Category: Monitoring and observability

Coverage and utilization metrics can be misleading and do not always tell the full story when it comes to managing commitments and measuring their impact. Effective Savings Rate (ESR) is an objective FinOps metric that measures your true ROI. In this blog post, we will discuss challenges with coverage and utilization, why ESR is important, benchmarking insights, and automation with ProsperOps.

By: Anshu Kapoor, Sr. PSA – AWS By: Diego Dalmolin, WW Resilience Principal PSA – AWS   Assessing the resilience posture is an important step to build reliable and highly available applications in the cloud. A resilient application is one that is capable to respond, withstand, and recover from failures. By evaluating the resilience posture […]

As data volumes grow, visibility into key metrics becomes crucial for optimizing reliability, performance, and cost. Pariveda’s observability solution leverages AWS services to build operational dashboards displaying AWS Glue job details like runtimes, status, and computational load. By unlocking deeper insights, users can pinpoint optimization opportunities, troubleshoot issues faster, and drive greater efficiency across their data pipelines as workloads scale.

Amazon Security Lake centralizes security data from multiple AWS sources into a customer-owned data lake. A New Relic integration provides a single pane for performance and security telemetry, ingests Amazon Security Lake data, and allows threat detection via curated dashboards and anomaly alerts. This solution improves cloud security posture by consolidating data, providing insights, and enabling automated response to potential threats.

Data observability enables monitoring, understanding, and troubleshooting data pipelines to ensure smooth and efficient workflows. By tracking metrics like lineage, volume, and schema, data engineers can quickly identify issues, optimize performance, and make informed decisions. TCS outlines an approach using AWS services for event ingestion, aggregation, and visualization to address observability challenges.

NETSCOUT’s nGeniusONE platform offers insights into service delivery and user experience to manage availability and quality, reducing time to resolve performance issues by correlating metadata across network, applications, and devices. This post discusses implementing holistic visibility using NETSCOUT APM with VPC traffic mirroring and AWS Gateway Load Balancer. NETSCOUT delivers consistent, high-resolution visibility to identify and manage threats and performance in cloud environments.

AWS offers a wide range of services that can help implement Industry 4.0 processes and tools seamlessly. Concept Reply supports and advises customers on all aspects of IoT and cloud computing—from designing and developing customized IoT solutions to implementing and managing them seamlessly. Learn how AWS services can be utilized to develop a scalable IoT solution that offers high performance and low latency, along with monitoring capabilities that help achieve Industry 4.0 standards.

In this post, delve into the process of detecting tenant activities within microservices spanning multiple AWS accounts. We provide insights into instrumenting AWS Lambda functions to include tenant information in tracing using ADOT and demonstrated how to establish a service map across several AWS accounts using Amazon CloudWatch. By leveraging AWS observability technology, SaaS providers can enhance operational efficiency and redirect their attention towards their desired development goals.

By automating performance testing and including chaos testing, organizations can identify failure scenarios in systems before they develop and cause downtime. Learn how Distributed Load Testing on AWS (DLT) automates performance testing at scale, and how AWS Fault Injection Simulator (AWS FIS) performs controlled chaos engineering experiments on AWS resources. By combining the power of AWS FIS and DLT, organizations can perform comprehensive resilience testing and continuously validate their systems.

With the rise of large language models, natural language processing, and generative AI models, embeddings are becoming a critical piece of data in more machine learning use cases. In this post, explore different ways embeddings are used in machine learning and where problems can show up that impact your ML models, and how you can use WhyLabs to identify those problems and create monitors to avoid them showing up again in the future.