Dataware Housing

  

Objectives:

 · Define a data warehouse

· Identify data warehouse use cases

. List the benefits of a data warehouse

What is a data warehouse?

A data warehouse is a system that aggregates data from one or more sources into a single consistent data store to support data analytics

Data warehouse analytics

Data warehouse systems support:

  1. Data Mining
  2. Artificial Intelligence
  3. Machine learning
  4. Front-end reporting
  5. OLAP Online analytical processing 

Where are data warehouses hosted?

Traditional data warehouses hosted on-premises within enterprise datacenters initially hosted on mainframes, and then on Unix, Windows and Linux systems

Where are data warehouses hosted?

2000’s

Growth of larger data volumes, emergence of data ware housing appliances (DWAs), consisting of specialized hardware with pre-integrated software

2010-present

Adoption of Cloud Data Warehouses, providing eliminating hardware purchases, as a scalable service pay-as-you-go service

Who uses data warehouses?

Practically every industry

  1. e-Commerce
  2. Transportation
  3. Medical
  4. Government
  5. Banking and fin-tech
  6. Social media

Retail and e-commerce

. Analyze and report on sales performance

· Create machine learning assisted shopping recommendations

Transportation

· Optimizes routes, travel times, equipment needs & staffing requirements

Healthcare

· Apply AI to patient data to assist with diagnosis and treatment

Banking and fin-tech

· Evaluate risks, detect fraud and cross-sell services

Social media

. Measure customer sentiment and project product sales

Governments

· Analyze and evaluate citizen-focused programs and assist with policy change decisions

 

Benefits of a data warehouse

Centralizes data from disparate sources

· Creates a single source of truth

· Leverages all the data while enhancing speed to access

· Facilitates smarter decisions using BI

 

What are the benefits of a data warehouse? 

 Data warehouses enable organizations to centralize data from disparate data sources, such as transactional systems, operational databases, and flat files. 

Data integration, removing bad data, eliminating duplicates, and standardizing data create 

A single source of the truth that results in better data quality for analysis. 

A single source of truth empowers users to leverage all the company's data and access 

that data more efficiently. 

In addition, separating database operations from data analytics generally improves data 

access performance, leading to faster business insights. 

Large-scale BI functions such as data mining, artificial intelligence, and machine 

learning tools facilitate smarter decisions by data professionals and business leaders. 

These capabilities build on each other to give organizations the means and opportunity 

to realize competitive advantages and gains.



Difference between Transactional and operational


OLTP vs. OLAP: Key Differences & Use Cases

OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) are two different database systems used for different purposes in business operations.

How OLTP & OLAP Work Together

  1. OLTP Systems (Transactional) Collect Data First

·       A customer purchases a product online (OLTP records the transaction).

·       A bank processes a withdrawal and updates account balance in real-time.

  1. OLAP Systems (Analytical) Use This Data for Insights

·       The e-commerce business analyzes customer purchase patterns to predict demand.

·       A bank runs fraud detection models based on past transactions.


Feature

OLTP (Transactional System)

OLAP (Analytical System)

Purpose

Handles real-time transactions

Analyzes and processes large datasets

Data Type

Current, real-time data

Historical, aggregated data

Operations

Read, write, update, delete (CRUD)

Complex queries, reporting, data mining

Speed

Fast, optimized for frequent transactions

Slower, optimized for deep analysis

Users

Customers, cashiers, employees

Business analysts, executives, data scientists

Example

ATM withdrawals, e-commerce orders, retail sales

Sales trend analysis, market research, financial forecasting

Database Type

Relational databases (e.g., MySQL, PostgreSQL)

Data warehouses (e.g., Amazon Redshift, Google BigQuery)






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