Paws.analytics: 'Amazon Web Services' Analytics Services

This study delves into the optimization strategies of Amazon Web Services (AWS) cloud computing solutions tailored specifically for businesses. It provides an in-depth exploration of the transformative impact that cloud computing has had on modern business operations, emphasizing the pivotal role played by AWS in delivering scalable, flexible, and cost-effective resources. With projections indicating the global public cloud services market's growth to $332.3 billion in 2021, it becomes evident that cloud solutions are increasingly relied upon, with AWS commanding a significant 32% market share. This paper highlights the importance of optimizing AWS solutions, considering its extensive range of services designed to meet diverse business needs. However, transitioning to cloud environments brings challenges related to data security, compliance, integration, and migration complexities, as evidenced by pertinent studies and literature reviews. This research synthesizes insights gleaned from various studies, emphasizing the adaptability and cost-effectiveness of AWS while stressing the critical role of robust security measures in its implementation across industries and geographic regions. Understanding prevalent business requisites and challenges in cloud adoption, including scalability, cost efficiency, reliability, availability, and compliance, is essential for organizations seeking to harness cloud technology effectively. Furthermore, this paper provides an overview of AWS's services encompassing computing, storage, databases, machine learning, and AI, showcasing how these empower businesses to streamline operations, foster innovation, and scale dynamically. It elucidates successful instances where AWS was strategically implemented in Netflix, Airbnb, Jollibee Group, and Capital One, demonstrating its impact on scalability, innovation, and service enhancement. The paper outlines anticipated trends in AWS and cloud computing, focusing on user-friendly tools, advancements in machine learning, infrastructure improvements, increased automation, and deeper integration of AI and advanced analytics. Ultimately, this research emphasizes business's need to optimize AWS solutions, navigate complexities, foster innovation, and achieve operational excellence in an increasingly competitive market.

This article explores the transformative role of Amazon Web Services (AWS) in facilitating human-AI collaboration across diverse industry sectors. It begins by establishing AWS's robust AI infrastructure foundation, centered on services like Amazon SageMaker that democratize access to sophisticated machine learning capabilities. It investigates the profound impact of AWS-powered AI systems on decision-making processes in healthcare, where diagnostic accuracy and preventive care have been enhanced, and in education, where personalized learning pathways have revolutionized academic outcomes. The discussion extends to productivity enhancements achieved through AWS AI services including Amazon Lex, Polly, Rekognition, and Comprehend, which have streamlined workflows and reduced operational costs while improving service quality. It further explores how AWS enables unprecedented personalization in e-commerce, entertainment, marketing, and financial services through advanced data processing and AI algorithms. Detailed attention is given to specialized collaborative tools such as Amazon Augmented AI, AWS Ground Truth, and Amazon Kendra, which establish effective human-AI workflows. Industry-specific implementations in manufacturing, financial services, and customer support demonstrate the practical applications of these technologies. It concludes by addressing critical challenges in data privacy, AI transparency, workforce development, and ethical governance that organizations must navigate to fully realize the benefits of AWS-powered human-AI collaboration, while highlighting AWS's ongoing evolution to address these concerns.

Public clouds such as Amazon Web Services (AWS) and Microsoft's Azure provide excellent capabilities for scalable Web applications and Hadoop-based processing. Recent additions to public clouds to support connected devices and IoT have the potential to similarly disrupt emerging home-grown and/or proprietary approaches. While early public cloud IoT success stories have focused on smaller-scale scenarios such as connected houses, it is unclear to what extent these new public cloud mechanisms and abstractions are suitable and effective for larger-scale and/or scientific scenarios, which often have a different set of constraints or requirements. In this paper, the design and implementation of a representative cloud-based IoT infrastructure in a specific public cloud - AWS - is presented. The system created is for dynamic vehicle traffic control based on vehicle volumes/patterns and public transport punctuality. We find that constructing server-less, stateful, and data driven IoT applications in AWS that can operate in real-time is non-trivial. The primary challenges span application manageability and design, latency performance, asynchronicity, and scalability.

<title>Abstract</title> Serverless computing reduces management overhead and scales automatically, yet its latency and performance differ significantly across cloud platforms. This study evaluates serverless function execution and infrastructure-level characteristics on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). We benchmark function invocation latency in edge deployments, conduct regional latency comparisons across AWS and Azure, and use PerfKit Benchmarker to measure network throughput, network latency, and storage I/O on AWS and GCP. Results show that Azure recorded the lowest minimum latency (555 ms) but suffered from high tail delays, while GCP delivered the most consistent average performance (1.14 s). AWS exhibited moderate latency with a relatively stable distribution. At the infrastructure level, GCP nearly doubled single-stream throughput (9.35 Gb/s) compared to AWS (4.97 Gb/s), whereas AWS achieved slightly lower round-trip network latency (60 vs. 67 $\mu$s). Storage I/O performance was nearly identical across both providers. These findings link infrastructure characteristics to observed serverless behavior, providing actionable insights for latency-sensitive and multi-cloud application deployments.

The Amazon Web services (AWS) infrastructure is very much essential for a newcomer for an organization for its usability and requirement for work environment. In order to have the adequate knowledge to build the AWS infrastructure here, in this project, we mainly focus on such kind of employees to help them to create the AWS infrastructure with ease. We also discussed various aspects and challenges while designing the AWS infrastructure. Here, in this paper, we focus on some of the basic steps to create as well as guide to creating the AWS infrastructure. We also provide the code as well as a flowchart model for a better explanation of the building work.

Enterprise IT infrastructures experience a transformation because of rapid cloud computing adoption, especially Amazon Web Services (AWS), which delivers scalability, flexibility, and cost efficiency. AWS users encounter difficulties optimizing their performance because they face problematic situations combining workload variability with complex resource distribution systems and delay issues. The implementation of Artificial Intelligence constructs an evaluation process to boost performance optimization in Amazon Web Services (AWS) cloud systems. AI-powered insights implemented through machine learning algorithms with predictive analytics capabilities and automation functionality improve workload distribution and enhance auto-scaling efficiency while reducing operational expenses. Processor analysis of historical data combined with real-time metrics allows AI algorithms to make proactive decisions about resource administration, detect anomalies, and perform innovative management. The research evaluates performance-altering AI-based approaches that enhance AWS services, including EC2, Lambda, and Sage Maker. Implementing AI-based optimizations generates copious improvements in computational efficiency and cost performance based on experimental data. Thanks to AI, cloud performance management strategies will fundamentally transform since they endorse efficient AWS environments that align with business requirements.

The rapid evolution of Amazon Web Services (AWS) cloud technologies continues to reshape enterprise computing environments across global industries. This technical review examines recent innovations in AWS services that are transforming organizational capabilities and competitive positioning. Beginning with computational performance advancements through AWS Graviton processors and specialized High-Performance Computing offerings, the article explores how these ARM-based architectures deliver enhanced efficiency across diverse workloads. The expanding AI and Machine Learning ecosystem, particularly through AWS Bedrock's foundation model integration and SageMaker's democratized ML tools, enables organizations to implement sophisticated intelligence capabilities without extensive expertise. Serverless computing developments, including Lambda Function URLs and SnapStart features, alongside visual workflow orchestration through Step Functions, have simplified application development while improving performance characteristics. Container orchestration enhancements facilitate consistent hybrid deployments across on-premises and cloud environments. Looking forward, AWS continues strategic investments in quantum computing initiatives, sustainability practices, and multi-cloud compatibility tools that position the platform at the forefront of cloud innovation. These advancements collectively enable organizations to achieve greater operational agility, cost efficiency, and innovation capacity while addressing evolving challenges in security, compliance, and technical workforce development.

Cloud computing is increasingly becoming a norm in the global industrial landscape with web services using it as a critical backbone service provider including significant video streaming services. The purpose of this paper is to present an overview of some of the Amazon Web Service (AWS) tools and features. The paper also discusses how AWS contributes to a range of industries making them dependent on AWS’s agility, scalability and deliverability of services. Implementation using information available on AWS leading portals as well as the practical experiments involving the discussed features are given. As more and more industries become dependent on Big data and AI, one can state that AWS will play a key role in the future developments across myriads of industries.

Cloud computing disaster recovery (DR) has transformed into an indispensable component of business continuity and operational resiliency in the modern IT system. The study offers insights on how the DR sites and Blue/Green deployment policies can be applied to Amazon Web Services (AWS), their applicability in the real world, performance, and scalability. The research demonstrates AWS service intersectional benefits, upgrading of such services as Elastic Compute Cloud (EC2), Simple Storage Service (S3), Route 53, and Elastic Beanstalk when improving Recovery Time Objectives (RTO), Recovery Point Objectives (RPO), and reducing system downtime. The data indicates that AWS DR placements have reduced 45-minute to go down (RTO), 30-minute to recover (RPO), and 70% of significant downtime per annum in organizations that have been deployed to them, an equivalent of 25-40% of the cost of downtime. Blue/green deployments also boost performance with a success rate of 95% in the number of deployments once a system is deployed, and decrease user-facing downtime to as much as 90% when updates are made to a system. Such enhancements underscore the ability of AWS to provide affordable, automated, and resilient disaster recovery to accommodate various domains, including e-commerce, healthcare, and finance. The study concludes that AWS offers a scalable operating system of high-availability disaster recovery and determines potential future research prospects in artificial intelligence-based automation and hybrid cloud approaches to further streamline cloud-based resilience frameworks.

The study delves into AWS's intricacies to reveal its latent capabilities and emphasise the revolutionary advantages it offers to both people and corporations. By analysing various AWS features, this study investigates the integration of Amazon Web Services (AWS) to develop scalable web applications, focusing on the configuration and deployment of key AWS components such as Elastic Compute Cloud (EC2), Auto Scaling, and Simple Storage Service (S3). Two experimental scenarios are conducted: a control scenario with a fixed number of EC2 instances and a dynamic scaling scenario utilising SLAstic for automatic horizontal scaling. The experiments employ m1. Large EC2 instances, monitoring performance metrics like CPU utilisation and response time through Amazon CloudWatch. The outcomes reveal that the dynamic scaling approach significantly enhances resource optimisation and application performance.

BackgroundWhole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50× WGS analysis task, which is far from the ideal speed required by the industry. Furthermore, the high-end infrastructure required by WGS computing is costly in terms of time and money. In this paper, we aim to improve the time efficiency of WGS analysis and minimize the cost by elastic cloud computing.ResultsWe developed a distributed system, GT-WGS, for large-scale WGS analyses utilizing the Amazon Web Services (AWS). Our system won the first prize on the Wind and Cloud challenge held by Genomics and Cloud Technology Alliance conference (GCTA) committee. The system makes full use of the dynamic pricing mechanism of AWS. We evaluate the performance of GT-WGS with a 55× WGS dataset (400GB fastq) provided by the GCTA 2017 competition. In the best case, it only took 18.4 min to finish the analysis and the AWS cost of the whole process is only 16.5 US dollars. The accuracy of GT-WGS is 99.9% consistent with that of the Genome Analysis Toolkit (GATK) best practice. We also evaluated the performance of GT-WGS performance on a real-world dataset provided by the XiangYa hospital, which consists of 5× whole-genome dataset with 500 samples, and on average GT-WGS managed to finish one 5× WGS analysis task in 2.4 min at a cost of $3.6.ConclusionsWGS is already playing an important role in guiding therapeutic intervention. However, its application is limited by the time cost and computing cost. GT-WGS excelled as an efficient and affordable WGS analyses tool to address this problem. The demo video and supplementary materials of GT-WGS can be accessed at https://github.com/Genetalks/wgs_analysis_demo.

The increasing migration of enterprise operations to Amazon Web Services (AWS) has amplified the need for robust, scalable, and intelligent cybersecurity solutions. Security Information and Event Management (SIEM) systems have become vital for detecting threats, correlating logs, and maintaining compliance in cloud environments. This systematic review explores the integration of SIEM tools within AWS-based infrastructures, focusing on their effectiveness in threat detection and log correlation. It examines leading SIEM solutions such as Splunk, IBM QRadar, Sumo Logic, and AWS-native services like Amazon GuardDuty, AWS CloudTrail, and AWS Security Hub. Emphasis is placed on key integration approaches, including API-based ingestion, agentless data capture, and real-time event streaming through AWS services like Kinesis and S3. The review critically analyzes studies published between 2018 and 2023, highlighting trends in the automation of log management, enrichment of security alerts through machine learning, and orchestration via Security Orchestration, Automation, and Response (SOAR) platforms. Challenges such as data normalization, scalability limitations, cross-service visibility, and compliance adherence in multi-account AWS architectures are discussed. The findings indicate that SIEM integration enhances threat detection efficiency by enabling proactive anomaly detection, facilitating rapid incident response, and improving forensic investigation capabilities. However, the review identifies gaps, particularly in cost optimization, handling high-velocity log streams, and adapting traditional SIEM models to dynamic, serverless AWS architectures. Best practices for successful SIEM deployment include leveraging AWS-native integrations, prioritizing event prioritization algorithms, applying continuous tuning, and aligning with security frameworks like NIST and CIS AWS Foundations Benchmark. Future research directions propose the development of AI-driven adaptive SIEM systems tailored for cloud-native environments, advanced correlation engines for serverless and containerized workloads, and strategies to optimize licensing and resource utilization. This systematic review provides cybersecurity practitioners, cloud architects, and researchers with a comprehensive understanding of SIEM integration complexities and evolving practices in AWS infrastructures, ultimately contributing to improved cloud security postures and operational resilience in the face of sophisticated cyber threats.

Setting and running Kubernetes on Amazon Web Services (AWS) is a very involved process. AWS has decided to not implement Kubernetes as a Service but built something of its own - Amazon EC2 Container Service (ECS). Hence, you need to know and consider a lot of things before you can successfully roll out Kubernetes on AWS. These are the consideration which can make or break it your Kubernetes rollout.

Cloud-based data warehousing has emerged as a strategic necessity in highly dynamic industries where rapid decision-making and customer responsiveness are critical. Traditional on-premise systems lack the scalability, flexibility, and cost-effectiveness to sustain marketing agility in such volatile markets. In this study, the role of cloud platforms, particularly Amazon Web Services (AWS) and Snowflake, is examined with a focus on enabling marketing agility through unified data management, real-time analytics, and customer-centric insights. Lessons from FinTech migrations emphasize the importance of data architecture planning, workload optimization, performance scalability, and cost-efficient resource utilization. Moreover, customer segmentation, predictive analytics through integration of machine learning and artificial intelligence, along with customized campaigns will enable organizations with competitive advantage. Competitive analysis emphasizes the ways in which visualization tools and analytics services can be used to expand the value of cloud-native solutions by providing actionable information to engage with customers. According to the findings, cloud-based data warehousing is a key component of digital transformation as it helps businesses achieve operational efficiency, compliance, and market flexibility in quickly changing business environments

Urban has developed an elastic and powerful approach to the analysis of massive datasets using Amazon Web Services’ Elastic MapReduce (EMR) and the Spark framework for distributed memory and processing. The goal of the project is to deliver powerful and elastic Spark clusters to researchers and data analysts with as little setup time and effort possible, and at low cost. To do that, at the Urban Institute, we use two critical components: (1) an Amazon Web Services (AWS) CloudFormation script to launch AWS Elastic MapReduce (EMR) clusters (2) a bootstrap script that runs on the Master node of the new cluster to install statistical programs and development environments (RStudio and Jupyter Notebooks). The Urban Institute’s Spark for Social Science Github page holds code used to setup the cluster and tutorials for learning how to program in R and Python.