Difference between revisions of "LiveDC Overview"
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| − | + | At the core of LiveDC, is the aggregation of data. In order to do this, LiveDC utilizes four components for execution: 1) Elicit; 2) templates; 3) tags; and 4) data series. Elicit is our data collector that resides on your network. It is used to capture all of the critical data that will be analyzed using the LiveDC platform. | |
| − | At the core of LiveDC, is the aggregation of data. In order to do this, LiveDC utilizes four components for execution: 1) Elicit; 2) | + | |
| − | As data is collected, it is organized and polled via instructions provided by various | + | As data is collected, it is organized and polled via instructions provided by various “templates” within the platform. These templates provide the key instructions for which data is sought, frequency of data requests, and format of data after it enters the platform. Templates help formulate standardized re-usable polling frequency / timing, layers for maps, influences, numbers, trends, URL Scrapes and SNMP collection. Templates are designed to help speed up the creation of data series and eliminate the possibility of errors. This ensures that all of the data collected is formatted appropriately to meet your needs for data analysis. |
| − | Identified, captured and tagged data in LiveDC | + | Identified, captured and tagged data in LiveDC is used by “data series” to make sense of all this disparate information. As the name implies, a data series is all the data values tracked over time, thus a series of data. The power of LiveDC comes from the aggregation of data series from all types of sources such as temperature, space, power, humidity, utilization, or manual entries. Each data series can also be combined with other data series to create higher order data series also known as a "dynamic set" for greater analysis and big data manipulation. Think of each data series as a brick in building the ultimate block of knowledge of your environment. Once data series are identified and created, LiveDC utilizes them to automatically gather the data and view the attributes through a number of mechanisms such as Maps, Graphs, and LiveTiles. Data to be utilized by LiveDC can be collected from a number of sources including, but not limited to: SNMP, HTTP, CSV, and Excel. Once the various data series are created, they can be utilized over and over for the creation of multiple LiveTiles, graphs, maps or customized analysis. |
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| − | As you go through the setup process, you will quickly see the constant use of | + | Leveraging tags helps make it easier to aggregate the various individual data series for new grouped data series. Tags can be set up in the beginning or added on the fly as you determine new needs or requirements based on business demand. As you will see, the quantities of data elements can grow very large depending on your needs. To assist in the use of all of this data, we have created the ability to tag data series with common names to help with capturing the right data series for the creation of dynamic sets. Tags are short labels that are easy to remember and can be easily combined with other tags to identify the various data series needed in your analysis. This provides you the power to easily search, poll, and create notifications, alerts, graphs, and LiveTiles, utilizing tags. We have built a foundational dictionary to help in the initial setup and creation of these tags. As you go through the variations of data series and their related polling types, keep in mind some of the ways you want to use the data so that a solid foundation can be set up. Every environment is unique, thus the requirement of client specific data series. To generically create data series would only help in the short term. By engaging the user to go through the data series setup process, the real power of LiveDC is realized. To assist in the basics, we have provided templates for data series to utilize so you can quickly edit and setup your environment. |
| + | |||
| + | As you go through the setup process, you will quickly see the constant use of data series to help capture the real-time information you desire. Naming data series with clear and concise terms will be very important to help realize the full value LiveDC provides. | ||
| + | |||
| + | ==About this Manual== | ||
| + | The purpose of this user manual is to provide guidance on using LiveDC. It is our assumption that the users of LiveDC have little to no knowledge of the platform. | ||
| + | |||
| + | The recommended browsers for LiveDC are: Google Chrome, Mozilla Firefox or Safari. Internet Explorer (IE) 9, 10 and 11 are not recommended but will allow to complete most tasks within the platform. If you are using IE we recommend you install the add-on “Chrome Frame”, which will allow IE to interpret the page similar to the way Google Chrome does. | ||
| + | |||
| + | The documentation / steps outlined in this wiki were written using Google Chrome. | ||
| + | |||
| + | You can obtain more information about LiveDC by emailing [mailto:info@itascapoint.com info@itascapoint.com]. | ||
Latest revision as of 17:16, 23 April 2019
At the core of LiveDC, is the aggregation of data. In order to do this, LiveDC utilizes four components for execution: 1) Elicit; 2) templates; 3) tags; and 4) data series. Elicit is our data collector that resides on your network. It is used to capture all of the critical data that will be analyzed using the LiveDC platform.
As data is collected, it is organized and polled via instructions provided by various “templates” within the platform. These templates provide the key instructions for which data is sought, frequency of data requests, and format of data after it enters the platform. Templates help formulate standardized re-usable polling frequency / timing, layers for maps, influences, numbers, trends, URL Scrapes and SNMP collection. Templates are designed to help speed up the creation of data series and eliminate the possibility of errors. This ensures that all of the data collected is formatted appropriately to meet your needs for data analysis.
Identified, captured and tagged data in LiveDC is used by “data series” to make sense of all this disparate information. As the name implies, a data series is all the data values tracked over time, thus a series of data. The power of LiveDC comes from the aggregation of data series from all types of sources such as temperature, space, power, humidity, utilization, or manual entries. Each data series can also be combined with other data series to create higher order data series also known as a "dynamic set" for greater analysis and big data manipulation. Think of each data series as a brick in building the ultimate block of knowledge of your environment. Once data series are identified and created, LiveDC utilizes them to automatically gather the data and view the attributes through a number of mechanisms such as Maps, Graphs, and LiveTiles. Data to be utilized by LiveDC can be collected from a number of sources including, but not limited to: SNMP, HTTP, CSV, and Excel. Once the various data series are created, they can be utilized over and over for the creation of multiple LiveTiles, graphs, maps or customized analysis.
Leveraging tags helps make it easier to aggregate the various individual data series for new grouped data series. Tags can be set up in the beginning or added on the fly as you determine new needs or requirements based on business demand. As you will see, the quantities of data elements can grow very large depending on your needs. To assist in the use of all of this data, we have created the ability to tag data series with common names to help with capturing the right data series for the creation of dynamic sets. Tags are short labels that are easy to remember and can be easily combined with other tags to identify the various data series needed in your analysis. This provides you the power to easily search, poll, and create notifications, alerts, graphs, and LiveTiles, utilizing tags. We have built a foundational dictionary to help in the initial setup and creation of these tags. As you go through the variations of data series and their related polling types, keep in mind some of the ways you want to use the data so that a solid foundation can be set up. Every environment is unique, thus the requirement of client specific data series. To generically create data series would only help in the short term. By engaging the user to go through the data series setup process, the real power of LiveDC is realized. To assist in the basics, we have provided templates for data series to utilize so you can quickly edit and setup your environment.
As you go through the setup process, you will quickly see the constant use of data series to help capture the real-time information you desire. Naming data series with clear and concise terms will be very important to help realize the full value LiveDC provides.
About this Manual
The purpose of this user manual is to provide guidance on using LiveDC. It is our assumption that the users of LiveDC have little to no knowledge of the platform.
The recommended browsers for LiveDC are: Google Chrome, Mozilla Firefox or Safari. Internet Explorer (IE) 9, 10 and 11 are not recommended but will allow to complete most tasks within the platform. If you are using IE we recommend you install the add-on “Chrome Frame”, which will allow IE to interpret the page similar to the way Google Chrome does.
The documentation / steps outlined in this wiki were written using Google Chrome.
You can obtain more information about LiveDC by emailing info@itascapoint.com.
