What is the difference between an historian and a data warehouse? [closed]

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I am working on a project to implement an historian. I can't really find a difference between an historian and a data warehouse.

Any details would be useful.

Answer 1

Data Historian

Data historians are groups of tables within a database that store historical information about a process or information system.

Data historians are used to keep historical data regarding a manufacturing system. This data can be changes in state of a data point, current values and summary data for these points. Usually this data comes from automated systems like PLCs, DCS or other process controlling system. However some historian data can be human entered.

There are several historians available for commercial use. However, one of the most common historians have tended to be custom developed. The commercial versions would be products like OsiSoft’s PI or GE’s Data Historian.

Some examples of data that could be stored in a data historian are items (or tags) like: - Total products manufactured for the day -Total defects created on a particular crew shift -Current temperature of a motor on the production line -Set point for the maximum allowable value being monitored by another tag -Current speed of a conveyor -Maximum flow rate of a pump over a period of time -Human entered marker showing a manual event occured -Total amount of a chemical added to a tank

These items are some of the important data tags that might be captured. However, once captured the next step is in presentation or reporting of that data. This is where the work of analysis is of great importance. The data/time stamp of one tag can have a huge correlation to another/other tag(s). Carefully storing this in the historians’ database is critical to good reporting.

The retrieval of data stored in a data historian is the slowest part of the system to be implemented. Many companies do a great job of putting data into a historian, but then do not go back and retrieve any of the data. Many times this author has gone into a site that claims to have a historian only to find that the data is “in there somewhere”, but has never had a report run against the data to validate the accuracy of the data.

The rule-of-thumb should be to provide feedback on any of the tags entered as soon as possible after storage into the historian. Reporting on the first few entries of a newly added tag is important, but ongoing review is important too. Once the data is incorporated into both a detailed listing and a summarized list the data can be reviewed for accuracy by operations personnel on a regular basis.

This regular review process by the operational personnel is very important. The finest data gathering systems that might historically archive millions of data points will be of little value to anyone if the data is not reviewed for accuracy by those that are experts in that information.

Data Warehouse

Data warehousing combines data from multiple, usually varied, sources into one comprehensive and easily manipulated database. Different methods can then be used by a company or organization to access this data for a wide range of purposes. Analysis can be performed to determine trends over time and to create plans based on this information. Smaller companies often use more limited formats to analyze more precise or smaller data sets, though warehousing can also utilize these methods.

Accessing Data Through Warehousing

Common methods for accessing systems of data warehousing include queries, reporting, and analysis. Because warehousing creates one database, the number of sources can be nearly limitless, provided the system can handle the volume. The final result, however, is homogeneous data, which can be more easily manipulated. System queries are used to gain information from a warehouse and this creates a report for analysis.

Uses for Data Warehouses

Companies commonly use data warehousing to analyze trends over time. They might use it to view day-to-day operations, but its primary function is often strategic planning based on long-term data overviews. From such reports, companies make business models, forecasts, and other projections. Routinely, because the data stored in data warehouses is intended to provide more overview-like reporting, the data is read-only.