Data warehousing (DW) is the repository of a data and it is used for Management decision support system. Data warehouse consists of wide variety of data that has high level of business conditions at a single point in time.
Data Mining is set to be a process of analyzing the data in different dimensions or perspectives and summarizing into a useful information. Can be queried and retrieved the data from database in their own format.
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ETL is abbreviated as Extract, Transform and Load. ETL is a software which is used to reads the data from the specified data source and extracts a desired subset of data. Next, it transform the data using rules and lookup tables and convert it to a desired state.
Real-time datawarehousing captures the business data whenever it occurs. When there is business activity gets completed, that data will be available in the flow and become available for use instantly.
Aggregate tables are the tables which contain the existing warehouse data which has been grouped to certain level of dimensions. It is easy to retrieve data from the aggregated tables than the original table which has more number of records.
A Datamart is a specialized version of Datawarehousing and it contains a snapshot of operational data that helps the business people to decide with the analysis of past trends and experiences. A data mart helps to emphasizes on easy access to relevant information.
ER diagram is abbreviated as Entity-Relationship diagram which illustrates the interrelationships between the entities in the database. This diagram shows the structure of each tables and the links between the tables.
In datawarehousing, loops are existing between the tables. If there is a loop between the tables, then the query generation will take more time and it creates ambiguity. It is advised to avoid loop between the tables.
If you've ever had to bring data from multiple systems and applications together, you know what an expensive and time-consuming task that can be. Without being able to share and understand the same data easily, each application or data integration project requires a custom implementation.
In addition to the metadata system, Common Data Model includes a set of standardized, extensible data schemas that Microsoft and its partners have published. This collection of predefined schemas includes entities, attributes, semantic metadata, and relationships. The schemas represent commonly used concepts and activities, such as Account and Campaign, to simplify the creation, aggregation, and analysis of data.
And what if you need to create a fourth app? Your data will be ready in Common Data Model schema, so your development efforts can concentrate on business logic rather than data quagmires and sticky transformations.
App makers and/or developers: Whether these users leverage code-based platforms or a low-code/no-code platform such as Power Apps or Power BI, they need to store and manage data for their apps.
Common Data Model simplifies data management and app development by unifying data into a known form and applying structural and semantic consistency across multiple apps and deployments. To summarize the benefits:
Common Data Model is influenced by data schemas that are present in Dynamics 365, covering a range of business areas. If you are a customer or a partner using Dynamics 365, you are already using Common Data Model.
Data Warehouse is a collection of software tool that help analyze large volumes of disparate data. The goal is to derive profitable insights from the data. This course covers advance topics like Data Marts, Data Lakes, Schemas amongst others.
Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal is to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes.
Data modeling employs standardized schemas and formal techniques. This provides a common, consistent, and predictable way of defining and managing data resources across an organization, or even beyond.
Ideally, data models are living documents that evolve along with changing business needs. They play an important role in supporting business processes and planning IT architecture and strategy. Data models can be shared with vendors, partners, and/or industry peers.
Like any design process, database and information system design begins at a high level of abstraction and becomes increasingly more concrete and specific. Data models can generally be divided into three categories, which vary according to their degree of abstraction. The process will start with a conceptual model, progress to a logical model and conclude with a physical model. Each type of data model is discussed in more detail below:
As a discipline, data modeling invites stakeholders to evaluate data processing and storage in painstaking detail. Data modeling techniques have different conventions that dictate which symbols are used to represent the data, how models are laid out, and how business requirements are conveyed. All approaches provide formalized workflows that include a sequence of tasks to be performed in an iterative manner. Those workflows generally look like this:
Two popular dimensional data models are the star schema, in which data is organized into facts (measurable items) and dimensions (reference information), where each fact is surrounded by its associated dimensions in a star-like pattern. The other is the snowflake schema, which resembles the star schema but includes additional layers of associated dimensions, making the branching pattern more complex.
Data modeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand relationships among the data in a database or data warehouse. In addition, it can:
Numerous commercial and open source computer-aided software engineering (CASE) solutions are widely used today, including multiple data modeling, diagramming and visualization tools. Here are several examples:
Abstract: This is the first tutorial in a series designed to get you acquainted and comfortable using Excel and its built-in data mash-up and analysis features. These tutorials build and refine an Excel workbook from scratch, build a data model, then create amazing interactive reports using Power View. The tutorials are designed to demonstrate Microsoft Business Intelligence features and capabilities in Excel, PivotTables, Power Pivot, and Power View.
In these tutorials you learn how to import and explore data in Excel, build and refine a data model using Power Pivot, and create interactive reports with Power View that you can publish, protect, and share.
This tutorial series uses data describing Olympic Medals, hosting countries, and various Olympic sporting events. We suggest you go through each tutorial in order. Also, tutorials use Excel 2013 with Power Pivot enabled. For more information on Excel 2013, click here. For guidance on enabling Power Pivot, click here.
Select the OlympicMedals.accdb file you downloaded and click Open. The following Select Table window appears, displaying the tables found in the database. Tables in a database are similar to worksheets or tables in Excel. Check the Enable selection of multiple tables box, and select all the tables. Then click OK.
Exploring imported data is easy using a PivotTable. In a PivotTable, you drag fields (similar to columns in Excel) from tables (like the tables you just imported from the Access database) into different areas of the PivotTable to adjust how it presents your data. A PivotTable has four areas: FILTERS, COLUMNS, ROWS, and VALUES.
It might take some experimenting to determine which area a field should be dragged to. You can drag as many or few fields from your tables as you like, until the PivotTable presents your data how you want to see it. Feel free to explore by dragging fields into different areas of the PivotTable; the underlying data is not affected when you arrange fields in a PivotTable.
With little effort, you now have a basic PivotTable that includes fields from three different tables. What made this task so simple were the pre-existing relationships among the tables. Because table relationships existed in the source database, and because you imported all the tables in a single operation, Excel could recreate those table relationships in its Data Model.
But what if your data originates from different sources, or is imported at a later time? Typically, you can create relationships with new data based on matching columns. In the next step, you import additional tables, and learn how to create new relationships.
With the data still highlighted, press Ctrl + T to format the data as a table. You can also format the data as a table from the ribbon by selecting HOME > Format as Table. Since the data has headers, select My table has headers in the Create Table window that appears, as shown here.Formatting the data as a table has many advantages. You can assign a name to a table, which makes it easy to identify. You can also establish relationships between tables, enabling exploration and analysis in PivotTables, Power Pivot, and Power View.
Format the data as a table. As described earlier in this tutorial, you press Ctrl + T to format the data as a table, or from HOME > Format as Table. Since the data has headers, select My table has headers in the Create Table window that appears.
You now have an Excel workbook that includes a PivotTable accessing data in multiple tables, several of which you imported separately. You learned to import from a database, from another Excel workbook, and from copying data and pasting it into Excel.
To make the data work together, you had to create a table relationship that Excel used to correlate the rows. You also learned that having columns in one table that correlate to data in another table is essential for creating relationships, and for looking up related rows. 2ff7e9595c
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