The data warehouse is the data source for SQL Server Analysis Services (SSAS). This, however, is not the only function of a data warehouse. With friendly names, single table data sources created from previously disjoined tables, and the flexibility to manipulate data, a data warehouse can be a big improvement in self-service reporting.
Data warehouses can be built using add-on products like Power BI, BI360, Jet Reports, or through the tools available in SQL. This means combining CRM, POS, time-keeping, or other database systems into the same central source for easy reporting. Through an Extract Transform and Load (ETL) process, the data warehouse tables are created from these sources. The data warehouse is a separate database that resides independently on a server and can contain information from multiple data sources. This redundant information makes reporting easier by allowing you to report and filter on this field without linking the tables. In a data warehouse, you can combine these tables by adding fields to the line table from the header table such as salesperson. Some of the data in the header table is not needed in the line table as it would be redundant (e.g., repeated salesperson info on each transactional line).
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An example of how to use this in Microsoft Dynamics 365 Business Central and Dynamics NAV is the Sales Header and Sales Line tables. A de-normalized database has redundant data included for optimized read performance. Data ManipulationĪs opposed to normalization, de-normalization is the process of using a data warehouse (DWH) to transform a normalized database into one that is de-normalized. It is important to understand how different reporting tools work to determine which is best for you. This problem can become exacerbated when a report has thousands of queries. The problem arises when queries grow too big in size and complexity such that the results take too long to return an answer. To answer a question, one would query a database with a set of filters to return the results. Traditional database reporting works on these concepts.
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For example, all the customer sales from a certain zip code would be obtained by summing the sales fact table based on that dimension linked by the common field, customer number. This primary and foreign key relationship associates the tables and by linking these keys to different dimensions and can be analyzed. Thus, customer #1000 in the sales line table is the same customer #1000 in the customer table. These tables are related to each other through keys. The customer attributes such as the assigned salesperson, credit terms and address are stored in a dimensional table. The table with transactional information is called a Fact Table. Meaning if you sell something to customer #1000, the sales line table will record the amount, quantity, customer number, and other unique information related to the sale. BC and NAV run on a normalized SQL database structured in this way.ĭatabase normalization is the theory behind relational databases wherein information is separated into different tables to prevent redundancy.
A database can consist of many tables in the same way an Excel document can have many sheets. Though the names are different, the structure is the same.
Rows, columns, and sheets form the structure in Excel while a database uses records, fields, and tables. If you have ever seen an Excel spreadsheet, then you have seen the structure of a database. To understand how to look at BI tools for Business Central and NAV, it is important to first understand how they store data. The question is, which business intelligence tools (BI) are the best? In this article, we take a closer look at BI tools for Microsoft Dynamics 365 Business Central (BC) and Dynamics NAV. Today, the business world is user-centered, with self-service reporting available to allow decision makers to get their reports the way they want them.
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