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Data Engineering Lifecycle
In today’s landscape, a data engineer is pivotal in overseeing the entire data engineering process. This involves gathering data from diverse sources and ensuring its availability for downstream applications. A deep understanding of the various stages in the data engineering lifecycle is essential. Additionally, a data engineer must possess the skill to evaluate data tools effectively, considering various aspects such as cost, speed, flexibility, scalability, user-friendliness, reusability, and interoperability.
Illustration of the data engineering lifecycle, from
Fundamentals of Data Engineering
Another perspective can be seen in this
Tweet:
For more insights, see Data Engineering Architecture, such as the one from A16z.
Case Study: Open Data Stack Project
The Open Data Stack project exemplifies practical application, incorporating key lifecycle components like ingestion, transformation, analytics, and machine learning.
Further reading: The Evolution of The Data Engineer: Past, Present & Future.
# Undercurrents
These are the foundational elements of the lifecycle, pervasive throughout its various stages: security, data management, DataOps, data architecture, orchestration, and software engineering. The lifecycle cannot function effectively without these integral undercurrents.
# Core Principles and Links
Here are the above core principles of the engineering lifecycle, added with my own thoughts or features.
- Data Integration (Ingestion)
- Transformation
- Semantic Layer / Metrics Layer
- Physical transformation (e.g., dbt)
- Storage Layer
- Analytics and Machine Learning
- Additional Elements:
- General Foundations (Undercurrents):
# Data Lifecycle
Related is the Data Lifecycle and Data Canvas.
Origin:
References:
Created 2022-12-21