Amazon FSx for Windows File Server is a fully managed file storage service built on Windows Server. Migrating on-premise Windows file systems to a managed service like FSx enables organizations to reduce operational overhead and take advantage of the flexibility and scalability of the cloud. But having visibility into file access activity across their environment is key for security and compliance requirements, particularly in sectors such as financial services and healthcare.

Running any application in production assumes reliable monitoring to be in place and serverless applications are no exception. As modern cloud applications get more and more distributed and complex, the challenge of monitoring availability, performance, and cost get increasingly difficult. Unfortunately there isn’t much offered right out of the box from cloud providers.

The SIEM is a central point where data is collected and correlated, and as we move to consume more cloud services and data sets the SIEM itself must also change in architecture. Architecture change is hard to make for existing products. Calling a product a ‘cloud solution’ is not the same as taking an on-premises product and hosting it for customers. It means building a new SIEM for a new world. There are a lot of reasons users seek new SIEMs.

Recently, we released our new “Calico Certified Operator: AWS Expert” course. You can read more about why we created this course and how it can benefit your organization in the introductory blog post. This blog post is different; it’s an opportunity for you, the potential learner, to get a glimpse of just a few interesting parts of the course. You won’t learn all the answers here, but you’ll learn some of the questions!

Edge computing is gaining huge momentum lately, and with the onset of 5G, the opportunities are endless. Moreover, it ensures or brings computation and data storage closer to where the data is generated, further enables better control, reduces costs, provides faster and actionable insights, and supports continuous operations. In fact, by 2025, 75% of enterprise data will be processed at the edge, compared to only 10% today, as predicted by Gartner.

Logz.io is proud to launch a new partnership with Microsoft that enables Azure customers to directly integrate with Logz.io’s platform from within the Azure Console. This integration importantly allows Azure developers to begin monitoring their workloads faster than ever before, using the open-source technologies that their teams love. Check out this video for a demonstration of how it works.

At Grafana Labs, we are proud to be one of the largest code contributors to Cloud Native Computing Foundation projects. We are currently the leading company contributor to Prometheus, and also make substantial contributions to Cortex, Thanos, Jaeger, and OpenTelemetry. Our own open source projects — Grafana, Grafana Loki, and Grafana Tempo — have also become fundamental parts of the cloud native ecosystem.

In any case, by using the MITRE ATT&CK framework to model and implement your cloud IaaS security, you will have a head start on any compliance standard since it guides your cybersecurity and risk teams to follow the best security practices. As it does for all platforms and environments, MITRE came up with an IaaS Matrix to map the specific Tactics, Techniques, and Procedures (TTPs) that advanced threat actors could possibly use in their attacks on Cloud environments.

Industry 4.0 is taking every industry by storm with unprecedented advancement with innovative technology solutions. Its key technologies such as automation, AI, ML, Data Analytics and IoT enables industries to drive business operations with automated data-driven intelligence. Integrating the physical and digital systems, the manufacturing industry is increasingly adopting intelligent manufacturing in this Industry 4.0 era.

AWS Auto Scaling groups are a powerful tool for creating scaling plans for your application. They let you dynamically create a group of EC2 instances that will maintain a consistent and predictable level of service. HAProxy’s Data Plane API adds a cloud-native method known as Service Discovery to add or remove these instances within a backend in your proxy as scaling events occur. In this article, we’ll take a look at the steps used to integrate this functionality into your workflow.

In this article you’ll find out how to 10x your development speed with local serverless debugging. Questions such as “what happens when you scale your application into millions of requests?”, “what to expect when going serverless?”, “how does it look like?”, or “how is it to build applications on serverless and work locally?” will be addressed.

Ayyeka connects decision makers to their remote assets, so they can make better and timely decisions. Their Industrial IoT solutions for remote monitoring enable asset owners and operators to achieve enhanced compliance and improved efficiency across the board. Hardware, software, and digital services are merged to deliver an end-to-end experience and value when connecting asset and infrastructure networks to data systems.

Images are one of the most basic, common attributes for your virtual machines (VMs), and contain the operating system which may be customized with specific installations and features. It is necessary to keep VM images organized and structured so that they are easily maintained, managed, and are accessible. Azure introduced their Shared Image Gallery to help solve this, giving users a way to manage, share and distribute custom images.

As everything good in life, serverless also comes with its downsides. One of them is the infamous “cold start”. In this article, we’ll cover what they are, what influences serverless startup latency, and how to mitigate its impacts in our applications.

Globally the impact of AI is increasingly growing year on year in every industry sector. Opening new scopes in construction & architecture sector, the global AImarket is estimated to grow at a CAGR of 29.4% (around) from 2019 to 2026 and is expected to reach around US$ 2.1 Billion by 2026.

CloudOps is on the up. This is in part due to the rapid acceleration of the shift to cloud that was caused by the pandemic. The shift allowed companies to innovate faster, enjoy greater flexibility and scalability, and become more cost efficient. Many organizations who rapidly adopted cloud or increased their usage now realize that they need to better manage their cloud investments in order to fully embrace these benefits.

Serverless lets you deploy applications far away in a data center of a cloud provider. This relieves you of the lion’s share of operational burdens. The more you buy into your cloud provider’s ecosystem, the less you have to do yourself: no more OS updates or database bugfix installations. But you still need to do some operation-related work on your own. For instance, monitoring your application to know what’s going on in that far away data center.

Running and troubleshooting production services requires deep visibility into your applications and infrastructure. While basic logs and metrics are available out of the box with Google Cloud Compute Engine (GCE), capturing advanced data used to require the installation of both a metrics agent and a logging agent.

Splunk Cloud Architect Paul Davies recently authored and released the GCP Application Template, a blueprint of visualizations, reports, and searches focused on Google Cloud use cases. Many of the reports included in his application require Google Cloud asset inventory data to be periodically generated and sent into Splunk. But HOW exactly do you craft that inventory generation pipeline so you can "light-up" Paul's application dashboards and reports?

In this post we will discuss some key considerations and strategies to monitor your AWS Lambda functions. This will include: which Lambda metrics you’ll want to monitor, how to collect AWS Lambda metrics with Prometheus and Logz.io, how to create a monitoring dashboard with alerts, and how to search and visualize your metrics.

A deep dive into boto3 and how AWS built it. AWS defines boto3 as a Python Software Development Kit to create, configure, and manage AWS services. In this article, we’ll look at how boto3 works and how it can help us interact with various AWS services.

A cloud platform can be described as a storage place that is highly accessible, flexible, scalable and agile in all its characteristics. Users from anywhere via the internet can access their data, collaborate, share, and work together in real-time with the cloud, which means anything less than a cloud platform cannot be possible to comprehend today. The substantial dominance of cloud platforms is their authenticity and usability.

When we launched Qovery in January 2020, our product was only supporting app deployment on Amazon Web Services (AWS). 20 months later, 5534 developers from more than 120 countries use Qovery to deploy their apps on AWS and Digital Ocean. Today, more and more European companies would love to benefits from the excellent user experience of Qovery on a European cloud service provider 🇪🇺.

More and more cloud providers are emerging and spreading their business across different regions in the current times. As a result, customers want to reduce their IT workload and migrate their product or application to a cloud-based environment. The primary reason behind this is that the cloud heavily reduces the overheads of investing in IT infrastructure, hardware upgrades, maintenance, etc.

Being alerted to an issue with your application before your customers experience undue interruption is a goal of every development and operations team. While methods for identifying problems exist in many forms, including uptime checks and application tracing, alerts on logs is a prominent method for issue detection. Previously, Cloud Logging only supported alerts on error logs and log-based metrics, but that was not robust enough for most application teams.

The evolution of Spot’s products has always been tightly aligned with our customers. Over the years, we’ve added integrations across the cloud stack, and developed new features that bring even more efficiency, automation and optimization to cloud infrastructure operations.

Speaking with thousands of organizations adopting cloud computing, it’s clear that the preferred way to consume cloud, from infrastructure to platforms to applications, is “as a service”. Customers want fully managed services, with security and SLAs built in.

AWS Step Functions is a powerful orchestration service that lets you model even the most complex business workflows. It packs a great visualization tool (which you can also use to design your workflows visually now!) and can integrate with many AWS services directly, including Lambda, DynamoDB, and API Gateway. It’s one of my favorite AWS services and I often use it to model complex or business-critical workflows.

Nowadays, monitoring is very important. Why is that? Because applications become more and more complicated. But not only applications—infrastructure becomes complicated too. Some companies are moving to the cloud; others are building hybrid infrastructure. And if some pieces of infrastructure are in the cloud while others are on-premises, this creates even more unclarity on how to get an overview of the infrastructure as a whole.

For a successful cloud migration, creating (and later refining), a cloud migration strategy tailored to your organization’s goals, available resources, workloads, and priorities is an absolute necessity. So here, we’ll take a look at a simple list of the areas you’ll need to quantify and understand to build that strategy and then improve it as you move forward through the cloud migration process.

Governance, risk, and compliance (GRC) are major inhibitors for organizations moving to the cloud—and for good reason. Cloud environments are complex, and even a single misconfigured security group can result in a serious data breach. In fact, misconfigurations were the leading cause of cloud security breaches in 2020. This puts a lot of pressure on developer and operations teams to properly secure their services and maintain regulatory compliance.

Spinnaker is an open source multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence. It is a leading CI/CD tool and provides core feature sets for cluster and deployment management. Created by Netflix and Google, it combines a powerful and flexible pipeline management system with integrations to the major cloud providers. As Spinnaker developed new features and integrations, many of Spot by NetApp customers adopted it as part of their DevOps stack.

A lot of people think that using the cloud makes you elastic, but cloud really only gives you the potential to be elastic. If your apps aren’t elastic, and your cloud usage doesn’t flex up and down based on activity levels, then it’s really just expensive. The same is true of containers—they don’t magically make all of your hosting problems go away, and while they have the potential to make you more agile and efficient, it isn’t that simple.

Serverless applications, more often than not, have logic distributed over multiple functions and services, which with growth and agents and wrappers attached, can get more complex and costly. This is where Serverless monitoring comes in to help. But what is Serverless monitoring? Serverless monitoring allows developers to gain important insight on what happens during each execution and event, errors become more easily visible, and measuring resource consumption for each invocation is possible.

2020 has seen a surge in the number of organizations embracing remote work to adapt to the new normal, leading to increased reliance on digital collaborative workspaces like Microsoft Teams. As organizations migrate critical business processes to such digital workspaces, IT service management teams need to leverage these platforms as channels for delivering IT and enterprise support.

While Spot by Netapp allows organizations to reliably and affordably run production environments on spot instances, operation teams always want to be alerted about any events of interest that occur in their environment. Over the last 5 years, Spot by NetApp customers have been using our existing notification mechanism and have shared excellent feedback for various enhancements and new capabilities.

Another generic error message from our favorite FaaS provider AWS Lambda. And again, there are multiple reasons why this issue could arise. Let’s first look at the basics of AWS Lambda to get a better intuition for when things go wrong later. Lambda is an asynchronous event-based service at heart.

We’ve been monitoring 100,000’s of serverless backend components for 3+ years at Dashbird. In our experience, Serverless infrastructure failures boil down to: These isolated faults become causes of failure due to dependencies in our cloud architectures (ref. Difference of Fault vs. Failure). If a serverless Lambda function relies on a database that is under stress, the entire API may start returning 5XX errors.

At Taloflow we recently launched a way for companies migrating from AWS/GCP/Azure to 3rd party object storage providers like Storj to receive an objective TCO analysis of all the switching and storing costs associated. But what exactly is object storage - and how does it compare to block storage? For both, we will cover the technical description, benefits, and application use-cases.

When designing a highly available compute environment one needs to determine the infrastructure layout required for uninterrupted application runtime even in the event of component failure. One key factor to be considered when planning for this is the geographic locations of all relevant resources. One must maintain physical separation between different compute resources with identical roles so that if a single datacenter goes down, applications remain operational.

Between 2019 and 2020, more Australian businesses have turned to cloud computing technologies. A recent survey from the Australian Bureau of Statistics found that businesses using paid cloud computing — IT services used over the internet to access software, computing power, and storage capacity — jumped to 55% from 42% in 2017 to 2018. Indeed, cloud hosting technology is more popular now than ever before.

AWS is undoubtedly one of the best Cloud service providers to run serious business out there. Reliable and cost-effective. No doubt here. But something utterly wrong with AWS is the experience on their web management console. It is so bad that thousands of developers come using Qovery every month with the only promise of - a better developer experience on top of AWS. Here are the 7 reasons why Qovery is an excellent choice for SaaS startups.

Setting up Cloud Monitoring dashboards for your team can be time consuming because every team's needs are different. Picking the right metrics, using the right visualizations to represent these metrics, deciding what metrics can go on the same chart, and determining the right pre-processing steps for metrics requires background and experience that may not yet exist among your development and operations teams.

Security teams have the tough job of monitoring and securing every single workload in each cloud and for workloads in the development pipeline. Inevitably, these processes wind up being a bottleneck from the developer’s perspective, and developers get frustrated. Understandably, developers feel like security is simply making their jobs harder. But, on the other hand, security teams feel like they’re powerless to provide full coverage.

The rise of public cloud services has enabled businesses to innovate faster, scale effortlessly, and adopt more advanced technologies easier than ever before. However, there’s a dark side to using public cloud services: complexity and cost. Public cloud services can scale to handle almost any workload, but in doing so, they can quickly generate unpredictable costs for your business.

A digital twin is a virtual or digital replica of a physical entity. In the Industrial Internet of Things (IoT) realm, a digital twin is the dynamic virtual copy of a physical framework, a vehicle, a machine, or any device. Most often, a digital twin is developed to carry out simulations before the actual device is manufactured. Today, digital twin is taking the IoT industry by storm and the market is expected to hit $16 billion by 2023.