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A brief introduction to IaC with Terraform by Kenton Robbins Managing cloud infrastructure can be a complex and time consuming process. Using Terraform, we are able to create a blueprint capable of reproducing your infrastructure simply by running a script. Find out how 'infrastructure as code' can reduce operational costs and risk while increasing efficiency and stability. Hosted by Alex Cachia, codeHarbour provides an opportunity for discussion and a platform for digital presenters to get their technological ideas out there to the people who need to hear it.

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Let’s roll with amazon web services

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With distributed frameworks like Hadoop and Kafka, it is essential to deploy the right environment to successfully support these workloads. Learn about the different block storage options from AWS and walk through with our experts on how to select the best option for your big data analytic workloads. We will demonstrate how to setup, select, and modify volume types to right size your environment needs.

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The Cloud and Amazon Web Services 2014 AWS

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In this session, we share how an Amazon.com team that owns a document management platform that manages billions of critical customer documents for Amazon.com migrated from a relational to a non-relational database. Initially, the service was built as an Oracle database. As it grew, the team discovered the limits of the relational model and decided to migrate to a non-relational database. They chose Amazon DynamoDB for its built-in resilience, scalability, and predictability. We provide a template that you can use to migrate from a relational data store to DynamoDB. We also provide details about the entire process: design patterns for moving from a SQL schema to a NoSQL schema; mechanisms used to transition from an ACID (Atomicity, Consistency, Isolation, Durability) model to an eventually consistent model; migration alternatives considered; pitfalls in common migration strategies; and how to ensure service availability and consistency during migration.

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Amazon Web Services

Developers and DBAs from a traditional relational background are spoilt for choice when looking to integrate caching and NoSQL into an application architecture to solve scaling problems and reduce costs. Even when using relational databases there are 3 managed database services on AWS for the MySQL engine alone. Trying to evaluate all the options often creates analysis paralysis, resulting in a reluctance to try something new or different. This session will guide you through a series of use cases that use different databases to solve business problems that customers face today.

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Cost and Performance Optimisation in Amazon RDS This session is for database administrators and other technical users looking to learn the top techniques for optimising the performance and cost of operating Amazon RDS. You will leave with a toolkit of best-practices that can be applied to your deployments for achieving optimal performance, flexibility, and cost-savings. Brad Staszcuk, Solutions Architect, Amazon Web Services

Cost and Performance Optimisation in Amazon RDS - AWS Summit Sydney 2018

Amazon Web Services

Pitney Bowes is reinventing its business based on a SaaS and a cloud-based model to deliver services for their clients globally centered on the Pitney Bowes Commerce Cloud. The Pitney Bowes Commerce Cloud is a commerce enabler, providing access to solutions, analytics and APIs across the full commerce continuum with speed and agility to help clients identify customers, locate opportunities, enable communications, power shipping from anywhere to everywhere, and manage payments. During this session, the Pitney Bowes team will discuss the strategy behind the Commerce Cloud, how we accelerated the speed of innovation and creation of new product solutions by taking full advantage of the AWS platform, from Cloud Infrastructure Services (Compute, Big Data, Storage and Content Delivery, Databases and Networking) , to Analytics and Internet of Things Services. Pitney Bowes’ applications are deployed in Docker containers using AWS Elastic Beanstalk, and are utilizing S3, Amazon RDS, SQS, SNS, and ElastiCache. CloudWatch and CloudFormation are being used to manage the solutions that Pitney Bowes’ brings to the market. Additionally, the Pitney Bowes Data and Analytics platform is powered by Elastic Map Reduce (EMR), Spark, Aurora, DynamoDB and Postgres. The session will also discuss Pitney Bowes’ partnership with AWS to deploy Pitney Bowes’ APIs for Location Intelligence via the AWS Marketplace, allowing developers, customers and partners to access innovative location intelligence data services in an easy to consume and highly reliable manner.

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Amazon Web Services

The session covers how to build web, mobile, Api solutions using Microsoft Azure platform. The session covers in detail how to build intelligent solutions using Azure App Service. Azure App Service includes web apps, logic apps and mobile apps services as PaaS solutions for engineers. The session is a demo driven and demonstrate how to design and provision complete end-to-end solutions using Microsoft intelligent cloud.

Build intelligent solutions using ms azure

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John Newton, founder and CTO of Alfresco, describes how Amazon Aurora enables the Alfresco Content Management System to store, manage, and retrieve billions of documents and related information with fast and linear scalability. Using new techniques of information modeling, indexing, and processing with the recently launched Aurora database, Alfresco can support cloud-based workloads previously not possible for high-throughput insurance, banking, and case-based applications. This session addresses the challenges of scaling document repositories to this level; architectural approaches for coordinating data; search and storage technologies such as Aurora, Solr, Amazon EBS, and Amazon S3; the breadth of use cases that modern content systems need to support; and how to support user applications that require subsecond response times. The result is a solution that once would have required large data centers to support but can now be handled cost-effectively with AWS and Aurora.

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The ability to create global namespace, high availability, and volume economics enables unique capabilities when Red Hat Storage Server is used on Amazon Web Services (AWS). In this session, you’ll learn about the capabilities and the potential of using AWS ephemeral storage, availability zones, and regions to deliver a truly distributed cloud storage solution that is geographically diverse, cost effective, and highly available.

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In 2006, Amazon Web Services (AWS) began offering IT infrastructure services to businesses in the form of web services - now commonly known as cloud computing. Since then, our pace of innovation has continued rapidly. Let's take a look at some of the exciting announcements and latest service updates over the past 6 months and learn about: - New features and enhancements to existing services such as Amazon EC2, Amazon CloudFront, Amazon Redshift and Amazon SNS - How these features and services fit together in the overall AWS landscape - New solutions and approaches to common IT use cases that are now possible

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Modern application delivery pipelines rely on series of increasingly complex test environments. Manual processes and ad-hoc management typically lead to misconfigured environments, scheduling conflicts, and project delays. You know automation and transitioning resources to the cloud can help, but don’t know where to start and unclear how to prove the value. Join Amazon Web Services (AWS) and Plutora in this joint presentation on managing test environments and transitioning them to the cloud. Learn secrets about Why you need a single source of truth to smooth out scheduling kinks. How to improve configuration tracking and management to enhance validation efforts. The best way to manage the complexity of large-scale test environments. How to reduce costs and eliminate conflicts by identifying and moving environments to the cloud.

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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.

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Application requirements have changed dramatically in recent years, requiring millisecond or even microsecond response times and 100 percent uptime. This change has led to a new wave of andquot;reactive applicationsandquot; with architectures that are event-driven, scalable, resilient, and responsive. In this session, we present the blueprint for building reactive applications on AWS. We compare reactive architecture to the classic n-tier architecture and discuss how it is cost-efficient and easy to implement using AWS. Next, we walk through how to design, build, deploy, and run reactive applications in the AWS cloud, delivering highly responsive user experiences with a real-time feel. This architecture uses Amazon EC2 instances to implement server push to broadcast events to application clients; AWS messaging (Amazon SQS/SNS); Amazon SWF to decouple system components; Amazon DynamoDB to minimize contention; and Elastic Load Balancing, Auto Scaling, Availability Zones, Amazon VPC, and Amazon Route 53 to make reactive applications scalable and resilient.

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Cloud computing gives you a number of advantages, such as the ability to scale your web application or website on demand. If you have a new web application and want to use cloud computing, you might be asking yourself, "Where do I start?" Join us in this session to understand best practices for scaling your resources from zero to millions of users. We show you how to best combine different AWS services, how to make smarter decisions for architecting your application, and how to scale your infrastructure in the cloud.

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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

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What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

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Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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The Raspberry Pi 5 was announced on October 2023. This new version of the popular embedded device comes with a new iteration of Broadcom’s VideoCore GPU platform, and was released with a fully open source driver stack, developed by Igalia. The presentation will discuss some of the major changes required to support this new Video Core iteration, the challenges we faced in the process and the solutions we provided in order to deliver conformant OpenGL ES and Vulkan drivers. The talk will also cover the next steps for the open source Raspberry Pi 5 graphics stack. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://eoss24.sched.com/event/1aBEx

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