Data Engineering Lifecycle
Last updated Dec 6, 2023
Table of Contents
In today’s dynamic environment, a data engineer is responsible for managing the entire data engineering process. This encompasses gathering data from diverse sources and preparing it for use in downstream applications. Mastery of the various stages of the data engineering lifecycle is crucial, along with a knack for assessing data tools to ensure they deliver on multiple fronts: cost-effectiveness, speed, flexibility, scalability, user-friendliness, reusability, and interoperability.
The data engineering lifecycle, as depicted by Fundamentals of Data Engineering
Alternatively, refer to this visualization in a Tweet:
Further insights can be found in Data Engineering Architecture (e.g., the one from A16z).
Example Open Data Stack Project
In our Open Data Stack project, we delve into the essential components of the lifecycle, such as ingestion, transformation, analytics, and machine learning.
Discover more at The Evolution of The Data Engineer: A Look at The Past, Present & Future.
# Undercurrents
These are the core pillars of the lifecycle, omnipresent across its various stages: security, data management, DataOps, data architecture, orchestration, and software engineering.
The lifecycle’s functionality hinges on these undercurrents.
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# My Fundamentals:
](https://www.ssp.sh/brain/data-engineering-lifecycle/#my-fundamentals)[
# Data Engineering Lifecycle
](https://www.ssp.sh/brain/data-engineering-lifecycle/#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.
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# Core Principles and Links
](https://www.ssp.sh/brain/data-engineering-lifecycle/#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:
- 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