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Data Engineering Concepts

Last updated Feb 9, 2024

There are many data engineering concepts. Most of them are described in this second brain. But here’s a table of the most important in data engineering:

Category Data Engineering Topics
Storage Solutions Data Warehouse, Data Lake, Data Lakehouse, OLAP, OLTP, NoSQL, Storage Layer, Data Lake File Formats, Data Lake Table Format
Data Processing ELT, ETL, EtLT, Reverse ETL, ELTP, Notebooks, MapReduce, Hadoop, Streaming, Batch Processing
Data Architecture Modern Data Stack, Open Data Stack, Functional Data Engineering, Software-Defined Asset, Declarative vs Imperative, Data Engineering Lifecycle, Data Lifecycle
Data Management Data Catalog, Data Contracts, Data Governance, Data Quality, Data Security
Data Modeling & Warehousing Metrics Layer, Semantic Warehouse, Data Virtualization, Data Modeling, Dimensional Modeling, Conceptual, Logical to physical Data Models, Data Vault, Relational Model, Normalization, Denormalization, Granularity, Cardinality
Performance & Optimization Push-Downs, Metrics, KPI
Cloud & Big Data Technologies Cloud Data Warehouses, Big Cloud Vendors, Data Fabric, Data Mesh, MLOps
Data Integration & Federation Data Orchestrators, Data Lineage, Graph Databases, GraphQL, Analytics API, Data Integration

See also Data Engineering Approaches or Data Engineering Vault.

References: #publish
Created 2023-08-21