observability

Comparing New Relic’s New AI-Driven Digital Experience Monitoring Solution with Datadog

cloud monitoing, monitoring, network observability, observability

In the ever-evolving landscape of digital experience monitoring, two prominent players have emerged with innovative solutions: New Relic and Datadog. Both companies aim to enhance user experiences and optimize digital interactions, but they approach the challenge with different strategies and technologies. Let’s dive into what sets them apart.

New Relic’s AI-Driven Digital Experience Monitoring Solution

New Relic recently launched its fully-integrated, AI-driven Digital Experience Monitoring (DEM) solution, which promises to revolutionize how businesses monitor and improve their digital interactions. Here are some key features:

1. AI Integration: New Relic’s solution leverages artificial intelligence to provide real-time insights into user interactions across all applications, including AI applications. This helps identify incorrect AI responses and user friction points, ensuring a seamless user experience.
2. Comprehensive Monitoring: The platform offers end-to-end visibility, allowing businesses to monitor real user interactions and proactively resolve issues before they impact the end user.
3. User Behavior Analytics: By combining website performance monitoring, user behavior analytics, real user monitoring (RUM), session replay, and synthetic monitoring, New Relic provides a holistic view of the digital experience.
4. Proactive Issue Resolution: Real-time data on application performance and user interactions enable proactive identification and resolution of issues, moving from a reactive to a proactive approach.

Datadog’s Offerings

Datadog focuses on providing comprehensive monitoring solutions for infrastructure, applications, logs, and more. Here are some highlights:

1. Unified Monitoring: Datadog offers a unified platform that aggregates metrics and events across the entire DevOps stack, providing visibility into servers, clouds, applications, and more.
2. End-to-End User Experience Monitoring: Datadog provides tools for monitoring critical user journeys, capturing user interactions, and detecting performance issues with AI-powered, self-maintaining tests.
3. Scalability and Performance: Datadog’s solutions are designed to handle large-scale applications with high performance and low latency, ensuring that backend systems can support seamless digital experiences.
4. Security and Compliance: With enterprise-grade security features and compliance with industry standards, Datadog ensures that data is protected and managed securely.

Key Differences

While both New Relic and Datadog aim to enhance digital experiences, their approaches and focus areas differ significantly:

• Focus Area: New Relic is primarily focused on monitoring and improving the front-end user experience, while Datadog provides comprehensive monitoring across the entire stack, including infrastructure and applications.

• Technology: New Relic leverages AI to provide real-time insights and proactive issue resolution, whereas Datadog focuses on providing scalable and secure monitoring solutions.

• Integration: New Relic’s solution integrates various monitoring tools to provide a comprehensive view of the digital experience, while Datadog offers a unified platform that aggregates metrics and events across the full DevOps stack.

Conclusion

Both New Relic and Datadog offer valuable solutions for enhancing digital experiences, but they cater to different aspects of the digital ecosystem. New Relic’s AI-driven DEM solution is ideal for businesses looking to proactively monitor and improve user interactions, while Datadog’s robust monitoring offerings provide comprehensive visibility across infrastructure and applications. By leveraging the strengths of both platforms, businesses can ensure a seamless and optimized digital presence.

What do you think about these new offerings? Do you have a preference for one over the other?

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Network Monitoring for Cloud-Connected IoT Devices

cloud monitoing, IoT, monitoring, network observability, observability

One of the emerging trends in network monitoring is the integration of cloud computing and Internet of Things (IoT) devices. Cloud computing refers to the delivery of computing services over the internet, such as storage, processing, and software. IoT devices are physical objects that are connected to the internet and can communicate with other devices or systems. Examples of IoT devices include smart thermostats, wearable devices, and industrial sensors.

Cloud-connected IoT devices pose new challenges and opportunities for network monitoring. On one hand, cloud computing enables IoT devices to access scalable and flexible resources and services, such as data analytics and artificial intelligence. On the other hand, cloud computing introduces additional complexity and risk to the network, such as latency, bandwidth consumption, and security threats.

Therefore, network monitoring for cloud-connected IoT devices requires a comprehensive and proactive approach that can address the following aspects:

  • Visibility: Network monitoring should provide a clear and complete view of the network topology, status, and performance of all the devices and services involved in the cloud-IoT ecosystem. This includes not only the physical devices and connections, but also the virtual machines, containers, and microservices that run on the cloud platform. Network monitoring should also be able to detect and identify any anomalies or issues that may affect the network functionality or quality.
  • Scalability: Network monitoring should be able to handle the large volume and variety of data generated by cloud-connected IoT devices. This requires a scalable and distributed architecture that can collect, store, process, and analyze data from different sources and locations. Network monitoring should also leverage cloud-based technologies, such as big data analytics and machine learning, to extract meaningful insights and patterns from the data.
  • Security: Network monitoring should ensure the security and privacy of the network and its data. This involves implementing appropriate encryption, authentication, authorization, and auditing mechanisms to protect the data in transit and at rest. Network monitoring should also monitor and alert on any potential or actual security breaches or attacks that may compromise the network or its data.
  • Automation: Network monitoring should automate as much as possible the tasks and processes involved in network management. This includes using automation tools and scripts to configure, deploy, update, and troubleshoot network devices and services. Network monitoring should also use automation techniques, such as artificial intelligence and machine learning, to perform predictive analysis, anomaly detection, root cause analysis, and remediation actions.

Solutions for Network Monitoring for Cloud-Connected IoT Devices

There are many solutions available for network monitoring for cloud-connected IoT devices. Some of them are native to cloud platforms or specific IoT platforms, while others are third-party or open-source solutions. Some of them are specialized for certain aspects or layers of network monitoring, while others are comprehensive or integrated solutions. Some of them are:

  • Domotz: Domotz is a cloud-based network and endpoint monitoring platform that also provides system management functions. This service is capable of monitoring security cameras as well as network devices and endpoints. Domotz can monitor cloud-connected IoT devices using SNMP or TCP protocols. It can also integrate with various cloud platforms such as AWS, Azure, and GCP.
  • Splunk Industrial for IoT: Splunk Industrial for IoT is a solution that provides end-to-end visibility into industrial IoT systems.  Splunk Industrial for IoT can collect and analyze data from various sources such as sensors, gateways, and cloud services. Splunk Industrial for IoT can also provide dashboards, alerts, and insights into the performance, health, and security of cloud-connected IoT devices.
  • Datadog IoT Monitoring: Datadog IoT Monitoring is a solution that provides comprehensive observability for cloud-connected IoT devices. Datadog IoT Monitoring can collect and correlate metrics, logs, traces, and events from various sources such as sensors, gateways, cloud services. Datadog IoT Monitoring can also provide dashboards, alerts, and insights into the performance, health, and security of cloud-connected IoT devices.
  • Senseye PdM: Senseye PdM is a solution that provides predictive maintenance for industrial IoT systems. Senseye PdM can collect and analyze data from various sources such as sensors, gateways, and cloud services. Senseye PdM can also provide  dashboards, alerts, and insights into the condition, performance, and reliability of cloud-connected IoT devices.
  • SkySpark: SkySpark is a solution that provides analytics and automation for smart systems. SkySpark can collect and analyze data from various sources such as sensors, gateways, and cloud services. SkySpark can also provide dashboards, alerts, and insights into the performance, efficiency, and optimization of cloud-connected IoT devices.

Network monitoring for cloud-connected IoT devices is a vital and challenging task that requires a holistic and adaptive approach. Network monitoring can help to optimize the performance, reliability, and security of the network and its components. Network monitoring can also enable new capabilities and benefits for cloud-IoT applications, such as enhanced user experience, improved operational efficiency, and reduced costs.

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Containers and Kubernetes Observability Tools and Best Practices

cloud monitoing, containers, deep observability, kubernetes, monitoring, observability

Containers and Kubernetes are popular technologies for developing and deploying cloud-native applications. Containers are lightweight and portable units of software that can run on any platform. Kubernetes is an open-source platform that orchestrates and manages containerized workloads and services.

Containers and Kubernetes offer many benefits, such as scalability, performance, portability, and agility. However, they also introduce new challenges for observability. Observability is the ability to measure and understand the internal state of a system based on the external outputs. Observability helps developers and operators troubleshoot issues, optimize performance, ensure reliability, and improve user experience.

Observability in containers and Kubernetes involves collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the containerized applications and the Kubernetes clusters. These data and events include metrics, logs, traces, events, alerts, dashboards, and reports.

In this article, we will explore some of the tools and best practices for observability in containers and Kubernetes.

Tools for Observability in Containers and Kubernetes

There are many tools available for observability in containers and Kubernetes. Some of them are native to Kubernetes or specific container platforms, while others are third-party or open-source solutions. Some of them are specialized for certain aspects or layers of observability, while others are comprehensive or integrated solutions. Some of them are:

  • Kubernetes Dashboard: Kubernetes Dashboard is a web-based user interface that allows users to manage and monitor Kubernetes clusters and resources. It provides information such as cluster status, node health, pod logs, resource usage, network policies, and service discovery. It also allows users to create, update, delete, or scale Kubernetes resources using graphical or YAML editors.
  • Prometheus: Prometheus is an open-source monitoring system that collects and stores metrics from various sources using a pull model. It supports multi-dimensional data model, flexible query language, alerting rules, and visualization tools. Prometheus is widely used for monitoring Kubernetes clusters and applications, as it can scrape metrics from Kubernetes endpoints, pods, services, and nodes. It can also integrate with other tools such as Grafana, Alertmanager, Thanos, and others.
  • Grafana: Grafana is an open-source visualization and analytics platform that allows users to create dashboards and panels using data from various sources. Grafana can connect to Prometheus and other data sources to display metrics in various formats such as graphs, charts, tables, maps, and more. Grafana can also support alerting, annotations, variables, templates, and other advanced features. Grafana is commonly used for visualizing Kubernetes metrics and performance
  • EFK Stack: EFK Stack is a combination of three open-source tools: Elasticsearch, Fluentd, and Kibana. Elasticsearch is a distributed search and analytics engine that stores and indexes logs and other data. Fluentd is a data collector that collects
    and transforms logs and other data from various sources and sends them to Elasticsearch or other destinations. Kibana is a web-based user interface that allows users to explore and visualize data stored in Elasticsearch. EFK Stack is widely used for logging and observability in containers and Kubernetes as it can collect and analyze logs from containers pods, nodes, services, and other software.
  • Loki: Loki is an open-source logging system that is designed to be cost-effective and easy to operate. Loki is inspired by Prometheus and uses a similar data model and query language. Loki collects logs from various sources using Prometheus service discovery and labels. Loki stores logs in a compressed and indexed format that enables fast and efficient querying. Loki can integrate with Grafana to display logs alongside metrics

Best Practices for Observability in Containers and Kubernetes

Observability in containers and Kubernetes requires following some best practices to ensure effective, efficient, and secure observability Here are some of them:

  • Define observability goals and requirements: Before choosing or implementing any observability tools or solutions, it is important to define the observability goals and requirements for the containerized applications and the Kubernetes clusters These goals and requirements should align with the business objectives, the user expectations, the service level agreements (SLAs), and the compliance standards. They should also specify what data and events to collect, how to analyze them, how to alert on them, and how to visualize them.
  • Use standard formats and protocols: To ensure interoperability and compatibility among different observability tools and solutions, it is recommended to use standard formats and protocols for collecting, storing, and exchanging data and events. For example, use OpenMetrics for metrics, JSON for logs, OpenTelemetry for traces, CloudEvents for events. Containers and Kubernetes Observability Tools and Best Practices. These standards can help reduce complexity, overhead, and vendor lock-in in observability.
  • Leverage native Kubernetes features: Kubernetes provides some native features that can help with observability For example, use labels and annotations to add metadata to Kubernetes resources that can be used for filtering, grouping, or querying. Use readiness probes and liveness probes to check the health status of containers. Use resource requests and limits to specify the resource requirements of containers. Use horizontal pod autoscaler (HPA) or vertical pod autoscaler (VPA) to scale pods based on metrics. Use custom resource definitions (CRDs) or operators to extend the functionality of Kubernetes resources These features can help improve the visibility, control, and optimization of containers and Kubernetes clusters.

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How Cloud Monitoring Can Boost Your DevOps Success

cloud monitoing, cybersecurity, observability

DevOps is a culture and practice that aims to deliver high-quality software products and services faster and more efficiently. DevOps involves the collaboration and integration of various roles and functions, such as development, testing, operations, security, and more. DevOps also relies on various tools and processes, such as code repositories, build pipelines, testing frameworks, deployment tools, and more.

However, DevOps also poses some challenges and risks, such as ensuring the reliability, availability, performance, security, and cost-efficiency of the software products and services. This is especially true when the software products and services are deployed on the cloud, which offers scalability, flexibility, and convenience, but also introduces complexity, variability, and uncertainty.

This is where cloud monitoring comes in. Cloud monitoring is the process of collecting and analyzing data and information from cloud resources, such as servers, containers, applications, services, etc. Cloud monitoring can help DevOps teams to achieve their goals and overcome their challenges by providing them with insights and feedback on various aspects of their cloud-based software products and services.

In this blog post, we will explore how cloud monitoring can boost your DevOps success in four ways:

• Cloud monitoring enables proactive problem detection and resolution: Cloud monitoring can help you to detect and resolve problems before they affect your end-users or your business outcomes. By using cloud monitoring tools, you can collect and analyze various metrics and logs from your cloud resources, such as CPU, memory, disk, network, latency, errors, etc. You can also set up alerts and notifications to inform you of any anomalies or issues that may indicate a potential problem. This way, you can quickly identify the root cause of the problem and take corrective actions to fix it.

• Cloud monitoring facilitates performance optimization and cost efficiency: Cloud monitoring can help you to optimize the performance and scalability of your cloud-based software products and services by providing you with insights into resource utilization, load balancing, auto-scaling, etc. You can use cloud monitoring tools to measure and benchmark the performance of your cloud resources against your expectations and requirements. You can also use cloud monitoring tools to adjust and optimize your resource allocation and configuration to meet the changing demands and conditions of your end-users and your environment. Additionally, cloud monitoring can help you to reduce the cost of your cloud operations by providing you with visibility into resource consumption, billing, and budgeting. You can use cloud monitoring tools to track and analyze your cloud spending and usage patterns. You can also use cloud monitoring tools to set up limits and alerts to prevent overspending or underutilization of your cloud resources.

• Cloud monitoring supports continuous delivery and integration: Cloud monitoring can help you to achieve continuous delivery and integration of your cloud-based software products and services by providing you with feedback and validation throughout the development and deployment lifecycle. You can integrate cloud monitoring tools with other DevOps tools and processes, such as code repositories, build pipelines, testing frameworks, deployment tools, etc. You can use cloud monitoring tools to monitor the quality and functionality of your code changes as they are integrated into the main branch. You can use cloud monitoring tools to measure and benchmark the performance of your cloud resources against your expectations and requirements. You can also use cloud monitoring tools to adjust and optimize your resource allocation and configuration to meet the changing demands and conditions of your end-users and your environment. Additionally, cloud monitoring can help you to reduce the cost of your cloud operations by providing you with visibility into resource consumption, billing, and budgeting. You can use cloud monitoring tools to track and analyze your cloud spending and usage patterns. You can also use cloud monitoring tools to set up limits and alerts to prevent overspending or underutilization of your cloud resources.

• Cloud monitoring supports continuous delivery and integration: Cloud monitoring can help you to achieve continuous delivery and integration of your cloud-based software products and services by providing you with feedback and validation throughout the development and deployment lifecycle. You can integrate cloud monitoring tools with other DevOps tools and processes, such as code repositories, build pipelines, testing frameworks, deployment tools, etc. You can use cloud monitoring tools to monitor the quality and functionality of your code changes as they are integrated into the main branch. You can also use cloud monitoring tools to monitor the status and health of your deployments as they are rolled out to different environments or regions. This way, you can ensure that your software products and services are always in a deployable state and meet the quality standards and expectations of your end-users and your stakeholders.

• Cloud monitoring fosters collaboration and communication: Cloud monitoring can help you to improve collaboration

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Monitoring and Observability in the Oracle Cloud

cloud monitoing, monitoring, observability

Monitoring and observability are essential practices for ensuring the availability, performance, security, and cost-efficiency of cloud-based systems and applications. Monitoring and observability involve collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the cloud environment, such as metrics, logs, traces, and user experience.

Oracle Cloud provides a comprehensive set of tools and services for monitoring and observability of its cloud resources and services. Oracle Cloud also supports integration with third-party tools and standards for monitoring and observability of hybrid and multi-cloud environments.

(Image: Delphi, Greece)

In this article, we will discuss some of the benefits and challenges of monitoring and observability of Oracle Cloud.

Benefits of Monitoring and Observability of Oracle Cloud

Some of the benefits of monitoring and observability of Oracle Cloud are:

  • Visibility: Oracle Cloud provides visibility into the health, performance, usage, and cost of its cloud resources and services. Users can access metrics, logs, events, alerts, dashboards, reports, and analytics from the Oracle Cloud console or APIs. Users can also use Oracle Cloud Observability and Management Platform, which provides a unified view of the observability data across Oracle Cloud and other cloud or on-premises environments.
  • Control: Oracle Cloud provides control over the configuration, management, and optimization of its cloud resources and services. Users can use policies, rules, thresholds, actions, functions, notifications, and connectors to automate monitoring and observability tasks. Users can also use Oracle Cloud Resource Manager to deploy and manage cloud resources using Terraform-based automation.
  • Security: Oracle Cloud provides security for its cloud resources and services. Users can use encryption, access control, identity management, auditing, compliance, firewall, antivirus, vulnerability scanning, and incident response to protect their cloud data and assets. Users can also use Oracle Cloud Security Advisor to assess their security posture and receive recommendations for improvement.
  • Innovation: Oracle Cloud provides innovation for its cloud resources and services. Users can use artificial intelligence (AI), machine learning (ML), natural language processing (NLP), computer vision (CV), blockchain, chatbots, digital assistants, Internet of Things (IoT), edge computing, serverless computing, microservices, containers, and Kubernetes to enhance their cloud capabilities and outcomes. Users can also use Oracle Cloud Enterprise Manager to monitor, analyze, and administer Oracle Database and Engineered Systems

Challenges of Monitoring and Observability of Oracle Cloud

Some of the challenges of monitoring and observability of Oracle Cloud are:

  • Complexity: Oracle Cloud offers a wide range of services and features that can create complexity and confusion for users. Users need to understand and choose the appropriate tools and services for their monitoring and observability needs. Users also need to configure and manage the tools and services properly to avoid errors, misconfigurations, or inefficiencies
  • Integration: Oracle Cloud supports integration with third-party tools and standards for monitoring and observability. However, users need to ensure compatibility, interoperability, and security of the integration solutions. Users also need to deal with potential issues such as data duplication, inconsistency, or loss
  • Skills: Oracle Cloud requires users to have adequate skills and knowledge to use its tools and services for monitoring and observability. Users need to learn how to use the Oracle Cloud console, APIs, CLI, SDKs, and other interfaces. Users also need to learn how to use the Oracle Cloud Observability and Management Platform, Oracle Cloud Resource Manager, Oracle Cloud Security Advisor, Oracle Cloud Enterprise Manager, and other tools and services.

Monitoring and observability are essential practices for ensuring the availability, performance, security, and cost-efficiency of cloud-based systems and applications. Oracle Cloud provides a comprehensive set of tools and services for monitoring and observability of its cloud resources and services. Oracle Cloud also supports integration with third-party tools and standards for monitoring and observability of hybrid and multi-cloud environments.
However, monitoring and observability of Oracle Cloud also pose some challenges such as complexity, integration, and skills Users need to be aware of these challenges and address them accordingly to ensure effective, efficient, and secure monitoring and observability of Oracle Cloud.

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Review of AI Tools for Cloud Monitoring and Observability

cloud monitoing, observability

Cloud monitoring and observability are essential practices for ensuring the availability, performance, and security of cloud-based systems and applications. Cloud monitoring and observability involve collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the cloud environment, such as metrics, logs, traces, and user experience.

However, cloud monitoring and observability can also be challenging and complex, as cloud environments are dynamic, distributed, heterogeneous, and scalable. Traditional monitoring and observability tools may not be able to cope with the volume, velocity, variety, and veracity of cloud data and events. Moreover, human operators may not be able to process and act on the data and events in a timely and effective manner.

This is where artificial intelligence (AI) tools can help. AI tools can leverage machine learning (ML), natural language processing (NLP), computer vision (CV), and other techniques to enhance cloud monitoring and observability capabilities. AI tools can provide benefits such as:

  • Automated data collection and ingestion from various sources and formats
  • Intelligent data processing and analysis to identify patterns, anomalies, correlations, and causations
  • Actionable insights and recommendations to optimize performance, reliability, security, and cost
  • Automated remediation and resolution of issues using predefined or self-learning actions
  • Enhanced user interface and user experience using natural language or visual interactions

In this article, we will explore some of the AI tools that are used or can be used for cloud monitoring and observability. We will also review some of the features, benefits, and challenges of these tools.

Dynatrace

Dynatrace is a software intelligence platform that provides comprehensive observability for hybrid and multi-cloud ecosystems. Dynatrace uses AI to automate data collection and analysis, provide actionable answers to performance problems, optimize resource allocation, and deliver superior customer experience.

Some of the features of Dynatrace are:

  • Automatic discovery and instrumentation of all applications, containers, services, processes, and infrastructure
  • Real-time topology mapping that captures and unifies the dependencies between all observability data
  • Causation-based AI engine that automates root-cause analysis and provides precise answers
  • OpenTelemetry integration that extends the breadth of cloud observability
  • Scalability and efficiency that ensure complete observability even in highly dynamic environments

Some of the benefits of Dynatrace are:

  • Simplified procurement and management of cloud observability tools
  • Enhanced visibility and correlation across multiple sources and types of data
  • Improved scalability and performance of cloud observability solutions

Some of the challenges of Dynatrace are:

  • Reduced negotiating power and flexibility with vendors
  • Potential single points of failure or compromise in case of vendor breaches or outages
  • Increased dependency on vendor support or updates

IBM Observability by Instana APM

IBM Observability by Instana APM is a solution that provides end-to-end visibility into serverless applications on AWS Lambda. IBM Observability by Instana APM uses AI to collect metrics, logs, and traces from AWS Lambda functions, provide real-time dashboards, alerts, and insights into the performance, errors, costs, and dependencies of serverless applications.

Some of the features of IBM Observability by Instana APM are:

  • Agentless data ingestion that does not require any code changes or configuration
  • Domain-specific AI engine that enables data organization and analysis
  • High-cardinality view that allows filtering and slicing by any attribute or dimension
  • Distributed tracing that supports OpenTelemetry standards
  • Cost optimization that monitors usage and cost of serverless functions

Some of the benefits of IBM Observability by Instana APM are:

  • Easy deployment and integration with AWS Lambda
  • Comprehensive coverage and granularity of serverless data
  • Fast detection and resolution of serverless issues

Some of the challenges of IBM Observability by Instana APM are:

  • Limited support for other serverless platforms or providers
  • Dependency on AWS services for data storage or streaming
  • Potential data privacy or sovereignty issues

Elastic Observability

Elastic Observability is a solution that provides unified observability for hybrid and multi-cloud ecosystems,
including AWS, Azure, Google Cloud Platform, and more. Elastic Observability allows users to ingest telemetry data from various sources such as logs, metrics, traces, and uptime using Elastic Agents or Beats shippers It also provides powerful search, analysis, and visualization capabilities using Elasticsearch engine, Kibana dashboard, and Elastic APM service.

Some of the features of Elastic Observability are:

  • Agent-based or agentless data ingestion that supports various protocols, formats, and standards
  • Open source platform that allows customization, extension, and integration
  • Scalable architecture that can handle large volumes of data at high speed
  • Anomaly detection that uses ML to identify unusual patterns or behaviors
  • Alerting framework that supports multiple channels, actions, and integrations

Some of the benefits of Elastic Observability are:

  • Flexible deployment options on-premises, in the cloud, or as a service
  • Cost-effective pricing model based on resource consumption
  • Rich ecosystem of plugins, integrations, and community support

Some of the challenges of Elastic Observability are:

  • Complex installation and configuration process
  • High learning curve for users who are not familiar with Elasticsearch or Kibana
  • Potential security or compliance issues with open source software

Summary

AI tools can enhance cloud monitoring and observability capabilities by automating data collection and analysis, providing actionable insights and recommendations, and enabling automated remediation and resolution of issues. We have reviewed some of the AI tools that can be used for cloud monitoring and observability:

  • Dynatrace
  • IBM Observability by Instana APM
  • Elastic Observability

These tools have different features, benefits, and challenges that users should consider before choosing one.

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AWS vs Azure: Serverless Observability and Monitoring

cloud monitoing, monitoring, observability

Serverless computing is a cloud service model that allows developers to run code without provisioning or managing servers. Serverless applications are composed of functions that are triggered by events and run on demand. Serverless computing offers many benefits, such as scalability, performance, cost-efficiency, and agility.

However, serverless computing also introduces new challenges for observability and monitoring. Observability is the ability to measure and understand the internal state of a system based on the external outputs. Monitoring is the process of collecting, analyzing, and alerting on the metrics and logs that indicate the health and performance of a system.

Observability and monitoring are essential for serverless applications because they help developers troubleshoot issues, optimize performance, ensure reliability, and improve user experience. However, serverless applications are more complex and dynamic than traditional applications, making them harder to observe and monitor.

Some of the challenges of serverless observability and monitoring are:

  • Lack of visibility: Serverless functions are ephemeral and stateless, meaning they are created and destroyed on demand, and do not store any data or context. This makes it difficult to track the execution flow and dependencies of serverless functions across multiple services and platforms.
  • High cardinality: Serverless functions can have many variations based on input parameters, environment variables, configuration settings, and runtime versions. This creates a high cardinality of metrics and logs that need to be collected and analyzed.
  • Distributed tracing: Serverless functions can be triggered by various sources, such as HTTP requests, messages, events, timers, or other functions. This creates a distributed tracing problem, where developers need to correlate the traces of serverless functions across different sources and services.
  • Cold starts: Serverless functions can experience cold starts, which are delays in the execution time caused by the initialization of the function code and dependencies. Cold starts can affect the performance and availability of serverless applications, especially for latency-sensitive scenarios.
  • Cost optimization: Serverless functions are billed based on the number of invocations and the execution time. Therefore, developers need to monitor the usage and cost of serverless functions to optimize their resource allocation and avoid overspending.

AWS and Azure are two of the leading cloud providers that offer serverless computing services. AWS Lambda is the serverless platform of AWS, while Azure Functions is the serverless platform of Azure. Both platforms provide observability and monitoring features for serverless applications, but they also have some differences and limitations.

In this article, we will compare AWS Lambda and Azure Functions in terms of their observability and monitoring capabilities, including their native features and third-party software reviews and recommendations.

Native Features

Both AWS Lambda and Azure Functions provide native features for observability and monitoring serverless applications. These features include:

  • Metrics: Both platforms collect and display metrics such as invocations, errors, duration, memory usage, concurrency, and throughput for serverless functions. These metrics can be viewed on dashboards or queried using APIs or CLI tools. Metrics can also be used to create alarms or alerts based on predefined thresholds or anomalies.
  • Logs: Both platforms capture and store logs for serverless functions. These logs include information such as start and end time, request ID, status code, error messages, custom print statements, etc. Logs can be viewed on consoles or queried using APIs or CLI tools. Logs can also be streamed or exported to external services for further analysis or retention.
  • Tracing: Both platforms support distributed tracing for serverless functions. Distributed tracing allows developers to track the execution flow and latency
    of serverless functions across different sources and services. Tracing can help identify bottlenecks errors, failures or performance issues in serverless applications.

Both platforms use open standards such as OpenTelemetry or W3C Trace Context for tracing. However, there are also some differences between AWS Lambda and Azure Functions in terms of their native features for observability and monitoring.

Some of these differences are:

  • Metrics granularity: AWS Lambda provides metrics at a 1-minute granularity by default while Azure Functions provides metrics at a 5-minute granularity by default
    However, both platforms allow users to change the granularity to a lower or higher level depending on their needs
  • Metrics aggregation: AWS Lambda aggregates metrics by function name function version or alias (if specified), region (if specified) or globally (across all regions). Azure Functions aggregates metrics by the function name (or function app name), region (if specified) or globally (across all regions).
  • Logs format: AWS Lambda logs are formatted as plain text with a timestamp prefix. Azure Functions logs are formatted as JSON objects with various fields such as timestamp, level, message, category, functionName, invocationId, etc.
  • Logs retention: AWS Lambda logs are stored in Amazon CloudWatch Logs service for 90 days by default (or longer if specified by users). Azure Functions logs are stored in Azure Monitor service for 30 days by default (or longer if specified by users)
  • Tracing integration: AWS Lambda integrates with AWS X-Ray service for tracing. AWS X-Ray provides a web console and an API for viewing traces and analyzing the performance of serverless applications on AWS. Azure Functions integrates with Azure Application Insights service for tracing. Azure Application Insights provides a web console and an API for viewing traces and analyzing the performance of serverless applications on Azure.

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Cloud Native Security: Cloud Native Application Protection Platforms

cloud monitoing, cybersecurity, monitoring, network observability, observability

Back in 2022, 77% of interviewed CIOs stated that their IT environment is constantly changing. We can only guess that this number, would the respondents be asked today, will be as high as 90%+. Detecting flaws and security vulnerabilities becomes more and more challenging in 2023 since the complexity of typical software deployment is exponentially increasing year to year. The relatively new trend of Cloud Native Application Protection Platforms (CNAPP) is now supported by the majority of cybersecurity companies, offering their CNAPP solutions for cloud and on-prem deployments.

CNAPP rapid growth is driven by cybersecurity threats, while misconfiguration is one of the most reported reasons for security breaches and data loss. While workloads and data move to the cloud, the required skill sets of IT and DevOps teams must also become much more specialized. The likelihood of an unintentional misconfiguration is increased because the majority of seasoned IT workers still have more expertise and got more training on-prem than in the cloud. In contrast, a young “cloud-native” DevOps professional has very little knowledge of “traditional” security like network segmentation or firewall configuration, which will typically result in configuration errors.

Some CNAPP are proud to be “Agentless” eliminating the need to install and manage agents that can cause various issues, from machine’ overload to agent vulnerabilities due to security flows and, guess what, due to the agent’s misconfiguration. Agentless monitoring has its benefits but it is not free of risks. Any monitored device should be “open” for such monitoring, typically coming from a remote server. If an adversary was able to fake a monitoring attempt, he can easily get access to all the monitored devices and compromise the entire network. So “agentless CNAPP” does not automatically mean a better solution than a competing security platform. Easier for maintenance by IT staff? Yes, it is. Is it more secure? Probably not.

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Cloud Monitoring Market Size Estimations

cloud monitoing, cybersecurity, monitoring, network observability, observability

According to a marketing study, the global IT infrastructure monitoring market is supposed to grow at 13.6% CAGR reaching USD $64.5 in 2031. Modern IT infrastructure becomes increasingly more complex and requires new skills from IT personnel, often blurring the borders between IT staff, DevOps, and development teams. With the continued move from on-prem deployments to the enterprise cloud, IT infrastructure goes to the cloud as well, and thus IT teams have to learn basic cloud-DevOps skills, such as scripting, cloud-based scaling, events creation, and monitoring. Furthermore, no company today offers a complete monitoring solution that can monitor any network device and software component.

Thus, IT teams have to build their monitoring solutions piece by piece, using various mostly not interconnected systems, developed by different, often competing vendors. For some organizations, it also comes to compliance, such as GDPR or ISO requirements, and to SLAs that obligate the IT department to timely detect, report, and fix any issue with their systems. In this challenging multi-system and multi-device environment, network observability becomes the key to enterprise success. IT organizations keep increasing their budgets seeking to reach the comprehensive cloud and on-prem monitoring for their systems and devices, and force the employees to run network and device monitoring software on their personal devices, such as mobile phones and laptops. This trend also increases the IT spend on cybersecurity solutions such as SDR and network security analysis with various SIEM tools.

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Digital Experience Journal: Top 20 Vendors for Managing IT Performance in 2022

monitoring, observability

Digital Enterprise Journal’s recently published analysis of IT performance markets, 24 Key Areas Shaping IT Performance Markets in 2022. Designed to help end-user organizations understand what solution is the best fit for their specific needs, it provides an in-depth analysis of which vendors, including Catchpoint, align with key user requirements for managing IT performance in relation to this year’s key trends.

Catchpoint is proud of customers like Equinix, SAP and Cox Automotive, who tell their success stores with the company’s products:

Kelsey Waters, Senior Director of Cloud Operations, Equinix:

Catchpoint gives Equinix a more complete picture of internet visibility into what’s going on in the network, and that helps the company solve problems more quickly and communicate problems with clarity for customers. With Catchpoint, Equinix is able to identify and diagnose issues in a matter of minutes and begin to correct them before they become larger problems for end users.

Equinix is a leader in the digital infrastructure space, providing a platform that guarantees flexibility, scalability, and security. Top-tier enterprises, software as a service (SaaS), and cloud providers rely on Equinix to deliver services and expect no compromise when it comes to digital performance.

Equinix is a neutral co-location and data center provider. “The fundamental idea of Equinix was to create a place where competitive networks could come together and share data in a secure way,” explains Kelsey Waters of Cloud Operations. Equinix includes its subset, Equinix Metal. Equinix Metal provides bare metal services in a consumption-based model, similar to public clouds but in a bare metal fashion (Catchpoint is itself a customer).

Digital performance is crucial to Equinix, as they help customers scale businesses with agility and ease, without worrying about critical infrastructure. With more than 220 data centers in over 26 locations worldwide, Equinix strives to maintain 99.9% uptime. Equinix partnered with Catchpoint to:

  • Ensure service reliability.
  • Offer customers insights into observability and performance trends.
  • Maintain consistent availability and reachability.
  • Provide a full picture of the internet.

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To ensure customers provide the best end-user experience, Equinix services must consistently run at peak levels. That’s why they invested in Catchpoint’s end user observability solution to stay ahead of any network-impacting incident

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