Akamai mPulse vs Amazon OpenSearch Service comparison

Akamai mPulse is a real user monitoring (RUM) solution that gives performance engineers, administrators, and developers the ability to effortlessly visualize website functionality issues and identify ways to improve processes that conventional testing protocols do not find. mPulse gives users usable scenarios to better understand how processes such as user interactions, visual progress, and third-party resources may be disrupting the overall user experience and application delivery.

mPulse enables users to take a deep dive into the specific performance issues and complete comprehensive error analyses, to thoroughly understand the effect on critical user interactions such as conversions, page views, and more.

mPulse gathers and delivers data on an organization's website’s performance and metrics on user web browsing experiences. The mPulse feature “Boomerang” is a JavaScript Library that monitors the website page load time. Boomerang has a unique plugin architecture and works with all websites. The Boomerang feature is embedded on each page of an organization's website.

mPulse works seamlessly with Akamai solution Ion, so the RUM data can be instantly gathered once the Luna Control Center has been activated. Ion instantly attaches Boomerang to the organization’s web properties; there is no need to change the website code.

Akamai mPulse Benefits

• Third-party monitoring: mPulse enables users to effortlessly monitor and visualize the effect that third-party vendors, resources, and scripts may have on the organization’s web properties.
  
  
• Real-time intuitions: RUM data is continually being gathered, so users are able to instantly experience changes in user performance during every critical event. Users are able to respond immediately and make corrections or amendments as needed. Additionally, mPulse integrates effectively with other third-party notification solutions, such as Slack, PagerDuty, and webhook API support.
  
  
• Framework integrations: mPulse integrates with many of today’s popular single-page application (SPA) frameworks, such as React.js, Backbone.js, Angular.js, Ember.js, and even some custom frameworks. mPulse has the ability to also be seamlessly integrated in Non-SPA websites.
  
  
• Intuitive feedback: mPulse gathers data and creates workable solutions so users have a better understanding of how objects, images, or even entire pages are affecting user behavior. Users can gather numerous metrics (custom or advanced) to achieve a better, more complete understanding of ways to improve the user experience with the application or website.
  
  
• Custom metrics and timers: With mPulse, users can effortlessly create custom timers to discover important performance sessions unique to the application. The mPulse dashboard allows users to set up trackers for numerous metrics, such as:
  
  
  • First image load time
  • Sidebar load time
  • Page load times
  • Third-party resource content load time
    
    
• mPulse Beacon API: This valuable benefit allows users to send custom metrics from mobile and web applications to mPulse. This gives mPulse greater flexibility and usability to frameworks and platforms that can make HTTP calls. Representational State Transfer (REST) interface is included and is able to be used by any web application using any language running on the platform. There is also a Beacon API library for JavaScript. This option is available from the dashboard by creating a new application and an API key.

Amazon OpenSearch Service provides scalable and reliable search capabilities with efficient data processing, supporting easy domain configuration and integration with numerous systems for enhanced performance.

Amazon OpenSearch Service offers advanced features for handling JSON, diverse search grammars, quick historical data retrieval, and ultra-warm storage. It also includes customizable dashboards and seamless tool integration for large enterprises. With its managed infrastructure, OpenSearch Service supports efficient system analysis and business analytics, improving overall performance and flexibility. Despite these features, areas like configuration complexity, lack of auto-scaling, and integration with Kibana require attention. Users seek enhanced documentation, better pricing options, and more flexible data handling. Desired improvements include default filters, mapping configuration, and alerting capabilities. Enhanced data visualization and Compute Optimizer Service integration are also recommended for future updates.

• Scalable Search: Offers reliable search capabilities across vast datasets.
• JSON Handling: Supports efficient management of JSON data.
• Ultra-warm Storage: Enables cost-effective historical data retrieval.
• Dashboard Customization: Supports tool integration and tailored visuals.
• System Upgrades: Allows for seamless infrastructure upgrades.

• Performance Enhancement: Significant improvement in search and data retrieval.
• Cost-Effectiveness: Provides a cloud-native solution optimized for AWS environments.
• Comprehensive Analytics: Delivers detailed insights for business applications.

Amazon OpenSearch Service is utilized in various industries for log management, data storage, and search capabilities. It supports infrastructure and embedded management, analyzing logs from AWS Lambda, Kubernetes, and other services. Companies use it for application debugging, monitoring security and performance, and customer behavior analysis, integrating it with tools like DynamoDB and Snowflake for a cost-effective solution.