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Introduction
Welcome Ninjas! In the current scenario, a vast amount of data is continuously generated by the users, which leads to the creation of digital data storage systems called data warehouses at which organizations can deploy their generated data, and later on, that data will be used for building valuable insights and fetching useful information out of it.
So, In this article, we will learn in detail about the components of data warehouse, and how things work around it.
What is Data Warehousing?
A data warehouse is a digital storage system where data from single or numerous sources are stored in a systematic manner.
The primary purpose of a data warehouse is to feed Business Intelligence firms, analytics, and reporting. It also fulfils the organizations' regulatory requirements that help them convert their data into useful information, which leads to assistance in making data-driven decisions.
A data warehouse stores current and previous data in the same place and acts as a single source of data information for the business.
Bell Inmon, the father of data warehouses and a very famous author of various data warehouses books, says that a data warehouse is an integrated, reliable, supporting business decision-making process.
A Typical Data Warehouse often includes the following Elements:
Data source: The system or systems that supply the data for the data warehouse are called the data source. Any system that stores data, such as a relational database, a NoSQL database, a file system, etc.
Data mart: A data mart is a more compressed, more specialised data warehouse created to cater to the requirements of a particular business unit or department.
Data Warehouse Database: The physical storage of the data in the data warehouse is the data warehouse database.
ETL: ETL stands for extract, transform, load, and it is used to extract data from a data source, transform it into a format that is compatible with a data warehouse, and then load the data into the data warehouse.
Meta Data: Information about data is known as metadata. It contains details on the ETL procedure, the data, and the data source. The data warehouse is managed and made easier to use with the help of metadata.
Tools for data mining: The data warehouse data are mined for patterns and insights using data mining techniques and tools.
Benefits of data warehousing
Data Management: Due to centralized data storage, we have all the data from different sources under one hood. It also supports data quality, data integration and transformation processes which improves the quality of data and ensures data is consistent without any errors.
Data Security and Control: Data warehousing enforces some validation rules to store data which promotes security and it also allows the user or group to define roles and permission giving them access control.
Decision Making: Data warehousing allows to use of advanced tools like Power Bi and other analytics tools to better understand the trend of data and make a better decision.
Performance: Data warehousing has various query optimising techniques such as indexing, partitioning, and caching to improve query performance. It also supports large amounts of data volume.
What if a Data Warehouse Doesn't Exist?
Data extraction will become very difficult for data analysts and data scientists; they have to extract it from the production databases and may report different results to the same question.
It is almost impossible to keep track of previous and current data simultaneously.
It will not be possible to compare historical data as data stored in the warehouses is already transformed and integrated.
Unable to keep track of metadata repository.
Components of Data Warehouse
There are various components of data warehouse that are specifically designed to enhance the system's speed so that you can get the result faster and precisely analyze the data in one go.
Warehouse Databases
It is one of the first components of a data warehouse; let's discuss some of the warehouse databases-
Analytics Database:-
These databases help sustain and manage data storage's analytics part.
Cloud-based Database:-
In this case, you hosted your database on the cloud so that you don't have to acquire any hardware system for establishing your data warehouse.
Central Database:-
It keeps all the data related to business organizations and makes it easier for analysts to build report about it.
Typical rational databases:-
These databases consist of data in the form of rows and columns, which collectively form a table.
ETL(Extraction, Transformation, and Loading)
ETL is another important component of the data warehouse. ETL, which stands for Extraction, Transformation, and Loading, is a data integration process in which data is extracted from various sources, it is transformed into a suitable format, and then loaded into a data warehouse. This component allows us to extract data, fill disarranged data, highlight data distribution from the central repository to the business intelligence applications, and much more.
How does ETL Work?
To understand how ETL works, we should go through each step of the ETL process.
Extract
Copy raw data from the source locations to a staging area. This data is collected by the data management team and can be structured or unstructured.
Transform
In the staging area, this data is transformed by filtering, cleansing, de-duplicating, validating, and authenticating the data, etc.
Loading
In this step, transformed data is moved from the staging area to the target data warehouse. It initially loads all the data, then gradually load it as changes occur in the data.
Metadata
Metadata is a component that can be used in a variety of conditions to build, manage and maintain the system. The simplest definition of metadata is “it is data about the data.”
It helps us to understand the context, nature, and structure of the data.
It enables the user to have an easy search and retrieval of data.
It is a key to unlocking the hidden content of the data and getting a proper understanding of it.
Query Tools
Tools are the components using which we interact with the data warehouse and get relevant data out of it.
Some of the tools used for interaction purposes are query and reporting tools, application development tools, data mining tools, and online analytical tools.
Firstly, query and reporting tools are categorized into managed query tools and reporting tools. Reporting tools are used for developing business reports, and the end-users can use them at an affordable cost.
Managed query tools to protect the end user from SQL query-related complexities by adding a security layer between the database and users.
Online analytical processing tools are generally used to extract or retrieve data selectively so that they can be analyzed from a different standpoint. These tools believe that data is managed in a multidimensional model.
Data mining tools are the set of tools that are used to analyze large amounts of data and the relationship in that data.
Data Marts
Data marts are components of data warehouse. Let’s discuss it in detail:
It is a data store that is designed for a particular department of an organization, or a Data mart is a subset of the data warehouse that is usually oriented to a specific purpose.
It helps stakeholders to make decisions quickly from the summarized data and make knowledgeable decisions.
In a data mart, companies can retrieve information more efficiently as it contains the most relevant information.
Used for making streamlined decisions and gives privileges to access minute data.
As it has very few data tables, data engineers can manage and change information without causing significant database changes.
Data Warehouse Management and Administration
Unlike traditional relational databases, a data warehouse collects vast amounts of past and current data. To manage this, we require an administrator with a skillset different from the traditional data administrator.
All the control elements manage the system within the data warehouse, and these components also control the transformation of data into the data warehouse.
Information Delivery System
In organizations, all the business executives with no training in data warehouses require information about the data warehouse; in such cases, they use the delivery component to convey the information.
The idea behind the information delivery system is made for those cases when the warehouse becomes completely functional; its users will not have to be aware of its maintenance. All the need is the contention of data at the required time.
After the expansion of the world-wide-web and internet, this delivery system has become more usable as any user can get information using the worldwide network.
Challenges with a Data Warehouse Architecture
Data from different sources have different formats and maintaining them with high quality and consistency within a data warehouse is a bit challenging.
Integration of data with various formats, structures, and systems calls is also difficult and needs better planning and knowledge.
Large data volumes can be hard to handle while keeping the system performance and responsiveness as high.
data security and privacy and complying with the regular changing privacy rules is a bit complex.
adapting to new and evolving business needs requires careful planning and strong management.
creating and maintaining large volumes of data in a data warehouse can be expensive and resource intensive.
Why we need a separate Data Warehouse?
Data Analysis: Data warehouses are designed for analytical purposes, allowing for complex queries and data analysis.
Historical Data: They store historical data over a long period, facilitating trend analysis and forecasting.
Integration: Data warehouses integrate data from multiple sources, providing a unified view for analysis.
Performance: Optimized for read-heavy workloads, they offer faster query performance compared to transactional databases.
Aggregation: They support data aggregation and summarization, enabling decision-making at different levels of granularity.
Difference between Database and Data Warehouse
Feature
Database
Data Warehouse
Purpose
Transactional processing and OLTP
Analytical processing and OLAP
Data Type
Current, real-time
Historical, aggregated
Schema Design
Normalized
Denormalized or star/snowflake schema
Usage
Day-to-day operations
Strategic decision-making and analysis
Query Complexity
Simple queries
Complex queries for analysis
Performance
Optimized for write operations
Optimized for read operations
Data Structure
Relational tables
Multi-dimensional structures (cubes)
Data Volume
Typically smaller
Typically larger than transactional databases
Frequently Asked Questions
What are the key requirements of a data warehouse?
Instead of focusing on the actual design of the data sources, the data warehouse should be structured around business principles. It ought to be a read-only data store. It ought to be able to expand as the amount of data does. To safeguard the data, it should be secure.
What are the uses of a Data Warehouse?
Collecting past and current data at the same storage, making better decisions, improving business efficiency, etc.
What is the basic process of a data warehouse?
The basic processes of a data warehouse are data extraction, data transformation, data loading, data analysis, and data presentation. These processes are mainly divided into the ETL phase and the analysis and presentation phase.
What are the components of Data Warehouse?
Components of a data warehouse include ETL(Extraction, Transformation, Loading), Metadata, Query tools, Data marts, Data Warehouse Management and Administration, and Information Delivery systems.
Conclusion
In this article, we learned about components of data warehouse like a Warehouse Database, ETL(how it works), query tools and their sub-components, Data marts, delivery systems, warehouse management, and administrations, etc.
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