Data Engineering Lifecycle

Last updated Oct 13, 2024

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.

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:
  • Data Catalog
  • Reverse ETL
• General Foundations (Undercurrents):
  • Data Security
  • Data Management
    • Data Modeling (e.g., Dimensional Modeling)
    • Data Quality, Observability, Monitoring (Governance)
  • Data Engineering Architecture
  • Orchestration
  • Software Engineering

Related is the Data Lifecycle and Data Canvas.

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References:
Created 2022-12-21