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What is Data Warehouse?
Data warehousing is nothing but storage. It is used to secure electronic data for any business or organization. The purpose of data warehousing is to build a historical data trove. To contribute useful insight into the company's services, it can be recovered and examined.
The data warehouse is also known as enterprise data warehousing. It is used to perform several works such as organizing, analyzing, and reporting information electronically. It also has various utilities or examples. Data warehousing offers data mining supper easy. By this method, businesses understand the data patterns. As an outcome, the company can commence to higher earnings or revenues.
A data warehouse also works like a data management system. It is designed to facilitate and promote business intelligence (BI) activities. It is a core part of analytics. To handle data warehouses, we have to perform SQL queries. The task is to analyze the data and often carry huge amounts of past information.
Data warehouse assignment help for students
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Importance of data warehouse in business
The data warehouse has various benefits in business. Those are improved data analytics, strategic market competence, and greater revenue. It has the ability to improve the speed and efficiency of accessing different data sets. By using several strategies, it will be easier for corporate decision-makers to determine insights for the business.
Types of data warehouse designs
Data warehouse design is used to build a solution procedure to integrate data from various sources. Various types of data warehouse designs are depended on how their functions are executed. Data Warehouses designs are categorized into three types. Those are given below:
● Data Mart
● Enterprise Data Warehouse
● Operational Data Store
Types of data warehouse schemas
The Data Warehouse Schema defines the contents of the Data Warehouse. It is achieved through facilitating the operations performed and the maintenance activities. It usually contains a detailed description of the databases, tables, views, indexes, etc. Various types of data warehouse schemas are given below:
● Star Schema
● Snowflake Schema
● Galaxy Schema
Types of data warehouse architecture
● A data warehouse has three architectures. Those are discussed below:
● Single-tier architecture. It aims to reduplicate data to reduce the existing data.
● Two-tier architecture. It separates physical data sources from the data warehouse. Many end-users support this.
● Three-tier architecture. It contains three tiers. Those are given below:
1. The bottom tier. It is the database of the data warehouse server.
2. The middle tier. It is an online analytical processing (OLAP) server. It presents an abstracted database view.
3. The top tier. It is a front-end client layer. It consists of the tools and APIs for data extraction.
Types of data warehouse fact tables
There are three types of fact tables are available in a data warehouse. Those are given below:
● Transaction Fact Table: - It is a basic procedure to run businesses. These fact tables describe an incident that happens at the initial point.
● Snapshot Fact Table: - It represents the nature of things at a critical time. It includes several semi-additive and non-additive matters.
● Accumulated Fact Sheet: - It is used to determine the action of a method. It has a start and a finish.
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FAQ on Data Warehouse
What Is OLAP?
The full form of OLAP is Online Analytical Procedure. It is a data warehouse method that is used for managing, processing, and collecting multidimensional data. The main intent is to collect the data and analyze the data.
What Is OLTP?
The full form of OLTP is Online Transaction Processing. It is focused on transaction-oriented data. By this approach, we can do various operations like upload, edit, and uninstall a small part of data from the database. For example: - user buying records.
What Is Metadata?
Metadata is just like data about the data. It is another type of data. Inside it, the actual collection of data is stored. It deals with the facts of data. The process makes it easier to search and deal with specific data examples. With the help of both manual and automatic processes, metadata can be stored.
What Is SCD?
The full form of SCD is slowly changing dimensions. We can see changes in the data gradually. The changes come in their dimensions. If we want to collect the data, which is shifting its dimension over time, we can use SCD there.
What Is the Distinction Between Metadata and a Data Dictionary?
Metadata means data about data. On the other hand, the data dictionary works based on its mission, ab initio commands, tables, server, etc.
What Is ETL?
The full form of ETL is Extract, Transform, and Load. The machine collects data from various sources, transforms them, and finally loads it into the data warehouse for business-related processing. ETL, a data integration technique enables organizations to do data collection and data integration. It can interact with various types of data with one another.
