dotnet core barcode generator

.net core barcode

.net core barcode

.net core barcode

.net core barcode generator,

.net core barcode generator,
.net core barcode,

.net core barcode generator,
dotnet core barcode generator,
dotnet core barcode generator,
.net core barcode,
.net core barcode generator,
.net core barcode generator,

dotnet core barcode generator,
.net core barcode generator,
.net core barcode,
.net core barcode generator,
.net core barcode,
.net core barcode,
dotnet core barcode generator,
dotnet core barcode generator,
dotnet core barcode generator,
.net core barcode,

dotnet core barcode generator,
.net core barcode generator,
.net core barcode,
dotnet core barcode generator,
dotnet core barcode generator,
.net core barcode generator,
.net core barcode generator,
.net core barcode,
dotnet core barcode generator,
.net core barcode,
.net core barcode,
.net core barcode,
.net core barcode,
.net core barcode generator,
dotnet core barcode generator,
.net core barcode generator,
.net core barcode generator,
.net core barcode,
.net core barcode,
.net core barcode generator,
dotnet core barcode generator,
.net core barcode,
.net core barcode generator,
.net core barcode generator,
.net core barcode generator,
dotnet core barcode generator,
dotnet core barcode generator,
.net core barcode,
dotnet core barcode generator,
.net core barcode,
dotnet core barcode generator,
.net core barcode generator,
dotnet core barcode generator,
.net core barcode,
.net core barcode,
.net core barcode generator,
.net core barcode,
.net core barcode,
.net core barcode generator,
dotnet core barcode generator,
.net core barcode,
.net core barcode generator,
.net core barcode generator,
dotnet core barcode generator,
.net core barcode,
dotnet core barcode generator,
.net core barcode generator,
dotnet core barcode generator,
dotnet core barcode generator,
.net core barcode generator,

While this book uses star schema examples to illustrate dimensional design techniques, any dimensional model can also be implemented as a multidimensional cube. Cubes can be incorporated into a dimensional architecture in two ways, as you learned in 3, Stars and Cubes. One approach calls for the cube as a primary storage mechanism of dimensional data; the other uses cubes as high-performance supplements derived from stars. When a cube serves as the primary storage mechanism for dimensional information, the derivation techniques in this chapter provide the same benefits. Derived cubes are deployed in lieu of derived stars. Like their relational counterparts, derived cubes may be used to improve the performance of queries, reduce the complexity of report development, or limit the scope of the schema. Sometimes cubes are not used for the primary storage of dimensional data because relational structures do a better job storing large volumes of data. Instead, cubes are used as high-performance supplements to the relational data. In this case, the cubes are derived by definition, and may mirror any of the derived schema designs in this chapter. The underlying star schema is relied upon as a data source for a variety of derived cubes, each of which is a compact high-performance data structure, targeted for a specific category of business question.

So far, we ve looked at several different methods of performing backups. Chances are good that more than one will apply to your environment. For example, developers workstations that contain large amounts of information might need to be backed up using local backup solutions, whereas a server farm would be a better scenario for a centralized solution. In many cases, the pros and cons of the potential solutions will make the decision obvious. In other cases, however, the scenario you choose will be based on time, administration, and technical constraints. With this in mind, let s move on to look at an overview of the technology available for accomplishing these tasks.

If you are reading this book cover to cover, you have already encountered several examples of derived schemas. Each offers benefits over the original dimensional schema for specific kinds of analysis. These examples included the snapshot, accumulating snapshot, and core fact tables.

Summary

Nowadays, hard disk space is cheap, and buying an additional drive can often provide the level of data redundancy needed as an adequate (though very cumbersome) solution for client machines The large amounts of data normally stored on the server side, however, still requires tape devices (which have evolved incredibly in the last few years) These days, it s likely that more than one solution will fit an organization s data protection needs The challenge has been reduced to making the best choice, instead of trying to find just anything that meets the goals The details of the solutions will be based on the business s constraints and on technology So, how can we choose the best hardware solution for backups We ll cover the specifics of various hardware solutions in 5.

Snapshot fact tables are sometimes derived from transaction fact tables, as discussed in 11. A snapshot fact table samples a measurement of status, such as an account balance or inventory level, at a predefined interval. While status can be determined by aggregating individual transactions, doing so can be very inefficient. The snapshot fact table makes this kind of analysis easy. Figuring out your bank account balance on a particular day, for example, should not require aggregating the entire history of transactions dating back to the day you opened it. This is possible but is highly inefficient. If it were necessary to do this for hundreds of accounts at a time, report performance would be very poor. Instead, a daily snapshot of account balances makes short work of figuring out this information. It is constructed from data in the transaction fact table during the load process. There is no need to aggregate years worth of historic transactions during the query process. Snapshot-grained fact tables are not always derived. As noted in 11, some snapshots capture measurements that do not represent the cumulative effect of transactions. Examples include ambient temperature, internal air pressure, and reservoir levels. These measurements can only be captured by sampling real-world conditions at the predetermined interval. In other cases, snapshots may be employed because transactions are too voluminous to keep in the data warehouse. In these cases, the snapshot is not considered a derived schema; instead, it serves as the primary storage mechanism for dimensional data.

This chapter described the SQL statements that are used to modify the contents of a database: The single-row INSERT statement adds one row of data to a table. The values for the new row are specified in the statement as constants.

For now, know that the following issues will affect your data protection plan: w Backup capacity When shopping for backup hardware devices, you ll probably first look at the capacities of a potential solution If you have about 20GB to back up, a very convenient choice will be a solution that can record this much information on a single piece of media That way, you won t be stuck in the server room on a Friday evening, waiting to change tapes Backup hardware vendors often use the assumption that your data will compress to half its normal size (a 2:1 ratio) This might occur for some types of data, but you should use better estimates We ll look at some recommendations in later chapters For now, keep in mind that a major factor in your decision will be how much data can be stored on a single piece of media.