While Data Warehousing and Database Management Systems (DBMS) share some similarities, they serve different purposes and distinct characteristics which will make YOU decide to ADD Data Warehousing to an existing Relational Database Management System
In 2024, as Data Warehousing continues to evolve with wonders on advancements in technology, methodologies, and best practices for organization to STRIVE BEST in the terms of deliveries.
It is important we understand this fact and move into action.
Definition
Data Warehousing; a process of collecting, storing, and managing large volumes of data from various sources within an organization to support decision-making and analysis for the benefits of all stake holders to the organization.
Purpose of Data Warehousing
Simply say, Data Warehousing concept is to provide a centralized reservoir where structured and unstructured data can be kept, integrated, and organized in a format optimized for querying and analysis.
It helps any Goal-Focus organization drive informed decision-making and gain competitive advantage amongst other businesses in the present and future.
Your Organizational Benefits of Creating Data Warehouse
Some may say, we are just beginning, let’s give some more time and work manually for the time being. Others can come up with excuses of not grown too well in the business to own a data ware house.
The simple truth is that you are missing out from its numerous gains of operating an organization that keeps business records in an automated data warehouse.
We are here to learn why you should create one now by exploring the untiring benefits for your organization(s).
- It can enable organization(s) to gain a holistic view of their data taken from unalike sources and derive actionable insights.
- It allows organizations to handle growing volumes of data thereby adapt to any prompted changes to business growth and expansion
- Analytical queries (gain insights and answer business questions) and reporting (present summarized data in a structured format to communicate key findings to stakeholders) is possible with Data Warehousing
- Data Warehousing ensure data privacy, integrity, and compliance with regulatory requirements.
- It sure facilitates data accessibility and collaboration ONLY to its USER(S) with Permitted Access by providing self-service analytics tools, dashboards, and reporting capabilities across the organization.
7 Major Key Components of Data Warehouse
Data Warehousing have key components which help work together to support data warehousing initiatives, enable organizations to derive actionable insights, make informed decisions, and drive business value from their data assets. Amongst them are:-
- Data Integration: A process of accumulating data from different sources, formats it, and systems into a unified and consistent view for its users
- Data Storage: A bank that archive(store) and retrieve data for future use
- Data Transformation and Cleansing: It involves modifying, restructuring, and standardizing data to ensure accuracy, consistency, reliability and quality for analysis
- Querying and Analysis: Refer to the process of retrieving and examining data stored within the data warehouse to derive insights, identify patterns, and make informed decisions
- Decision Support: This involves utilizing data analysis and reporting capabilities to provide stakeholders with actionable insights
- Historical Trend Analysis: It helps in making informed strategic decisions based on historical data by examining past data patterns, trends to identify insights and predict tomorrow’s outcomes
- Business Intelligence and Reporting: It is visualizing data from data warehouse, extract insights and generate practicable reports that supports decision making in an organization
How is Data warehousing relevance in modern business intelligence (BI) and analytics initiatives?
- This centralize Data as received from multiple sources enables the organization of consolidating different data into a single source of truth
- There is data consistency, accuracy, and completeness, providing a comprehensive view of business operations and performance.
- It has ability to process vast datasets and derive actionable insights in a timely manner.
- Support real-time and near-real-time monitoring of business performance metrics, KPIs (Key Performance Indicators), and operational metrics. It means it can track performance against goals, identify areas for improvement, and make informed decisions to optimize business outcomes.
- It does not rely on IT or technical support by providing self-service BI tools, thereby allow user(s) to explore data, create custom reports
In summary, data warehousing is essential for modern BI and analytics initiatives as it provides a centralized and integrated platform for data storage, analysis, and reporting.
By leveraging historical and real-time data, organizations can uncover hidden patterns, forecast trends, make proactive decisions to drive growth and innovation.
Handling Data Transformations through Data Warehousing Solutions
Data transformations involve modifying, reshaping, or converting raw data from its source format into a format suitable for analysis and reporting.
These transformations may include cleaning, filtering, aggregating, combining or enriching data to meet the requirements of downstream processes, such as loading into a data warehouse.
Transformation of Data may be applied using SQL queries, Scripting Languages, or Specialized ETL tools that offer graphical interfaces for designing transformation workflows.
SQL Queries Tools are;
- Talend Data Integration
- Informatica PowerCenter
- IBM InfoSphere DataStage
- Microsoft SQL Server Integration Services (SSIS)
- Apache NiFi
Scripting Language Tools are;
- Python
- JavaScripts
- Ruby
- Perl
- Bash
Specialized ETL Tools are;
- Apache NiFi
- Pentaho Data Integration (Kettle)
- Oracle Data Integrator (ODI)
- Talend Data Integration
- Microsoft SQL Server Integration Services (SSIS)
- IBM InfoSphere DataStage
- Informatica PowerCenter
Creating a Data Warehousing is one thing, but we have to go extra length in securing the data especially for sensitive ones.
Now, let us consider how to keep you data protected from an un-authorized user(s)
3 Best Methods for Securing Sensitive Data in Your Data Warehousing
- Encryption: Encrypt sensitive data from interception by using encryption algorithms
Examples –
- Advanced Encryption Standard (AES) – Secures data stored within data warehouse
- Transport Layer Security (TLS) – Secures data transmission
- Access controls and authentication mechanisms on any of the Data Transformation Tool: It restricts access to sensitive data with user roles, privileges, and permissions based on job responsibilities.
Examples –
- Administrators Roles; Read-only, read-write permissions with overall access
- Data Analysts Roles; Read-only, read-write permission with level access
- Business Users Roles; Read-only
- Data masking technique: It keeps sensitive information hidden in non-production environments. This technique replaces sensitive data with scrambled values while preserving data format and structure.
Example –
- Mask personally identifiable information (PII) with randomized or hashed equivalents.
4 Reasons of Adding Data Warehousing Alongside Database Management System
- Purpose;
Data Warehousing provide a centralized repository for storing and analyzing large volumes of structured and unstructured data from various sources
Database Management System is software systems that manage and organize structured data in databases
2. Data Structure;
Data Warehousing typically store large volumes of historical and current data in a structured format and incorporate dimensional modeling techniques
DBMSs manage structured data stored in databases using tables, rows, and columns
3. Usage;
Data warehouses are primarily used for decision support, business intelligence, and analytics
DBMSs are used for transaction processing, data storage, and retrieval in operational applications which handles day-to-day transactions.
4. Scope;
Data warehousing points at providing an extensive view of organizational data by integrating and consolidating data from multi-dimensional source and supports analytical processes.
DBMS manage data within personal databases, supporting specific applications or business functions such as customer relationship management (CRM) and others
Finally, if you have followed closely on our article on Data Warehousing Wonders, it is the best time to begin your data storage.
Designing Data Warehousing for your organization with no limit on size or years of operation of the organization.
Nice one
but I want ask a question
What is differents between data warehousing and data center
Thank you sir. well, i believe you saw the definition of what Data Warehousing is from our post. however, Data Warehousing is a process of collecting, storing, and managing large volumes of data from various sources within an organization to support decision-making and analysis for the benefits of all stake holders to the organization.
AND
A data center is a standing structure used to keep several computer systems and their associating components, such as telecommunications and storage systems.
The purpose of a Data Center is to ensure continuous operations of mission-critical IT infrastructure and support various computing and networking tasks, including data storage, processing, and distribution of data to its linked locations.
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