Introduction to Data Management Tools

Vocabulary flashcards covering data management tools, big data characteristics, database structures, and analytical software mentioned in the lecture notes.

Data

Raw, unorganized facts that lack meaning until they are processed, organized, and structured with context.

Information

Data that has been given context and organized so that it has meaning, such as the average training score of an entire department.

Data Management (DM)

The practices, architectural techniques, and tools used to achieve consistent access to and delivery of data across all subject areas and structure types in an enterprise.

Data Governance

A concern with data policies, procedures, access control, backup and recovery, and classification standards to keep business-critical data safe and confidential.

Data Discovery (Data Mining)

The examination of huge sets of data to find patterns, connections, outliers, and hidden relationships to assist in making informed business decisions.

Business Intelligence (BI)

A broad range of tools and practices used to extract, analyze, and report information to enable better strategic decision-making and predictions.

Data Lakes

Storage systems that hold large amounts of unstructured data in their raw form to allow for flexible analysis.

Machine Learning Algorithms

Tools that identify patterns and anomalies in data sets to make predictions based on that data.

Database Management Systems (DBMS)

Software systems used to create and manage databases where data are stored in computer files called tables.

Tables

The most important part of a database, consisting of records (rows) and fields (columns) used to store and query data.

Primary Key

A field in a database table that uniquely identifies a specific record, such as a unique student ID number.

Foreign Key

A field in a database table that provides a link between two different tables in a relational database.

Schema

The organization, layout, reference, or blueprint of a database that defines tables, fields, constraints, keys, and integrity.

Big Data

Large and expansive collected data sets that are often disparate and not in neat, tidy database tables.

Volume

One of the 4 Vs; the characteristic of big data referring to the enormous amount of digital data that grows exponentially.

Variety

One of the 4 Vs; indicates that data comes from both structured areas and fragmented, unstructured sources.

Veracity

One of the 4 Vs; refers to the quality and trustworthiness of data, requiring scrubbing to remove discrepancies.

Velocity

One of the 4 Vs; the accelerating speed at which data is produced over a given time period.

Structured Data

Data that resides in fixed formats, is typically well-labeled with traditional fields and records, and is easily searchable.

Unstructured Data

Unorganized data that cannot be easily read by a computer because it is not in rows and columns; it accounts for $80\%$ of all data.

Semi-structured Data

Data that contains both consistent structured elements and unstructured components, such as email and HTML files.

Data Warehouse

A digital storehouse used to consolidate disparate data in a central location, potentially holding yottabytes of data.

Yottabyte

A unit of data storage equal to one trillion ($10^{12}$) terabytes.

Data Mart

A smaller data set designed to support the specific needs of a single department, such as sales or human resources.

ETL (Extract, Transform, and Load)

A set of tools used to standardize data across systems by pulling it from sources, cleaning it, and moving it into a warehouse.

Hadoop

An infrastructure for storing and processing large, unstructured, or semi-structured data sets across a distributed file system of multiple servers.

Structured Query Language (SQL)

The most widely used standard computer language for relational databases, allowing programmers to manipulate and query data.

Tableau

Software that produces interactive data visualizations and dashboards to simplify raw data into business insights.