ITE353 Data Scalability and Analytics - Vocabulary Flashcards

Vocabulary flashcards covering analytics, scalability concepts, and the analytics tools mentioned in the lecture notes.

Analytics

The scientific process of discovering and communicating meaningful patterns in data to turn raw data into insights for better decisions; relies on statistics, programming, and operations research.

Data Scalability

The ability of a system to adapt over time to changes in data volume or processing demands, increasing or decreasing performance and cost.

Data Scaling

A technique to manage the overflow of data by increasing system capacity, enabling scalable data platforms and accommodating growth.

Scalable Data Platform

A data platform that can rapidly adjust to data growth or traffic by adding hardware or software, preparing for future needs.

Scaling Up (Vertical Scaling)

Adding faster processors and more memory to a single server; uses less network hardware and power, often a short-term fix for growth.

Scaling Out (Horizontal Scaling)

Adding more servers to perform parallel computing; a long-term solution that grows with demand by expanding the cluster.

Big Data

Extremely large and complex datasets that require advanced processing, storage, and analytics beyond traditional systems.

High CPU Usage

A key performance issue where CPU utilization is near capacity, slowing processing tasks.

Low Memory

A performance bottleneck caused by insufficient RAM, leading to slower data processing and possible swapping.

High Disk Usage

Excessive disk I/O and storage utilization causing slower data access and processing.

Tableau

A data visualization tool used to create interactive dashboards and visual analytics.

Python

A widely-used programming language for data analysis, scripting, and machine learning.

Microsoft Excel

Spreadsheet software used for data analysis, calculations, and charts.

SPSS

Statistical Package for the Social Sciences; software for statistical analysis.

SAS

Statistical Analysis System; analytics software for data management and analysis.

Apache Spark

Open-source distributed data processing engine for large-scale analytics.

Hadoop

Open-source framework for distributed storage and processing of big data across clusters.

RapidMiner

Data science platform for data preparation, modeling, and deployment.

Serverless Computing

Cloud model where the provider manages server provisioning and scaling, allowing code execution without managing servers.

Artificial Intelligence

Field of computing focused on creating systems that perform tasks requiring human-like intelligence.

Cloud Computing

Delivery of computing services over the internet, including storage, processing, and analytics.

Blockchain

Distributed ledger technology enabling secure, transparent recording of transactions.

Syllabus

Document outlining course topics, requirements, assessments, and grading.

Data-driven Decision Making

Decision making guided by data insights rather than intuition alone.