Skip to content
Meet Coda 👋
Meet Coda
Learn more
ELABORATE GUIDE ON DATA WAREHOUSING- WHAT, HOW, WHY, AND MUCH MORE
More
Share
Explore

ELABORATE GUIDE ON DATA WAREHOUSING- WHAT, HOW, WHY, AND MUCH MORE

Make the most of the data science revolution by hiring expert data science professionals to make future-proof business decisions. Best data science certifications are capable of leveling up the data science game for the technology aspirants manifold. Data science projects require ace skills and expertise in deploying data warehousing techniques to offer effective business solutions.
⁠
GUIDE ON DATA WAREHOUSING.png
⁠
⁠
We are living in a world where data is the ruling authority, consumerism is on the rise, and businesses going virtual. Data is the commanding force and hence, it involves huge expertise to collect, manage and deduce useful insights from it for big business decisions. Data warehousing is exactly known for the same. With such huge data being generated every second, corporations are inducing data warehousing as the pivot to drive key business outcomes. The data is just a number, if not dealt right with. Analyzing business data with the appropriate data warehousing strategies is the way to go.
Turning these big numbers into meaningful information to make timely business decisions is the key factor guiding future milestones. Data warehousing is the core of the business intelligence system that is targeted at data analysis and timely reporting. There are different names that data warehousing is known around, such as Decision Support system, Executive Information System, Management Information System, Business Intelligence Solution, and Analytic Application. The key characteristics of data warehousing include:
ü Being subject-oriented simply means that it provides information on a topic instead of ongoing organizational operations.
ü By combining data from varied sources, a data warehouse is open to integration by establishing a standard unit of measurement from different databases.
ü The data collected in a data warehouse is time-variant; that infers it is acknowledged over a given period and offers historical data as well.
ü Data warehousing comes packed with the non-volatility feature that helps in restoring and safeguarding previous data as well when the new data is fed into it.
Leading
Data science projects
⁠
are powered by critical data science elements that help make business processes smoother. They are listed below:
· Audit tables
· Tests
· DDL Repository
· Data connectors
· External Parameters table
· Team roles and responsibilities architecture between environments
· ETL toolkit with screens
This brings us to an important aspect of data warehousing-
How does the Data warehouse work?
Being the central repository of information from different data sources, the data flows through the transactional system and relative databases. Data may be structured, unstructured, and semi-structured. Other business tools are at play along with elaborate spreadsheets, this merger of information helps organizations in analyzing consumer behavior.
The three types of Data warehouses are:
o Enterprise data warehouse- It enables data to be easily classified based on the subject and provide access as per the bifurcation.
o Operational data store- The data warehouse refreshes itself in real-time and is widely preferred for routine activities- storing employee records.
o Data mart- A subset of data warehouse, that is designed for a specified business department- sales, finance, etc.
Here, it is inevitable to understand who needs to make use of data warehouses;
Ø The stakeholders and decision-makers who depend upon huge data
Ø Users
Ø Individuals who aspire to use simpler technology for data access
Ø Individuals demanding faster performance
Ø The ones who wish to investigate the hidden patterns in the data flows and groupings
Before you get cracking with the data warehousing processes, it is crucial to understand the components of data architecture for your business, which include devising;
§ Data pipelines
§ Cloud storage
§ APIs
§ AI-ML models
§ Data streaming and real-time analytics
§ Kubernetes
§ Cloud computing
Talking about Data warehouse as an intelligent technology, it is used by diversified sectors of our economy, such as:
ü Airline
ü Banking
ü Healthcare
ü Public sector
ü Investment and Insurance
ü Retail chain
ü Telecommunication
ü Hospitality
The prominent data warehouse tools doing the rounds in the world market are MarkLogic, Oracle12c, Xplenty, IBM Infosphere, Teradata, and Amazon Redshift, among many other popular data warehouse tools.
Data science professionals
⁠
make use of these tools which help in simplifying complex search operations and optimizing customer experiences; increasing operational efficiency as well. For a clearer understanding, it’s time to distinguish between the pros and cons of data warehousing as a clever technique.
1
PROS
CONS
2
Allows quick access to critical data
Not compatible with unstructured data
3
Offers consistent information on cross-functional activities
Time-consuming
4
Reduces stress on the production system
Can get outdated quick
5
Reduces total turnaround time for analysis and reporting
Difficult to make changes in the data
6
Easy to use
Limited project scope
7
Enables storage of a large amount of data
Develops different business rules
8
Allows users to analyze and compare periods and make future predictions
Eats up a lot of time and resources for training and implementation
There are no rows in this table
⁠
The future of data warehousing depends upon the quality of data science skills deployed coupled with credible
data science certifications
⁠
from MIT, USDSI™, Stanford, Harvard, etc, which must be earned to serve in the industry. Make the most of the time today to reach the pinnacle of your data science career in practically no time. Be the pivot for big business groups looking at quick and easy access to critical data from a wide range of sources; and help them make the best business decisions for amplified future growth.
Want to print your doc?
This is not the way.
Try clicking the ⋯ next to your doc name or using a keyboard shortcut (
CtrlP
) instead.