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Description
Houseware provides an additional dimension of revenue insight for your data warehouse. By linking your current data infrastructure or various SaaS applications, you can access ready-made metrics and entities. You have the flexibility to create user segments and cohorts while selecting from over 30 predefined blocks and templates to unlock revenue potential. Team collaboration allows you to effortlessly distribute a well-balanced mix of personalized campaigns, internal alerts, and notifications, all without the need for coding. Our team, composed of athletes, challenges the typical startup analogy that equates the journey to a marathon. Instead, we prepare for a marathon with the understanding that we will face multiple sprints along the way, each sprint feeling like a marathon in itself. This unique perspective encourages resilience and adaptability in our approach to growth and innovation.
Description
Marathon serves as a robust container orchestration platform that integrates seamlessly with Mesosphere’s Datacenter Operating System (DC/OS) and Apache Mesos, ensuring high availability through its active/passive clustering and leader election mechanism, which guarantees continuous uptime. It supports multiple container runtimes, offering first-class integration for Mesos containers utilizing cgroups as well as Docker, making it adaptable to various development environments. Additionally, Marathon facilitates the deployment of stateful applications by allowing persistent storage volumes to be linked to your apps, which is particularly beneficial for running databases such as MySQL and Postgres with storage managed by Mesos. The platform boasts an intuitive and powerful user interface, along with a range of service discovery and load balancing options to suit diverse needs. Health checks are implemented to monitor application performance via HTTP or TCP checks, ensuring reliability. Users can also set up event subscriptions by providing an HTTP endpoint to receive notifications, which can aid in integrating with external load balancers. Lastly, metrics can be queried in JSON format at the /metrics endpoint, while also being capable of integration with popular systems like Graphite, StatsD, DataDog, or scraped using Prometheus, allowing for comprehensive monitoring and analysis of application performance. This combination of features positions Marathon as a versatile tool for managing containerized applications effectively.
API Access
Has API
API Access
Has API
Integrations
Apache Mesos
D2iQ
Datadog
Docker
Gainsight
Google Analytics
Graphite Studio
HubSpot CRM
HubSpot Customer Platform
Intercom
Integrations
Apache Mesos
D2iQ
Datadog
Docker
Gainsight
Google Analytics
Graphite Studio
HubSpot CRM
HubSpot Customer Platform
Intercom
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Houseware
Founded
2021
Country
United States
Website
www.houseware.io
Vendor Details
Company Name
D2iQ
Founded
2013
Country
United States
Website
mesosphere.github.io/marathon/
Product Features
Revenue Intelligence
Actionable Insights
Alerts / Notifications
CRM Interactions
Call Scoring
Conversation Intelligence
Dashboard
Email/Message Interactions
Market Intelligence
Phone Call Interactions
Pipeline Visibility
Sales Coaching
Video Call Interactions
Revenue Operations
AI Insights
Account Health Dashboard
Automatic CRM Updates
Contact / Activity Tracking
RevOps Automation
Revenue Dashboard
Revenue Intelligence / Reporting
Sales Analytics
Sales Forecasting
Third-Party Data Aggregation