Data Warehouse Reporting With Advance Analytics For Business Reports

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The phrase “Data Warehouse” refers to a collection of technologies that are used to collect and analyse data from a variety of sources. It is the fundamental component of a business intelligence system that assists in the recording and analysis of historical data reporting. In reality, this is the electronic storage of a vast amount of data for a certain business, with the added function of answering questions and providing analytical reports.

As a result, Data Warehouse reporting refers to the process of gathering and organising data from a variety of sources. Also, assisting in the formation of meaningful concepts.

There are four constituent components of data warehouses. They are as follows :

  • Load Manager –The front component is another name for this component. It is involved with the gathering of raw level data from multiple sources and feeding it into the warehouse.
  • Warehouse Manager –This is the component that handles the data management for business reporting. It performs tasks such as gathering data in a consistent manner, constructing indexes for this data, and creating analytical reports, among others.
  • Query Manager –This component is capable of performing all backend query functions.
  • End-User Tools –These tools can be classified into the following categories:-
  • Data Reporting
  • Tools for Query
  • Tools for QueryaTools for developing applications
  • EIS Tools and
  • OLAP and other tools for data mining.


There are several types of databases that can be distinguished for data warehouse reporting based on the database properties they possess. These can be categorized into the following groups:-

Enterprise Data Warehouse

EDW stands for Enterprise Data Warehouse and is a centralized warehousing system. The Prism Warehouse, for example, uses it to execute complicated data analysis across the organization. It can also help with decision-making while conducting predictive analysis. This system can also be programmed to re-group data according to its subject and then grant access to them as needed.

Operational Data Store

The operational data store, sometimes known as the ODS, is a collection of optional data. This can be used when the data warehouse or OLTP systems are unable to assist with the organization’s reporting functions. This also helps with warehouse management because the data warehouse can be refreshed over a long period of time. The ODS is preferred for operations such as storing employee records in this case.

Data Mart

A Data Warehouse has a branch called a Data Mart. You can utilise it for a specific type of business, such as sales, finance, and so on.

Microsoft BI Data Warehouse: Key concepts

There following are certain key concepts when it comes to data warehouse reporting:-

Warehouse automation software

This is a type of software that generates data warehouse designs automatically utilising metadata, data warehousing techniques, pattern recognition methods, and other processes.

Data warehouse Architectures

A complicated informational structure is implied by the concept of data warehouse architecture. It includes both historical and educational information. Single tier, double tier, and three-tier data architecture are the three primary types of data architecture.

Warehouse Database schema

A logical format for representing a whole database is referred to as a schema. A data warehouse, like a database, requires its own schema to be maintained. A warehouse schema can be divided into three categories. Star, Snowflake, and Fact Constellation are their names.

The ETL Process: From the perspective of Power BI

Extract, Transform, and Load (ETL) is an acronym for Extract, Transform, and Load. This is the method for extracting data from a variety of sources. This can then be converted into a separated version and loaded into the data warehouse. ETL data warehousing involves input and feedback from a variety of investors, stakeholders, and senior executives in order to preserve the reputation of an agile system.

The flawless operation of this process is critical to an organization’s efficiency. This necessitates transformation in tandem with the organization’s changing business habits. Because this is a data warehouse action that occurs frequently, it must be well-documented.

As stated earlier, the ETL procedure consists of three steps, namely- 

Data Extraction: Data must be gathered or extracted from a variety of external sources.

Secondly Data Transformation: converts the extracted data into the format of the warehouse

Lastly, Data Load: sorting, summarizing, or consolidating the data, checking for its integrity and consistency, and finally loading that data into the data warehouse.

Power BI is one of the most popular business intelligence tools for data warehouse reporting on the market today. It enables businesses to collect data, create aesthetically appealing reports, and communicate them across their many departments.

In this case, Microsoft BI Data Flows allows the user to store data as an individual entity or a collection of entities. Through the construction of a solid ETL system, these dataflows enable multiple teams across an organisation to not only import data from diverse external sources, but also to articulate it into an intelligible manner. The Power BI ETL procedure is without a doubt one of the most well-known computing processes in the commercial world.

What does Microsoft BI use – OLTP or OLAP?

OLAP, or Online Analytical Processing, is used by Microsoft BI. This is software that allows a user to simultaneously process information and data from many databases. This makes analysts’ jobs easier and allows them to look at the same data from different perspectives.

You can use the OLAP process to calculate and sort data before distributing it. This significantly simplifies and speeds up the data analysis process. This data can be divided into numerous cubes, which speeds up and improves the data analysis process. In terms of its impact on business patterns and analysis, OLAP can be described as a business platform for all organisational domains that makes data planning, budgeting, and analysis easier.

When it comes to advanced data analytics reporting, Addend Analytics has been a market leader. For Power, we’ve been a Microsoft Gold partner

Power BI : Working of OLAP Cubes.

The OLAP software has its basis in the core concept of the OLAP cube. There are various cube definitions. To simply state, you can define this cube processing as a structure of data created in order to analyze data faster, in a more comprehensible manner.

The cube constitutes certain numeric facts that you can categorize by dimensions. The OLAP cube is also known as a hypercube. The parts of the cube consist of multi-dimensional data. It is usually derived from unknown external sources. When the cube data structure is put to use, you can analyze and logically group data from various sources. Data cubes play a vital report in data warehouse reporting through Power BI.

Database versus Data Warehouse: The better choice

The term ‘database’ refers to a collection of data that appears to reflect one or more real-world objects. The data warehouse, on the other hand, is a useful application. It can be used to store data from a variety of sources.

The following are the key differences between Database vs Data Warehouse –

  • To begin with, the definitions of these two phrases are different. A database is a collection of relative data that may be reflective of some real-world aspects, whereas a data warehouse is an application that saves data from multiple sources.
  • Data warehouses are used to analyse recorded data, while relational databases are used to record and attribute data.
  • A database is used to store data in an application-based manner, whereas data warehouses are used to store data in a subject-based format.
  • An Online Transactional Process (OLTP) is used in a database, whereas an Online Analytical Processing (OLAP) is used in a data warehouse.
  • Because the database tables are normalised, they are complex and difficult to grasp. The tables offered in data warehouses, on the other hand, are simpler because they are not normalised.
  • Database modelling approaches are referred to as ER modelling techniques. Data modelling techniques are also referred to as those used to create data warehouses.

Quality of Data and its consistency:

The term ‘Data Quality’ refers to a collection of simple procedures that you adopt in your warehouse management system. Furthermore, there are six other dimensions to this notion, which you might categorise as follows:-

  • Integrity
  • Accuracy
  • Completeness
  • Duplication
  • Currency and
  • Consistency

Data Consistency is one of these six aspects, and it has to do with data integrity and currency. This concept can be used in situations when data must be stored and maintained in two locations. Organizations should execute consistency checks within the systems from time to time if they designate two systems to perform the same task, one performing the original pattern of work and the other replicating the former.

Data consistency is checked by comparing the same data in two systems or comparing a collection of data with the overall system’s data. This is because a large system’s data, which normally runs smoothly on a cache-free basis, may be at risk of operating inconsistently.


As a result, we can all agree that data warehouse reporting is an important aspect of an organization’s operation because it aids in the analysis of data from external sources, which is an important means of expanding in the business world.

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