Data Terminology

Covering roles and responsibilites, disciplines and topics, data storage approaches, terms and abbreviations.



Data Roles

Data roles refer to the different responsibilities and functions of individuals or teams regarding data management, analysis, and decision-making within an organization. These roles can include data analysts, data scientists, data engineers, data stewards, and data governance professionals. Each role ensures data assets' quality, security, and effective utilization.

The modern data department and data team represent a significant evolution from traditional data engineering and operations roles, reflecting broader shifts in technology, business needs, and organizational strategies. This transformation is driven by the increasing volume, velocity, and variety of data, alongside the growing demand for real-time insights and data-driven decision-making.

Traditional Data Engineering and Operations focused on the design, construction, and maintenance of scalable data pipelines and storage solutions. Data engineers were primarily concerned with ETL (Extract, Transform, Load) processes, data warehousing, and ensuring data quality and reliability for downstream analytics. Data operations, on the other hand, dealt with the day-to-day management of data infrastructure, including database administration, data backup, recovery, and ensuring the smooth operation of data systems. The roles were highly specialized, with clear demarcations between responsibilities related to data processing, storage, and management.

The Modern Data Department and Data Team encompasses a broader range of functions and expertise, reflecting the shift towards more integrated and agile approaches to data management and utilization. It includes roles such as Data Scientists, Data Engineers, Data Analysts, Machine Learning Engineers, and DataOps professionals, each contributing to a more dynamic and collaborative environment.

Modern Data Department

  • Data Steward
  • Data Custodian
  • Data Privacy Officer
  • Data Strategist
  • Head of Data
  • VP of Data Strategy
  • Chief Data Officer (CDO)
  • Executive Data Sponsor

Modern Data Team

  • Data Architect
  • Data Scientist
  • Data Engineer
  • DataOps Engineer
  • Data Science Engineer
  • Data Quality Engineer
  • Data Security Engineer
  • ML Engineer
  • Data Specialist
  • Data Science Manager

Traditional Data Engineering

  • Data Analyst
  • Data Warehouse Developer
  • Software Developer
  • Software Engineer
  • Enterprise Architect
  • Solutions Architect
  • Data Modeler

Traditional Data Operations

  • DevOps Engineer
  • Site Reliability Engineer (SRE)
  • Database Reliability Engineer (DBRE)
  • Database Administrator (DBA)

Data Disciplines

Data disciplines encompass the various specialized areas or domains within the broader data management and analysis field. These disciplines may include data analytics, data science, data engineering, data governance, data quality management, data visualization, and data mining. Each discipline focuses on specific aspects of data, such as collection, storage, processing, modeling, and visualization, contributing to the overall data lifecycle and enabling data-driven decision-making.

  • Metadata Management
  • Master Data Management (MDM)
  • Reference Data Management
  • Data Catalog
  • Data Governance
  • Data Contract
  • Data Stewardship
  • Data Security
  • Data Quality
  • Data Lineage
  • Data in Motion
  • Data Insight
  • Data-Driven Decision

  • Data Standards
  • Data Privacy
  • Data Curation
  • Data Science
  • Data Workflow
  • Data Pipelines
  • Data Cleansing
  • Data Mapping
  • Data Integration
  • Data Transformation
  • Data Monetization

  • Data Taxonomy
  • Data Ontology
  • Data Modeling
  • Data Analysis
  • Data Communication
  • Business Intelligence (BI)
  • Data Analytics
  • Data Migration
  • Data Archaeology
  • Decision Intelligence (DI)

  • Data Store
  • Data Stream
  • Data Visualization
  • Data Storytelling
  • Data Dashboard
  • Data Observability
  • Data Engineering
  • Data Agility
  • Data Literacy
  • Data Gravity

Data Storage

Data storage refers to the various technologies, systems, and infrastructures used to persist and retain data for future use or analysis. This can include databases (relational, NoSQL, in-memory), file systems, data warehouses, data lakes, and cloud-based storage solutions. The choice of data storage solution depends on factors such as the volume, velocity, and variety of data and the specific requirements for data access, retrieval, and processing within an organization or application.

Database Architectures

  • Database
  • Data Warehouse (DW/DWH)
  • Data Mart
  • Data Lake
  • Data Lakehouse
  • Data Swamp
  • Data Silo
  • Data Mesh
  • Data Hub
  • Big Data
  • Data File
  • Data Set


  • Relational Database Management System (RDBMS)
  • NoSQL Database
  • Key/Value Database
  • In-Memory Database
  • Document Database
  • Graph Database
  • Time-Series Database
  • Ledger Database
  • Column-Oriented Database
  • Object-Oriented Database
  • Vector Database

Data Terms

  • Data Structure
  • Data Query Language
  • Structured Query Language (SQL)
  • Extract, Transform, Load (ETL)
  • Extract, Load, Transform (ELT)
  • Change Data Capture (CDC)
  • Atomicity, Consistency, Isolation, Durability (ACID)
  • Online Transactional Processing (OLTP)
  • Online Analytical Processing (OLAP)
  • NoSQL
  • Machine Learning (ML)
  • Artificial Intelligence (AI)
  • Single Source of Truth (SSOT)
  • Generative AI (GenAI)

Cool Project Terms

  • Digital Garden

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