This has improved as SL1 offers deeper levels of monitoring and also easier management of the platform (Ease of management is important as changes are more likely to get done. If I make an improvement to a monitoring method, I can easily apply that update to all of those monitored devices across multiple customers).
The Business Service views mean that I can easily create views into the monitoring data that are applicable to different levels within an organization. For example, an overview of business services for director level and a lower level performance view of assets for engineer level of employee.
When moving to ScienceLogic we gained a deeper insight into our infrastructure, its functionality and health alongside providing us with a more in-depth dashboarding tool-set.
We are also hoping with the move to service orientated dashboarding this will provide us with better visibility of service functionality, the impact a service issue has on other services, and also allow us to provide the business with dashboards to be able to view their service health.
We see it has significant value to the question here, however, we have not had time to quantify and evaluate.
The ScienceLogic SL1 platform aims to enable companies to digitally transform themselves by removing the difficulty of managing complex, distributed IT services. SL1 uses patented discovery techniques to find everything in a network, so users get visibility across all technologies and vendors running anywhere in data centers or clouds. The vendor states the advantage of SL1 is that it collects and analyzes millions of data points across an IT universe (made up of infrastructure, network, applications, and business services), to help users make sense of it all, share data, and automate IT processes.
With SL1, the user can:
- See everything across cloud and distributed architectures. Discover all IT components—–across physical, virtual, and cloud. Collect, merge, and store a variety of data in a clean, normalized data lake.
- Contextualize data through relationship mapping and machine learning (ML) for actionable insights. Use this context to understand the impact of infrastructure and applications on business service health and risk, accelerate root cause analysis, and execute recommended actions.
- Act on data that is shared across technologies and IT ecosystem in real time. Apply multi-directional integrations to automate workflows at cloud scale.
- Cisco HyperFlex
- New Relic
- Cloud -AWS
- Google Cloud
- IBM Cloud
- Cloud Services – Amazon EKS
- Fargate; Azure AKS; etc.
- Containers – Docker
- Software-defined Networks/WAN – Cisco
- Network - Cisco
- Storage - Dell EMC
- Pure Storage
- Hypervisors – VMware
- Operating Systems - Unix
- Business Applications
- Databases - Microsoft
- Office 365
- MS SQL Server
- IBM DB2
- APM - AppDynamics
- Storage - Dell EMC
- Cloud -AWS
- Applications -Microsoft
- Compute -VMWare
- Microsoft Hyper-V
- Converged -Nutanix
- Unified Communications and video - Cisco
|Small Businesses (1-50 employees)||0%|
|Mid-Size Companies (51-500 employees)||0%|
|Enterprises (more than 500 employees)||100%|
|Deployment Types||On-premise, SaaS|
|Operating Systems||Windows, Linux, Mac, UNIX|
|Supported Countries||Americas, EMEA, APAC|