. Attendees will learn about the Amazon Elastic File System (EFS) features and benefits, how to identify applications that are appropriate for use with Amazon EFS, and details about its performance and security models. We will highlight and demonstrate how to deploy Amazon EFS in one of our most common use cases and will share tips for success throughout.
Learning Objectives:
• Recognize why and when to use Amazon EFS
• Understand key technical/security concepts
• Learn how to leverage EFS’s performance
• See a demo of EFS in action
• Review EFS’s economics
Database Migration – Simple, Cross-Engine and Cross-Platform MigrationAmazon Web Services
Learn about the new AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases.
Data-driven companies have a need to make their data easily accessible to those who analyze it. Many organizations have adopted the Looker application, LookML on AWS, a centralized analytical database with a user-friendly interface that allows employees to ask and answer their own questions to make informed business decisions.
Join our webinar to learn how our customer, Casper, an online mattress retailer, made the switch from a transactional database to Looker’s data analytics program on Amazon Redshift. Looker on Amazon Redshift can help you greatly reduce your analytics lifecycle with a simplified infrastructure and rapid cloud scaling.
Join us to learn:
• How to utilize LookML to build reusable definitions and logic for your data
• Best practices for architecting a centralized analytical database
• How Casper leveraged Looker and Amazon Redshift to provide all their employees access to their data and metrics
Who should attend: Heads of Analytics, Heads of BI, Analytics Managers, BI Teams, Senior Analysts
An overview of the Amazon ElastiCache managed service, with examples of how it can be used to increase performance, lower costs and augment other database services and databases to make things faster, easier and less expensive.
Learn the fundamentals of Amazon DynamoDB and see the DynamoDB console first-hand as we walk through a demo of building a serverless web application using this high-performance key-value and JSON document store.
Managing Data with Amazon ElastiCache for Redis - August 2016 Monthly Webinar...Amazon Web Services
Many data sets, such as time-series collections or Internet of Things (IoT) deployments can include huge numbers of sensor reports and other data points, which can be a challenge to manage and aggregate. Amazon ElastiCache for Redis provides an on-demand managed service with the performance and scalability to turn big data into useful information. Join us to learn how to use Amazon ElastiCache to create serverless solutions that lets you rapidly make use of large and multisource data sets.
Learning Objectives:
• Learn how to ingest and analyze sensor data using Amazon ElastiCache for Redis and the AWS IoT Service
• Learn how to use ElastiCache Redis for Time-Series data
AWS re:Invent 2016: Big Data Architectural Patterns and Best Practices on AWS...Amazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
Making (Almost) Any Database Faster and Cheaper with CachingAmazon Web Services
Learn how to make your AWS databases up to 10x faster and up to 90% less expensive with Amazon ElastiCache for Redis. We’ll look at how to determine whether caching will benefit your database environment and show how to easily test and implement a high speed solution.
Amazon Web Services (AWS) offers a wide range of database options to fit your application requirements. From database services that are fully managed and that can be launched in minutes with just a few clicks to self-managed databases running on EC2. AWS managed database services include Amazon Relational Database Service (Amazon RDS), with support for six commonly used database engines, Amazon Aurora, a MySQL and PostgreSQL-compatible relational database, Amazon DynamoDB, a NoSQL database service or Amazon Redshift, a petabyte-scale data warehouse service. AWS also provides the AWS Database Migration Service, a service which makes it easy and inexpensive to migrate your databases to AWS cloud.
In this webinar, we take a closer look at the AWS database offerings and learn how to quickly select, set up, operate, and scale your database in the cloud.
Learning Objectives:
• Gain insights into the AWS database offering and know which to select for your workload.
• Learn how the AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS) can facilitate and simplify migrating your business critical applications to Amazon Web Services.
• Learn how Amazon DynamoDB Accelerator (DAX) can reduce Amazon DynamoDB response times from milliseconds to microseconds, even at millions of requests per second.
• Hear from our partners like Version1 and Clckwrk who can help you in your journey towards Database freedom.
AWS re:Invent 2016: Event Handling at Scale: Designing an Auditable Ingestion...Amazon Web Services
How does McGraw-Hill Education use the AWS platform to scale and reliably receive 10,000 learning events per second? How do we provide near-real-time reporting and event-driven analytics for hundreds of thousands of concurrent learners in a reliable, secure, and auditable manner that is cost effective? MHE designed and implemented a robust solution that integrates AWS API Gateway, AWS Lambda, Amazon Kinesis, Amazon S3, Amazon Elasticsearch Service, Amazon DynamoDB, HDFS, Amazon EMR, Amazopn EC2, and other technologies to deliver this cloud-native platform across the US and soon the world. This session describes the challenges we faced, architecture considerations, how we gained confidence for a successful production roll-out, and the behind-the-scenes lessons we learned.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
Deep Dive on MySQL Databases on AWS - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about MySQL deployment options on AWS
- Learn how to maintain high availability and security of your data
- Learn how to migrate MySQL databases to Amazon RDS
Amazon Web Services provides startups with the low cost, easy to use infrastructure needed to scale and grow any size business. Attend this session and learn how to migrate your startup to AWS and make the most out of the platform.
It’s been an exciting year for Amazon Aurora, the database with MySQL-compatible and PostgreSQL-compatible database engines. Amazon Aurora combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this deep dive session, we’ll discuss best practices and explore new features, including high availability options, new integrations with AWS services, and the performance management with Amazon RDS Performance Insights.
AWS re:Invent 2016: [REPEAT] How EA Leveraged Amazon Redshift and AWS Partner...Amazon Web Services
In November 2015, Capital Games launched a mobile game accompanying a major feature film release. The back end of the game is hosted in AWS and uses big data services like Amazon Kinesis, Amazon EC2, Amazon S3, Amazon Redshift, and AWS Data Pipeline. Capital Games will describe some of their challenges on their initial setup and usage of Amazon Redshift and Amazon EMR. They will then go over their engagement with AWS Partner 47lining and talk about specific best practices regarding solution architecture, data transformation pipelines, and system maintenance using AWS big data services. Attendees of this session should expect a candid view of the process to implementing a big data solution. From problem statement identification to visualizing data, with an in-depth look at the technical challenges and hurdles along the way.
AWS re:Invent 2016: JustGiving: Serverless Data Pipelines, Event-Driven ETL, ...Amazon Web Services
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), application programming interfaces (API), clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. Building scalable big data pipelines with automated extract-transform-load (ETL) and machine learning processes can address these limitations. JustGiving is the world’s largest social platform for online giving. In this session, we describe how we created several scalable and loosely coupled event-driven ETL and ML pipelines as part of our in-house data science platform called RAVEN. You learn how to leverage AWS Lambda, Amazon S3, Amazon EMR, Amazon Kinesis, and other services to build serverless, event-driven, data and stream processing pipelines in your organization. We review common design patterns, lessons learned, and best practices, with a focus on serverless big data architectures with AWS Lambda.
AWS re:Invent 2016: FINRA in the Cloud: the Big Data Enterprise (ENT313)Amazon Web Services
Large-scale enterprise migration can be a complex undertaking, especially for organizations that re-architect solutions to leverage the benefits of the Cloud. FINRA, which regulates US equities and options markets, recently completed a 2.5-year migration and re-architecture of its Big Data platform. Their platform consumes billions of market events every day. FINRA has developed scalable platforms and services on AWS that enable migrating enterprise applications and business functions to the Cloud quickly. Their data management platform takes advantage of AWS storage and compute products. In this session, IT influencers and decision makers will learn lessons from FINRA’s migration, including how to create an enterprise-class Cloud architecture and which technology skills are required for transitioning to the Cloud. We also share examples of the business value FINRA has realized.
Building Big Data Applications with Serverless Architectures - June 2017 AWS...Amazon Web Services
Learning Objectives:
- Use cases and best practices for serverless big data applications
- Leverage AWS technologies such as AWS Lambda and Amazon Kinesis
- Learn to perform ETL, event processing, ad-hoc analysis, real-time processing, and MapReduce with serverless
Building data processing applications is challenging and time-consuming, and often requires specialized expertise to deploy and operate. With serverless computing, you can perform real-time stream processing of multiple data types without needing to spin up servers or install software, allowing you to deploy big data applications quickly and more easily. Come learn how you can use AWS Lambda with Amazon Kinesis to analyze streaming data in real-time and then store the results in a managed NoSQL database such as Amazon DynamoDB. You’ll learn tips and tricks for doing in-line processing, data manipulation, and even distributed MapReduce on large data sets.
What’s New in Amazon RDS for Open-Source and Commercial DatabasesAmazon Web Services
In the past year, Amazon RDS has continued to expand functionality, scalability, availability and ease of use for all supported database engines: PostgreSQL, MySQL, MariaDB, Oracle and Microsoft SQL Server. We’ll take a close look at RDS use cases and new capabilities, splitting the time between open-source and commercial database engines.
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories.
AWS re:Invent 2016: Operations Automation and Infrastructure Management with ...Amazon Web Services
At Capital One, we are using Docker and container technologies to advance microservices adoption, increase efficiencies of cloud resources, and decouple the application layer from the underlying infrastructure. Capital One is a federated organization with a “you build it, you own it” culture that provides autonomy and speed for delivery teams. Each federated team runs and operates their container management stack. In order for the federated teams to accelerate their cloud and container-based apps adoption, we created self-service automation tools for creation and operations management of container management stack.
In this session, we explore our push-button automation tool that includes capabilities such as the creation and management of Amazon ECS clusters, an Application Load Balancer for dynamic and context-based routing and provides a user interface via a Jenkins Job or a AWS Lambda function. Our tooling also includes a home-grown dynamic service discovery and routing for applications requiring two-way mutual SSL authentication. We talk through how Capital One regularly updates AMIs with the latest patches and software versions using an automated solution that leverages AWS Lambda to rehydrate the Amazon ECS compute cluster with the latest AMI without causing any downtime. We also discuss how we created a sophisticated canary deployment automation using AWS Lambda and application services, where users can specify how to migrate to a new version of containerized apps and manage the deployment.
AWS empowers enterprise Docker deployment with Amazon ECS and an ecosystem of cloud services and serverless architectures, making containerization in mission-critical environments easier than ever.
Managing Data with Amazon ElastiCache for Redis - August 2016 Monthly Webinar...Amazon Web Services
Many data sets, such as time-series collections or Internet of Things (IoT) deployments can include huge numbers of sensor reports and other data points, which can be a challenge to manage and aggregate. Amazon ElastiCache for Redis provides an on-demand managed service with the performance and scalability to turn big data into useful information. Join us to learn how to use Amazon ElastiCache to create serverless solutions that lets you rapidly make use of large and multisource data sets.
Learning Objectives:
• Learn how to ingest and analyze sensor data using Amazon ElastiCache for Redis and the AWS IoT Service
• Learn how to use ElastiCache Redis for Time-Series data
AWS re:Invent 2016: Big Data Architectural Patterns and Best Practices on AWS...Amazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
Making (Almost) Any Database Faster and Cheaper with CachingAmazon Web Services
Learn how to make your AWS databases up to 10x faster and up to 90% less expensive with Amazon ElastiCache for Redis. We’ll look at how to determine whether caching will benefit your database environment and show how to easily test and implement a high speed solution.
Amazon Web Services (AWS) offers a wide range of database options to fit your application requirements. From database services that are fully managed and that can be launched in minutes with just a few clicks to self-managed databases running on EC2. AWS managed database services include Amazon Relational Database Service (Amazon RDS), with support for six commonly used database engines, Amazon Aurora, a MySQL and PostgreSQL-compatible relational database, Amazon DynamoDB, a NoSQL database service or Amazon Redshift, a petabyte-scale data warehouse service. AWS also provides the AWS Database Migration Service, a service which makes it easy and inexpensive to migrate your databases to AWS cloud.
In this webinar, we take a closer look at the AWS database offerings and learn how to quickly select, set up, operate, and scale your database in the cloud.
Learning Objectives:
• Gain insights into the AWS database offering and know which to select for your workload.
• Learn how the AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS) can facilitate and simplify migrating your business critical applications to Amazon Web Services.
• Learn how Amazon DynamoDB Accelerator (DAX) can reduce Amazon DynamoDB response times from milliseconds to microseconds, even at millions of requests per second.
• Hear from our partners like Version1 and Clckwrk who can help you in your journey towards Database freedom.
AWS re:Invent 2016: Event Handling at Scale: Designing an Auditable Ingestion...Amazon Web Services
How does McGraw-Hill Education use the AWS platform to scale and reliably receive 10,000 learning events per second? How do we provide near-real-time reporting and event-driven analytics for hundreds of thousands of concurrent learners in a reliable, secure, and auditable manner that is cost effective? MHE designed and implemented a robust solution that integrates AWS API Gateway, AWS Lambda, Amazon Kinesis, Amazon S3, Amazon Elasticsearch Service, Amazon DynamoDB, HDFS, Amazon EMR, Amazopn EC2, and other technologies to deliver this cloud-native platform across the US and soon the world. This session describes the challenges we faced, architecture considerations, how we gained confidence for a successful production roll-out, and the behind-the-scenes lessons we learned.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
Deep Dive on MySQL Databases on AWS - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about MySQL deployment options on AWS
- Learn how to maintain high availability and security of your data
- Learn how to migrate MySQL databases to Amazon RDS
Amazon Web Services provides startups with the low cost, easy to use infrastructure needed to scale and grow any size business. Attend this session and learn how to migrate your startup to AWS and make the most out of the platform.
It’s been an exciting year for Amazon Aurora, the database with MySQL-compatible and PostgreSQL-compatible database engines. Amazon Aurora combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this deep dive session, we’ll discuss best practices and explore new features, including high availability options, new integrations with AWS services, and the performance management with Amazon RDS Performance Insights.
AWS re:Invent 2016: [REPEAT] How EA Leveraged Amazon Redshift and AWS Partner...Amazon Web Services
In November 2015, Capital Games launched a mobile game accompanying a major feature film release. The back end of the game is hosted in AWS and uses big data services like Amazon Kinesis, Amazon EC2, Amazon S3, Amazon Redshift, and AWS Data Pipeline. Capital Games will describe some of their challenges on their initial setup and usage of Amazon Redshift and Amazon EMR. They will then go over their engagement with AWS Partner 47lining and talk about specific best practices regarding solution architecture, data transformation pipelines, and system maintenance using AWS big data services. Attendees of this session should expect a candid view of the process to implementing a big data solution. From problem statement identification to visualizing data, with an in-depth look at the technical challenges and hurdles along the way.
AWS re:Invent 2016: JustGiving: Serverless Data Pipelines, Event-Driven ETL, ...Amazon Web Services
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), application programming interfaces (API), clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. Building scalable big data pipelines with automated extract-transform-load (ETL) and machine learning processes can address these limitations. JustGiving is the world’s largest social platform for online giving. In this session, we describe how we created several scalable and loosely coupled event-driven ETL and ML pipelines as part of our in-house data science platform called RAVEN. You learn how to leverage AWS Lambda, Amazon S3, Amazon EMR, Amazon Kinesis, and other services to build serverless, event-driven, data and stream processing pipelines in your organization. We review common design patterns, lessons learned, and best practices, with a focus on serverless big data architectures with AWS Lambda.
AWS re:Invent 2016: FINRA in the Cloud: the Big Data Enterprise (ENT313)Amazon Web Services
Large-scale enterprise migration can be a complex undertaking, especially for organizations that re-architect solutions to leverage the benefits of the Cloud. FINRA, which regulates US equities and options markets, recently completed a 2.5-year migration and re-architecture of its Big Data platform. Their platform consumes billions of market events every day. FINRA has developed scalable platforms and services on AWS that enable migrating enterprise applications and business functions to the Cloud quickly. Their data management platform takes advantage of AWS storage and compute products. In this session, IT influencers and decision makers will learn lessons from FINRA’s migration, including how to create an enterprise-class Cloud architecture and which technology skills are required for transitioning to the Cloud. We also share examples of the business value FINRA has realized.
Building Big Data Applications with Serverless Architectures - June 2017 AWS...Amazon Web Services
Learning Objectives:
- Use cases and best practices for serverless big data applications
- Leverage AWS technologies such as AWS Lambda and Amazon Kinesis
- Learn to perform ETL, event processing, ad-hoc analysis, real-time processing, and MapReduce with serverless
Building data processing applications is challenging and time-consuming, and often requires specialized expertise to deploy and operate. With serverless computing, you can perform real-time stream processing of multiple data types without needing to spin up servers or install software, allowing you to deploy big data applications quickly and more easily. Come learn how you can use AWS Lambda with Amazon Kinesis to analyze streaming data in real-time and then store the results in a managed NoSQL database such as Amazon DynamoDB. You’ll learn tips and tricks for doing in-line processing, data manipulation, and even distributed MapReduce on large data sets.
What’s New in Amazon RDS for Open-Source and Commercial DatabasesAmazon Web Services
In the past year, Amazon RDS has continued to expand functionality, scalability, availability and ease of use for all supported database engines: PostgreSQL, MySQL, MariaDB, Oracle and Microsoft SQL Server. We’ll take a close look at RDS use cases and new capabilities, splitting the time between open-source and commercial database engines.
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories.
AWS re:Invent 2016: Operations Automation and Infrastructure Management with ...Amazon Web Services
At Capital One, we are using Docker and container technologies to advance microservices adoption, increase efficiencies of cloud resources, and decouple the application layer from the underlying infrastructure. Capital One is a federated organization with a “you build it, you own it” culture that provides autonomy and speed for delivery teams. Each federated team runs and operates their container management stack. In order for the federated teams to accelerate their cloud and container-based apps adoption, we created self-service automation tools for creation and operations management of container management stack.
In this session, we explore our push-button automation tool that includes capabilities such as the creation and management of Amazon ECS clusters, an Application Load Balancer for dynamic and context-based routing and provides a user interface via a Jenkins Job or a AWS Lambda function. Our tooling also includes a home-grown dynamic service discovery and routing for applications requiring two-way mutual SSL authentication. We talk through how Capital One regularly updates AMIs with the latest patches and software versions using an automated solution that leverages AWS Lambda to rehydrate the Amazon ECS compute cluster with the latest AMI without causing any downtime. We also discuss how we created a sophisticated canary deployment automation using AWS Lambda and application services, where users can specify how to migrate to a new version of containerized apps and manage the deployment.
AWS empowers enterprise Docker deployment with Amazon ECS and an ecosystem of cloud services and serverless architectures, making containerization in mission-critical environments easier than ever.
Amazon Web Services (AWS) provides on-demand computing resources and services in the cloud, with pay-as-you-go pricing. This session provides an overview and describes why companies are flocking to the cloud so quickly.
Overview of Blue Medora - New Relic Plugin for MongoDBBlue Medora
Overview of Blue Medora's New Relic Plugin for MongoDB databases. The Blue Medora New Relic Plugin for MongoDB databases provides support for New Relic Plugins as well as New Relic Insights.
Based upon years of migration experience, Cloudreach will share their 5 top tips for successful Data Centre Migrations to AWS. Covering both digital and enterprise workloads, the session will walk you through the approach, tooling and skills needed to succeed.
Webinar: Schema Patterns and Your Storage EngineMongoDB
How do MongoDB’s different storage options change the way you model your data?
Each storage engine, WiredTiger, the In-Memory Storage engine, MMAP V1 and other community supported drivers, persists data differently, writes data to disk in different formats and handles memory resources in different ways.
This webinar will go through how to design applications around different storage engines based on your use case and data access patterns. We will be looking into concrete examples of schema design practices that were previously applied on MMAPv1 and whether those practices still apply, to other storage engines like WiredTiger.
Topics for review: Schema design patterns and strategies, real-world examples, sizing and resource allocation of infrastructure.
Creating Your Virtual Data Center: VPC Fundamentals and Connectivity OptionsAmazon Web Services
In this session, we will walk through the fundamentals of Amazon Virtual Private Cloud (VPC). First, we will cover build-out and design fundamentals for VPC, including picking your IP space, subnetting, routing, security, NAT, and much more. We will then transition into different approaches and use cases for optionally connecting your VPC to your physical data center with VPN or AWS Direct Connect. This mid-level architecture discussion is aimed at architects, network administrators, and technology decision-makers interested in understanding the building blocks AWS makes available with VPC and how you can connect this with your offices and current data center footprint.
Come learn about new and existing Amazon S3 features that can help you better protect your data, save on cost, and improve usability, security, and performance. We will cover a wide variety of Amazon S3 features and go into depth on several newer features with configuration and code snippets, so you can apply the learnings on your object storage workloads.
Andy Shenkler, Sony's EVP & Chief Solutions & Technology Officer's presentation to the Storage & Archive track at the Media & Entertainment Cloud Symposium on Nov 4, 2016
Getting Started with the Hybrid Cloud: Enterprise Backup and RecoveryAmazon Web Services
This sessions is for architects and storage admins seeking simple and non-disruptive ways to adopt cloud platforms in their organizations. You will learn how to deliver lower costs and greater scale with nearly seamless integration into your existing B&R processes. Services mentioned: S3, Glacier, Snowball, 3rd party partners, storage gateway, and ingestion services.
Amazon Aurora is a MySQL-compatible database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. This session introduces you to Amazon Aurora, explains common use cases for the service, and helps you get started with building your first Amazon Aurora–powered application.
Migrating from the data center to the cloud requires users to rethink much of what they do to secure their applications. CloudCheckr COO Aaron Klein will highlight effective strategies and tools that AWS users can employ to improve their security posture. The idea of physical security morphs as infrastructure becomes virtualized by AWS APIs. In a new world of ephemeral, auto-scaling infrastructure, users need to adapt their security architecture to face both compliance and security threats. Specific emphasis will be placed upon leveraging native AWS services and the talk will include concrete steps that users can begin employing immediately. Session sponsored by CloudCheckr.
Deep Dive: Developing, Deploying & Operating Mobile Apps with AWS Amazon Web Services
In this session we’ll dive deeper into how you can test mobile applications on real devices, using AWS Device Farm, how to get business insights wirh AWS Mobile Analytics and Amazon Redshift, and keep your customers engaged using Amazon SNS Mobile Push and the new Worldwide Delivery of Amazon SNS Messages via SMS.
Getting Started with the Hybrid Cloud: Enterprise Backup and RecoveryAmazon Web Services
This sessions is for architects and storage admins seeking simple and non-disruptive ways to adopt cloud platforms in their organizations. You will learn how to deliver lower costs and greater scale with nearly seamless integration into your existing B&R processes. Services mentioned: S3, Glacier, Snowball, 3rd party partners, storage gateway, and ingestion services.
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.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...Amazon Web Services
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
Data warehousing is a critical component for analysing and extracting actionable insights from your data. Amazon Redshift allows you to deploy a scalable data warehouse in a matter minutes and start to analyse your data right away using your existing business intelligence tools. It’s a fast, fully-managed, and cost-effective data warehousing system. You can analyse all your data using standard SQL and your existing Business Intelligence (BI) tools. Amazon Redshift also includes Redshift Spectrum, allowing you to directly run SQL queries against exabytes of unstructured data in Amazon S3. In this, you will learn how to migrate from existing data warehouses, optimise schemas and load data efficiently. We will also cover analytics tools to help you build visualisations, perform ad-hoc analysis and quickly get business insights from your data.
Learning Objectives:
• Discover best practices for building a data warehouse using Amazon Redshift
• Learn to use Amazon QuickSight for Business Intelligence and AWS Glue for ETL.
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
The database market is large and filled with many solutions. In this talk, Seth Luersen from MemSQL we will take a look at what is happening within AWS, the overall data landscape, and how customers can benefit from using MemSQL within the AWS ecosystem.
AWS re:Invent 2016: How to Build a Big Data Analytics Data Lake (LFS303)Amazon Web Services
For discovery-phase research, life sciences companies have to support infrastructure that processes millions to billions of transactions. The advent of a data lake to accomplish such a task is showing itself to be a stable and productive data platform pattern to meet the goal. We discuss how to build a data lake on AWS, using services and techniques such as AWS CloudFormation, Amazon EC2, Amazon S3, IAM, and AWS Lambda. We also review a reference architecture from Amgen that uses a data lake to aid in their Life Science Research.
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.
Level: Intermediate
Speakers:
Tony Nguyen - Senior Consultant, ProServe, AWS
Hannah Marlowe - Consultant - Federal, AWS
Data Analytics Week at the San Francisco Loft
Using Data Lakes
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.
Speakers:
John Mallory - Principal Business Development Manager Storage (Object), AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Best Practices Using Big Data on AWS | AWS Public Sector Summit 2017Amazon Web Services
Join us for this general session where AWS big data experts present an in-depth look at the current state of big data. Learn about the latest big data trends and industry use cases. Hear how other organizations are using the AWS big data platform to innovate and remain competitive. Take a look at some of the most recent AWS big data developments. Learn More: https://aws.amazon.com/government-education/
Database and Analytics on the AWS Cloud - AWS Innovate TorontoAmazon Web Services
Antoine Genereux, AWS Solutions Architect, takes us on a tour of database solutions available for the AWS Cloud, and powerful analytics and business intelligence reporting tools.
Similar to Managing Data with Voume Velocity, and Variety with Amazon ElastiCache for Redis (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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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!
Elevating Tactical DDD Patterns Through Object Calisthenics
Managing Data with Voume Velocity, and Variety with Amazon ElastiCache for Redis
1. Michael Labib, Specialist Solutions Architect
August 2016
Managing IoT and Time Series
Data with Amazon ElastiCache
for Redis
2. 2
Learning Objectives
Understand Time Series Data & Challenges
Learn about Amazon ElastiCache for Time
Series Data
Learn how to build Time Series Solutions
with Amazon ElastiCache
Explore a Sensor Data Demonstration
4. Time Series Data – What is it?
Device / Sensor Data
Website Clickstream events
Logging and metrics
Social Media and sentiment
analysis
Can be analyzed upon
collection or batches
4
5. Time Series Data – Challenges
What type of database should you use?
Relational or NoSQL
How should you model the data to support the queries and analysis
needed?
What types of aggregations will you need?
What type of information would do you need to gather?
How will the applications access the data?
How do you build the solution in a cost effective manner?
How long should you retain the data?
How do you manage scalability given the amount of data and sensors grow?
And so on…
5
6. Database Families
Relational
+ Mature Technology
+ SQL is widely adopted
+ Durable and Consistent
- Inflexible data model
- Hard to scale
- Performance limitations
- Updates and other changes can be slow
- Limited to speed of storage technology
1 = Row
2 = Column
6
7. Database Families
NoSQL
+ ’Schema-less’ = flexible data model
+ Quickly adapt to new requirements
+ Easier to scale
+ Performance
+ Generally higher throughput and
+ lower latency than Relational DB
- Can be less durable and consistent
- Query tools less mature
7
8. Database Families
NoSQL: In-Memory Key-Value
+ Very fast performance
+ Sub millisecond reads and writes
+ High throughput
+ Schema-Less – Flexible data model
+ Redis - Advanced Data Structures
- Still emerging technology
- Query and other tools less mature
- Less connectors than other database
options
- Less durable
8
9. Request Rate
High Low
Latency
Low High
Structure
Low
High
9
Data Volume
Low High
Amazon
RDS
Amazon S3
Amazon
Glacier
Amazon
CloudSearch and
Amazon
Elasticsearch
Amazon
DynamoDB
Amazon
ElastiCache
HDFS
10. Ingesting Time Series Data with Amazon
ElastiCache
AWS
Lambda
Amazon
Kinesis
Streams
AWS
IoT
Data
Processor
Streaming
Real-time
Data
Device
Data
Amazon
ElastiCache for Redis
Datastore
1
Amazon
EC2
10
13. Redis – The In-Memory Leader
Powerful
~200 commands + Lua scripting
In-memory data structure server
Utility data structures
strings, lists, hashes, sets, sorted
sets, bitmaps & HyperLogLogs
Simple
Atomic operations
supports transactions
has ACID properties
Ridiculously fast!
<1ms latency for most commands
Highly Available
replication
Persistent
snapshots or append-only log
Open Source
13
14. Fully managed service = Automated Operations
Redis datastore hosted on Amazon EC2 Amazon ElastiCache for Redis
14
15. ElastiCache - Customer Value
Extreme
Performance
Sub-millisecond
access latencies
Engineered for
Cloud Scale
Open Source
Compatible
Compatible with
Redis and
Memcached
Existing code
will work when
you update node
end points
Fully
Managed
Automates tasks
such as failed
node
replacement,
software
patching,
upgrades and
backups
CloudWatch
enables you to
monitor cache
performance
metrics
Secure and
Hardened
Supports
Amazon VPC
and IAM for
secure and fine
grained access.
Monitors your
nodes and
applies security
patches when
necessary
Highly
Available
and Scalable
Multi-AZ with
automatic
failover to a
read replica, no
human
intervention
required.
Easily scale
your Redis
(vertically) and
Memcached
(horizontally)
environments
Cost
Effective
Pay as low as
US$0.017 per
hour. Get
started with 750
free hours per
month of a
micro node for a
year.
No cross
availability zone
data transfer
costs.
15
19. Capturing Moving Data
Equity prices, clickstreams, sensor streams …
Amazon Kinesis and AWS Lambda are serverless
Amazon Kinesis + Amazon ElastiCache for Redis ensure
order among incoming data
Kinesis provides an ordered Stream
Redis provides a Sorted Set
Fault Tolerant
Data Retention – Data accessible in Kinesis 24 hours after its
been added to the stream and can be extended up to 7 days.
ElastiCache provides HA with automatic failover to a read replica
ElastiCache is fast and flexible
Can query and retrieve hot data quickly
19
20. ElastiCache for Streaming Data Analytics
Access all Redis Data structures directly from
Spark
Significantly accelerate Spark performance over
disk-based solutions
Simplify analytics by leveraging Redis Data
Structures reducing processing overhead and
complexity
Improve application performance by accessing
aggregations and hot data from Redis
20
21. Event Driven Data
Capture time ordered changes from a
DynamoDB table using DynamoDB Streams
and propagate to ElastiCache
AWS
Lambda
Amazon
DynamoDB
Streams
Amazon
SES
Amazon
S3
Amazon
Cognito
Amazon
SNS
Amazon
CloudWatch
Amazon
Kinesis
Streams
Amazon
API
Gateway
Event Sources
21
Hot Data
Amazon
ElastiCache
AWS
LambdaLonger
Retention
Amazon
DynamoDB
22. Responding to Events
Can cover a huge variety of different data sources
Security events, application events, messaging events, user actions…
For IoT, often created when a sensor crosses a threshold
Lambda captures the incoming event data and routes it
Hot data to ElastiCache
Cold data to slower, disk-based databases
Lambda function to keep databases synchronized
Cost effective
Use the speed and flexibility of ElastiCache for hot data queries
‒ No incremental cost per query – price is driven by amount of
memory available
Use the DynamoDB or RDS for longer data retention
22
24. Sensors and Data Bursts
“We are all glorified motion sensors. Some things only become
visible to us when they undergo change” – Vera Nazarian, author
Sensors enable detailed understanding of conditions
Quickly perceive and react to changing conditions
Device data can be streaming (time-based delivery) or event
driven (threshold-based delivery)
Or both! A temperature sensor might deliver regular readings
and also send an alert when it becomes too hot or too cold
Sensor data solutions must scale to meet loads
Sensor clusters can generate large data bursts
AWS IoT auto scales
ElastiCache for Redis allows flexible sizing to meet changing
workloads
24
25. Data Modeling for Sensors
Sensor data is usually of high variety
Different sensors, and different versions of the same
sensor, collect different data in different formats
A single device can have many sensors
The flexible schema of ElastiCache for Redis allows non-
disruptive evolution of data models
Quickly test and use new or modified data models with
no need to migrate schemas, rebalance shards or other
onerous administrative tasks
25
28. Sensor Demo - Data Model
SensorData
timestamp
deviceId
temperature
humidity
climate
timestamp
Pub/Sub
deviceId
Hash
If (temperature> 95 F)
publish notification
deviceIP
requestId
Strings (INCR/DECR)
climate:warm
climate:cool
climate:hot
climate:cold
Time-Series events
Sort by timestamp (score)
Aggregations
For dashboards, analytics, etc.
Event details
Data structures updated within a
transaction block using Redis MULTI
Which events occurred at a particular time range?
What were the last N events?
Which devices match a particular pattern?
What is the temperature for a specific device?
What is all the data for a specific device?
What are the totals for each
climate type?
get climate:warm
get climate:cool
get climate:cold
get climate:hot
zrevrangebyscore SensorData +inf -inf WITHSCORES LIMIT 0 5
zrangebyscore SensorData (1410000000000
14400000000000
zscan SensorData 0 MATCH 2* COUNT 100
hget 2221 temperature
hgetall 2221
Sorted Set
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29. Summary
Time series data can present high
volume, velocity and variety challenges
ElastiCache for Redis is scalable,
performant and cost effective service for
sensor data and other time series data
sets
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