Data Lake vs. Data Mesh

Data Lake: The current landscape

A Data Lake is a centralized place where data from many teams is stored together. While this can be convenient, it often means that the people who best understand the data (the domain experts) are not the ones defining how it’s structured, documented, or used. Over time, this can lead to data that’s hard to interpret or reuse.

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  • Pros
  • Cons

  • One place to look for your data

  • Cheap storage

  • Flexible (store anything)

  • Becomes a data swamp if poorly curated

  • Central team becomes a bottleneck
  • Domain knowledge (e.g. microbiology context) gets lost. Thus making it hard to know if data sets can be trusted

Infographic created with ChatGPT

Data Mesh: The new architecture

A Data Mesh takes a different approach. Instead of centralizing everything, each domain (for example, microbiology experiments, sequencing, imaging) owns its data and treats it as a product. That means the teams who generate the data also define its meaning, quality, and documentation, while still following shared standards so data can be easily discovered and reused across projects.

Benefits to Data Mesh:

  • Datasets are easier to trust, understand, and reuse

  • The system scales better as projects and collaborations grow

  • Domain knowledge stays with the experts who generate the data

Key Takeaway:

Infographic created with ChatGPT

Data Lake = one big shared storage system

Data Mesh = domain-owned, well-described datasets connected through shared standards