Comprehensive Guide to IS Infrastructure, Cloud Computing, and Databases

IS infrastructure

The foundation that supports an organization's information systems — including hardware, software, networks, data, and facilities — enabling processing, storage, and communication of information.

Hardware

Physical devices that perform input, processing, storage, and output functions.

Software

Programs that manage hardware and enable user interaction.

Databases

Systems for storing, organizing, and retrieving data.

Networks

Connect systems and users to share data and resources.

Data Centers/Facilities

Physical or virtual environments that house IT resources.

System software

Manages hardware and runs the system (e.g., operating systems).

Application software

Performs specific tasks for users (e.g., Excel, CRM systems).

Database

An organized collection of data that supports efficient retrieval, management, and updating.

Usefulness of databases

They centralize data, reduce redundancy, improve accuracy, and support analytics and decision-making.

Networks in IS infrastructure

Systems that connect computers and devices to share data and resources.

Problems with on-premises infrastructure

High capital cost, limited scalability, maintenance burden, downtime risk, and difficulty adapting to changing demands.

Cloud computing

Delivering computing services (servers, storage, software, etc.) over the internet on a pay-as-you-go basis.

Core value proposition of cloud computing

Scalability, flexibility, reduced cost, and access to advanced technology without owning infrastructure.

Differences between on-premises and cloud infrastructure

Ownership: On-prem is owned; cloud is rented. Cost: On-prem = capital expense; cloud = operating expense. Scalability: Cloud scales instantly; on-prem requires new hardware. Maintenance: Cloud provider manages it; on-prem IT staff does.

Benefits of cloud infrastructure

Cost efficiency, scalability, reliability, accessibility, and faster deployment.

Cloud infrastructures facilitate

Remote access, global collaboration, elastic scaling, and digital transformation.

Main components of cloud infrastructure

Compute, storage, networking, virtualization, and management interfaces.

IaaS example

Amazon EC2 (virtual servers)

PaaS example

Google App Engine (development platform)

SaaS example

Salesforce, Gmail (ready-to-use software)

Client-server relationship

A model where a client requests services or data from a server, which processes and returns the result.

Business benefits of infrastructure investment

Greater efficiency, agility, innovation, data-driven decisions, and competitive advantage.

Moore's Law

The number of transistors on a microchip doubles roughly every two years, increasing computing power and decreasing cost.

Factors enabling rise of cloud computing

Broadband internet, virtualization, data center automation, scalable storage, and demand for global access.

Risks and challenges of cloud computing

Data security, compliance, vendor lock-in, downtime, and lack of control over infrastructure.

IaaS

Infrastructure as a Service — provides virtualized computing resources like servers and storage.

PaaS

Platform as a Service — provides tools and environments for developers to build, test, and deploy apps.

SaaS

Software as a Service — delivers fully managed software applications over the internet.

Value and responsibility in IaaS, PaaS, SaaS

IaaS: User manages OS & apps. PaaS: User manages applications only. SaaS: Provider manages everything.

Public Cloud

Services offered over the internet to multiple customers (e.g., AWS, Azure).

Private Cloud

Cloud infrastructure dedicated to a single organization for greater control and security.

Hybrid Cloud

Combines public and private clouds for flexibility and workload optimization.

Reasons for choosing cloud types

Public: Cost-effective, scalable. Private: Security and control. Hybrid: Balance between both.

Data

Raw facts.

Information

Processed data with meaning.

Knowledge

Applied information that supports decision-making.

Purpose of databases

Organize and store data for fast retrieval, maintain data integrity, and support decision-making.

Relational Databases

Uses tables and relationships (e.g., SQL).

Nonrelational Databases

Uses documents, graphs, or key-value pairs for flexibility and scalability.

Querying Basics in Databases

Using commands to retrieve or manipulate data, typically through SQL.

SQL

Structured Query Language: Used to manage and query relational databases.

CRUD

Create, Read, Update, Delete — four basic database operations.

Data Pipeline

A process that moves data from source systems to destinations for storage or analysis.

ETL

Extract, Transform, Load — the process of collecting data, cleaning/converting it, and loading it into a database or data warehouse.

Business Intelligence (BI)

The use of data analysis tools and techniques to support decision-making and improve business performance.

Types of BI tooling

Dashboards, data warehouses, visualization tools, reporting software, and analytics platforms.

Difference between visualization, analytics, and reporting

Visualization: Graphically represents data for insights. Analytics: Explores patterns and trends in data.

Reporting

Summarizes data for tracking metrics or KPIs.

Relative reference in Excel

A cell reference that changes automatically when copied to another cell (e.g., A1 → A2).

Absolute reference in Excel

A cell reference that remains fixed when copied or moved, using dollar signs (e.g., $A$1).

When to use an absolute reference

When you need to lock a specific cell (like a tax rate or benchmark) in a formula so it doesn't change when copied.

Autofill tool in Excel

A feature that automatically fills cells with a pattern or series based on the initial input (e.g., dates, numbers, or formulas).

How to use the Autofill tool

Drag the small square at the bottom-right corner of a selected cell across other cells to copy or extend the pattern.

Pattern setting in Excel

Defining a sequence or rule (like "Q1, Q2, Q3..." or "1, 3, 5...") so Excel can continue it automatically using Autofill.

Why is pattern setting useful

It saves time by auto-generating predictable sequences or formats without manual entry.

Key principles of good spreadsheet formatting

• Keep headers clear and consistent • Use cell formatting (bold, color, alignment) to enhance readability • Maintain uniform data types (numbers, dates, text) • Avoid clutter and redundant formatting • Use borders or shading to separate sections logically.

Why is formatting important in data analysis

It improves clarity, reduces interpretation errors, and makes data presentation more professional.

Data interpretation

The process of analyzing numerical or textual data to identify patterns, relationships, or insights that support decision-making.

Skills involved in interpreting data effectively

Understanding context, identifying outliers, recognizing trends, and applying statistical or logical reasoning.

PROB function

Calculates the probability that a value falls within a specified range, based on a set of values and their probabilities.

Example of PROB function

=PROB(A2:A10, B2:B10, 50, 100) → Probability that the value is between 50 and 100.

MEDIAN function

Returns the middle value in a dataset — half the numbers are above it and half below.

Example of MEDIAN function

=MEDIAN(A2:A10)

COUNT function

Counts the number of cells in a range that contain numeric values.

Example of COUNT function

=COUNT(A1:A10)

When to use COUNT

When you only want to count cells that have numbers, not text or blanks.

COUNTA function

Counts all non-empty cells, regardless of data type (text, numbers, dates, etc.).

Example of COUNTA function

=COUNTA(A1:A10)

When to use COUNTA

When you want to count any filled cell, not just numeric ones.

COUNTIF function

Counts cells that meet a specific condition.

Example of COUNTIF function

=COUNTIF(A1:A10, ">100") counts values greater than 100.

COUNTIFS function

Counts cells that meet multiple criteria across one or more ranges.

Example of COUNTIFS function

=COUNTIFS(A1:A10, ">100", B1:B10, "<50")

When to use COUNTIF or COUNTIFS

When analyzing data based on conditions — e.g., number of sales above target, customers in a specific region, etc.