Dive Into the Data Lake

A mirror-smooth lake reflects steep grey mountains and dark green forest beneath a pale blue sky.

What's the big idea behind a data lake?

Storing raw data of any kind at scale, ready for later use

Storing only cleaned, modelled data for monthly reports

Replacing every database with one giant spreadsheet

Holding only structured sales tables in fixed predefined schemas

or press Enter ⏎

Swimmingly done!

Spot on! A data lake holds structured, semi-structured and unstructured data in its raw form, so it can be shaped later for many uses.

Oops, a bit murky!

Not quite. A data lake stores raw data of any kind at scale, so analysts and data scientists can shape it later for whatever they need.

To get feedback you first need to answer the question above.

or press Enter ⏎

A data lake applies structure when data is read, not when it's loaded. What's this called?

Schema-on-write

Schema-on-read

Lazy normalisation

Deferred indexing

or press Enter ⏎

Swimmingly done!

Correct! Schema-on-read means you decide the structure when you query, which keeps loading fast and flexible.

Oops, a bit murky!

It's schema-on-read. Traditional warehouses use schema-on-write, defining structure before data goes in; lakes wait until it's read.

To get feedback you first need to answer the question above.

or press Enter ⏎

A dark, still forest pool with bare trees and dry reeds rising out of the water.

Your lovely lake has turned into a 'data swamp'. What most likely went wrong?

It ran out of storage space

Too many people were allowed to read it

Data was dumped in without governance or metadata

It stored unstructured data alongside tables

or press Enter ⏎

Swimmingly done!

Exactly! Without governance and metadata, nobody knows what's in the lake, where it came from or whether to trust it.

Oops, a bit murky!

The culprit is data dumped in without governance or metadata. Size and mixed formats are normal for a lake; losing track of what's in it is what makes the swamp.

To get feedback you first need to answer the question above.

or press Enter ⏎

Which best describes data governance?

The IT team's job of backing up servers nightly

A one-off clean-up project before an audit

Software that automatically deletes old files

Policies, roles and rules for managing data as an asset

or press Enter ⏎

Swimmingly done!

Right! Governance sets the policies, roles and rules that keep data accurate, secure and usable across the whole organisation.

Oops, a bit murky!

Governance is the policies, roles and rules for managing data as an asset. It's an ongoing business discipline, not just an IT task or a one-off project.

To get feedback you first need to answer the question above.

or press Enter ⏎

Rows of wooden cabinets with many small labelled drawers line a stone archway, lit by warm hanging lights.

What does a data catalogue mainly help people do?

Find, understand and trust available datasets

Automatically fix spelling errors in customer records

Speed up queries by compressing tables

Turn raw data into finished dashboards

or press Enter ⏎

Swimmingly done!

Yes! Like a library catalogue, it's a searchable inventory of datasets with descriptions, owners and lineage.

Oops, a bit murky!

A data catalogue helps people find, understand and trust datasets. Think library catalogue: it tells you what exists and where, not how to encrypt or compress it.

To get feedback you first need to answer the question above.

or press Enter ⏎

Sales says 'customer' means anyone who's bought; Marketing includes sign-ups. Which tool settles the row?

A data backup

A data dictionary

A data pipeline

A data lake

or press Enter ⏎

Swimmingly done!

Peace restored! A data dictionary gives each field and term an agreed definition, type and meaning.

Oops, a bit murky!

It's a data dictionary, which records agreed definitions, types and allowed values, so 'customer' means one thing to everyone.

To get feedback you first need to answer the question above.

or press Enter ⏎

Who is usually accountable for the quality and meaning of a dataset day to day?

The cloud hosting provider

Whoever queried it most recently

The data steward

The chief executive personally

or press Enter ⏎

Swimmingly done!

Correct! Data stewards look after definitions, quality and proper use of the data in their care.

Oops, a bit murky!

It's the data steward, the person who looks after a dataset's quality, definitions and proper use on behalf of the business.

To get feedback you first need to answer the question above.

or press Enter ⏎

A wide, pale blue river winds between grassy plains and steep, brown-gold mountains under a blue sky.

A report shows odd figures. What lets you trace where that data came from and how it changed?

Data compression

Data sharding

Data masking

Data lineage

or press Enter ⏎

Swimmingly done!

Nailed it! Data lineage maps the journey from source to report, so you can see where things went wrong.

Oops, a bit murky!

It's data lineage, which follows data upstream from the report to its source, including every transformation along the way.

To get feedback you first need to answer the question above.

or press Enter ⏎

What's a key business benefit of combining a lake with good governance and a catalogue?

Faster, trusted self-service analytics

No need for any security controls

Data never needs updating again

Every user can edit every dataset freely

or press Enter ⏎

Swimmingly done!

Yes! People can find trustworthy data themselves, saving time and making better decisions.

Oops, a bit murky!

The prize is faster, trusted self-service analytics. Governance adds control rather than removing it, and data still needs care.

To get feedback you first need to answer the question above.

or press Enter ⏎

Under GDPR, how does governance help with personal data in a lake?

GDPR doesn't apply to raw data

It tags and controls personal data so access and deletion are managed

It moves all personal data outside Europe

It lets you skip consent if data is anonymous-ish

or press Enter ⏎

Swimmingly done!

Correct! Classifying and controlling personal data lets you restrict access and honour requests like the right to erasure.

Oops, a bit murky!

Governance tags and controls personal data, so access is restricted and deletion requests can be honoured. GDPR applies to raw data too.

To get feedback you first need to answer the question above.

or press Enter ⏎

Perfect! Every answer right!

11 of 11 right

Challenge your colleagues to keep their data out of the swamp!

Your nickname, if you want

Without one, it says “A friend”.

See your answers · all right

Question 1:
What's the big idea behind a data lake?
Storing raw data of any kind at scale, ready for later use
Storing only cleaned, modelled data for monthly reports
Replacing every database with one giant spreadsheet
Holding only structured sales tables in fixed predefined schemas
Spot on! A data lake holds structured, semi-structured and unstructured data in its raw form, so it can be shaped later for many uses.
Not quite. A data lake stores raw data of any kind at scale, so analysts and data scientists can shape it later for whatever they need.
Question 2:
A data lake applies structure when data is read, not when it's loaded. What's this called?
Schema-on-write
Schema-on-read
Lazy normalisation
Deferred indexing
Correct! Schema-on-read means you decide the structure when you query, which keeps loading fast and flexible.
It's schema-on-read. Traditional warehouses use schema-on-write, defining structure before data goes in; lakes wait until it's read.
Question 3:
Your lovely lake has turned into a 'data swamp'. What most likely went wrong?
It ran out of storage space
Too many people were allowed to read it
Data was dumped in without governance or metadata
It stored unstructured data alongside tables
Exactly! Without governance and metadata, nobody knows what's in the lake, where it came from or whether to trust it.
The culprit is data dumped in without governance or metadata. Size and mixed formats are normal for a lake; losing track of what's in it is what makes the swamp.
Question 4:
Which best describes data governance?
The IT team's job of backing up servers nightly
A one-off clean-up project before an audit
Software that automatically deletes old files
Policies, roles and rules for managing data as an asset
Right! Governance sets the policies, roles and rules that keep data accurate, secure and usable across the whole organisation.
Governance is the policies, roles and rules for managing data as an asset. It's an ongoing business discipline, not just an IT task or a one-off project.
Question 5:
What does a data catalogue mainly help people do?
Find, understand and trust available datasets
Automatically fix spelling errors in customer records
Speed up queries by compressing tables
Turn raw data into finished dashboards
Yes! Like a library catalogue, it's a searchable inventory of datasets with descriptions, owners and lineage.
A data catalogue helps people find, understand and trust datasets. Think library catalogue: it tells you what exists and where, not how to encrypt or compress it.
Lightning round 1
Question 6:
Sales says 'customer' means anyone who's bought; Marketing includes sign-ups. Which tool settles the row?
A data backup
A data dictionary
A data pipeline
A data lake
Peace restored! A data dictionary gives each field and term an agreed definition, type and meaning.
It's a data dictionary, which records agreed definitions, types and allowed values, so 'customer' means one thing to everyone.
Question 7:
Who is usually accountable for the quality and meaning of a dataset day to day?
The cloud hosting provider
Whoever queried it most recently
The data steward
The chief executive personally
Correct! Data stewards look after definitions, quality and proper use of the data in their care.
It's the data steward, the person who looks after a dataset's quality, definitions and proper use on behalf of the business.
Question 8:
A report shows odd figures. What lets you trace where that data came from and how it changed?
Data compression
Data sharding
Data masking
Data lineage
Nailed it! Data lineage maps the journey from source to report, so you can see where things went wrong.
It's data lineage, which follows data upstream from the report to its source, including every transformation along the way.
Question 9:
What's a key business benefit of combining a lake with good governance and a catalogue?
Faster, trusted self-service analytics
No need for any security controls
Data never needs updating again
Every user can edit every dataset freely
Yes! People can find trustworthy data themselves, saving time and making better decisions.
The prize is faster, trusted self-service analytics. Governance adds control rather than removing it, and data still needs care.
Question 10:
Under GDPR, how does governance help with personal data in a lake?
GDPR doesn't apply to raw data
It tags and controls personal data so access and deletion are managed
It moves all personal data outside Europe
It lets you skip consent if data is anonymous-ish
Correct! Classifying and controlling personal data lets you restrict access and honour requests like the right to erasure.
Governance tags and controls personal data, so access is restricted and deletion requests can be honoured. GDPR applies to raw data too.

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