We use ScienceLogic across the organization to monitor both our infrastructure and the health endpoints of services, and have found it …
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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|
Frequently Asked Questions
We have many routers at work.
Almost all of them have 10K interfaces on each one.
Creating dashboards which present many different views of many different, large routers can create undue database load. (We're actively discouraging their use as a long-running process because each dashboard widget gets its own database connection, not a shared database connection.) We will kill all long running queries in the database if they run longer than 30 minutes.
The database schema design makes interesting trade-off decisions (optimizing for ease of write at the expense of complex / cross-device reads).
The new GraphQL schema model looks promising, but we're still waiting for that to begin to appear in the SL1 core functionality.
Given enough hardware and budget, you can go a long ways before you start pushing the boundaries of the platform architecture.
Being conservative in what you monitor for the vast majority of your CIs allows you to retain compute capacity for dashboards, runbook engines and the like.
Having large routers or vCenters will require an upscaling of your data collector sizings just to complete the Device Component Map data collection.