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The Curse of the
Data Lake Monster
Kiran Prakash and Lucy Chambers
We have a
problem
@lucyfedia
So what is a data lake?
● Democratisation of Data
● Centralized and Monolithic
● Domain Agnostic
● Structured and unstructured data
@kiran_p
https://martinfowler.com/articles/data-monolith-to-mesh.html @kiran_p
Why do Data
Lakes Fail?
@kiran_p
Build it they will come!
● Seen primarily as an infrastructure problem
● Pinning down uses cases & value stream is hard
● Analysis paralysis & overengineering
@kiran_p
Centralised and Monolithic
https://martinfowler.com/articles/data-monolith-to-mesh.html @kiran_p
Functional Decomposition
@kiran_phttps://martinfowler.com/articles/data-monolith-to-mesh.html
Axis of change
@kiran_phttps://martinfowler.com/articles/data-monolith-to-mesh.html
@kiran_p
Data Swamps
Focus on initiatives which
align with business outcomes.
Structure teams around
business capabilities.
Product
Thinking
Self service platform for
storage, catalogue,
computation, access rights
and pipelines etc.
Autonomous teams with clear
bounded context building and
running products
independently.
Platform
Thinking
Domain Driven
Design
The Data Mesh Paradigm
@kiran_p
Product Thinking
For Data Projects
@lucyfedia
Project vs Product
Project Mode Product Mode
START Solution (often) defined at outset.
Problem identified at outset.
Solution developed iteratively and tested.
STOP Team moves on when solution delivered. Team moves on when problem verifiably fixed.
FOCUS Features delivered in a given time & budget.
Progress made on key business goals
(measured by metrics).
HAS FIXED SCOPE? Usually. Almost never.
@lucyfedia
Product teams have two jobs and two customers
● Deliver business capabilities
- External User
● Expose their domain’s data for others to consume
- (often) Internal User
@lucyfedia
● Discoverable
● Addressable
● Trustworthy
● Self-describing
● Interoperable
● Secure
A data product is:
@lucyfedia
Data Swamps
“If a tree falls in a wood, and
no-one is around to hear it,
does it make a sound?”
- Some philosopher
@lucyfedia
“If someone puts data into
a data lake, and no-one
can find it, is it even there?”
- Me
@lucyfedia
Data Mesh
Architecture
Domain Driven
Design
Self Service
Platforms
@kiran_p
Distributed Pipelines
@kiran_phttps://martinfowler.com/articles/data-monolith-to-mesh.html
@kiran_p
Self service platforms for:
● Storage
● Data pipeline
● Discovery & Catalogue
● Access control
● Archiving
● Encryption
Data Mesh
@kiran_phttps://martinfowler.com/articles/data-monolith-to-mesh.html
Example:
a fictional insurance company
Reduce fraud by
5% per year
Identify
fraudulent
claims
Reduce vehicle damage
claims by 2% per year
Increase conversion
rate by 2%
Predict Weather
Patterns
Upselling
Insurance
Products
The Use-Cases
@lucyfedia
@lucyfedia
Fraud Detection
Customer Claims
Customer
Health
Customer
Vehicle
Claims
Health
Claims
Vehicle
Customer
House
Claims
House
Lake Shore
Marts
Data Lake
(for Raw Data)
@lucyfedia
Fraud Detection
Customer Claims
Customer
Health
Customer
Vehicle
Claims
Health
Claims
Vehicle
Customer
House
Claims
House
Lake Shore
Marts
Data Lake
(for Raw Data)
Upselling
Customer Products
Products
@lucyfedia
Fraud Detection
Customer Claims
Customer
Health
Customer
Vehicle
Claims
Health
Claims
Vehicle
Customer
House
Claims
House
Lake Shore
Marts
Data Lake
(for Raw Data)
Upselling
Customer Products
Alert
Customer Weather
Products Weather
Not a technology problem
Becoming data-driven
is usually an
organisational problem
Work with cross functional
product teams and real use-
cases to deliver business
value.
Build by autonomous cross
functional teams using data
platforms instead of
centralized data lake
Domain data
is a product
Distributed
Data Mesh
Key Takeaways
@kiran_p & @lucyfedia
Kiran Prakash
@kiran_p
Thank you
Lucy Chambers
@lucyfedia
How to Move Beyond a
Monolithic Data Lake to
a Distributed Data Mesh
martinfowler.com/articles/
data-monolith-to-mesh.html
The Curse of the
Data Lake Monster
thoughtworks.com/insights/
blog/curse-data-lake-monster

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