Deciphering Data Engineering and Analytics Engineering Acronyms
DLH.io (DataLakeHouse)
Make data-driven decisions by consolidating data to grow revenue, decrease costs, save time, and add business value.
When it comes to modern data working, data science, data engineering, and analytics engineering so many tools, concepts, and methodologies have spun up over the last decade. Sometimes all this jargon seems like it is spun into a spider web of confusing consonants and acronyms only for those technically initiated. But alas giving meaning to meaning is important.
So, as a process of getting back to the basics and educating the uninitiated here is a short list of some popular abbreviations/acronyms every data worker and technology leader should know:
A Few Generally Data-Related Acronyms/Abbreviations Definitions
DWH = Data Warehouse
DW = Data Warehouse
DLH = Data Lakehouse
ETL = Extract Transformation and Load
ELT = Extract Load and Transform
DB = Database
RDBMS = Relational Database Management System
OLTP = Online Transaction Processing
OLAP = Online Analytical Processing
Data Lake = Data Lake Storage (aka Object Storage)
Blob Storage = Object Storage
DevOps = Development Operations or Developer Operations
DataOps = Data Operations
CI = Continuous Integration
CD = Continuous Deployment
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CI/CD = Continuous Integration & Continuous Deployment
DIM = Dimension
TBL = Table
GPT = Generative Pre-Trained Transformer
AI = Artificial Intelligence
GAI or GenAI = Generative Artificial Intelligence
ML = Machine Learning
KPI = Key Performance Indicator
SRC = Source
TGT = Target
RAW = Raw (typically for raw data)
ACID = Atomicity, Consistency, Isolation, Durability
Did we miss one that is general or commonly used and not vendor specific?
Some of the more popular definitions in data engineering, analytics engineering, data science, data working, and machine learning seem like they are found in daily conversation. While others, potentially less technical, may seem like a Rosetta Stone is needed to decipher these acronyms.??
While above may be a short list, rest assured that it will continue to grow, as technology in the data space continues to evolve.?
Check back here as a helpful resource for you to share and stay abreast of how the best data workers in the world talk about data and analytics.
Data Engineer | Tailwyndz LLC
6 个月Thanks for posting these Acronyms related to data engineering, as knowing key acronyms is essential. I just wanted to add an another important one which is ACID: - Atomicity: Ensures transactions are all-or-nothing. - Consistency: Maintains database integrity. - Isolation: Keeps transactions independent. - Durability: Guarantees data remains after a commit. Understanding these principles is crucial for any data engineer to manage and process data effectively.