Skylar Analytics includes Data Visualization, Data Exploration, Anomaly Detection, and Predictive Alerting. This manual will explain all of these components, and how to use them.
For an overview of Skylar AI, see Introduction to Skylar AI.
What is Skylar Analytics?
The Skylar Analytics suite of services uses data gathered by Skylar One to explore data, generate visualizations, and monitor IT infrastructure metrics. Skylar Analytics can also use Skylar AI to predict alerts before they happen, and detect anomalies that could become events that might disrupt your IT infrastructure and functionality.
Skylar One uses port 443 to communicate with your Skylar Analytics system. Skylar AI does not require a port.
Skylar Analytics includes the following components:
- Data Visualization. Enables SQL-based dashboards and charts based on data gathered by Skylar AI and Skylar One. Data Visualization is achieved using a ScienceLogic-hosted instance of Apache Superset.
- Data Exploration. Enables third-party tools that use the Open Database Connectivity (ODBC) interface to access the metric data from Skylar AI. This component lets you use ODBC to connect Skylar AI data with applications like Tableau, Microsoft Power BI, or other business intelligence tools.
- Anomaly Detection. Uses always-on anomaly detection to find metric outliers in Dynamic Application time series data. It also computes an anomaly score that characterizes the significance of each anomaly. You can view anomalies for all Dynamic Application metrics for a device by visiting the tab on the Device Investigator page for that device.
- Predictive Alerting. Helps to avoid problems such as file systems running out of space. The alerts appear as enriched events within Skylar One.
The other chapters in this manual cover each Skylar Analytics component in detail.
Mapping Skylar One Dynamic Application Object Names to Skylar AI Columns
When data from Skylar One Dynamic Applications is exported to Skylar AI, the names of collection and presentation objects are automatically converted into clean, standardized column names for the Skylar data lake. The following rules ensure that all Skylar column names are consistent, machine-friendly, and easy to work with. If you are not sure how a name will be converted, use these common word replacements and clean-up rules as a guide.
The conversion process follows several steps:
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Standardize Special Characters
- If a letter is followed by a non-word character and an "a", replace it with the letter plus "A". This format ensures that column names are valid and avoid special symbols.
- Example: ba$ → bA
-
Replace Common Words
Certain words are automatically shortened to standard abbreviations. The following table contains the most common abbreviations:
Original Word Becomes ScienceLogic SL Microsoft MS Server Svr Database DB FileSystem FS Interface IF Resource Rsrc Worker Wrkr Service Svc Relationship Relnship Total Ttl Interval Ival Baseboard Basebrd Num Of Num Distribution Distro Level Lvl Hardware HW Software SW Default Dflt Namespace Nspc Virtual Machine VM Kilobytes KB Megabytes MB Gigabytes GB Terabytes TB Backup Bkup Successful Good Expiration Expiry Manufacturer Mfgr Device Dvc Sockets Socks Command Cmd VMware Open Open Processor Procssr Processes Procs -
Shorten Common Technical Terms
Some longer technical words are shortened to their first few letters. Examples:
- Physical → P
- Utilization → U
- Capacity → C
- Configuration → C
- Discovery → D
- Storage → S
- Limit → L
- Network → N
- Address → Addr
Only the beginning of the word is kept for these cases.
-
Clean Up the Name
- Remove all non-alphanumeric characters, such as spaces, slashes, parentheses.
- Replace common terms:
- Average → Avg
- QueueLength → QLen
- slSl → SL
- SL1Skylar → SL1Sky
- Exporter → Exptr
- Receiver → Rcvr
-
Add Unit, if Applicable
- If the original name included a unit, like MB, GB, %, and so on, add it at the end after an underscore.
- Format: columnname_unit
- Example: MemoryUtilization (Gigabytes) → MemU_GB