The Hook data modeling pattern with Andrew Foad
AgileData Podcast #63
Join Shane Gibson as he chats with Andrew Foad on his data modeling pattern "Hook"
Listen
Listen on all good podcast hosts or over at:
https://podcast.agiledata.io/e/the-hook-data-modeling-pattern-with-andrew-foad-episode-63/
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Read the podcast transcript at:
https://agiledata.io/podcast/agiledata-podcast/the-hook-data-modeling-pattern-with-andrew-foad/#read
Google NoteBookLLM Briefing
Briefing Document: The Hook Data Modeling Pattern
Source: Excerpts from "The Hook data modeling pattern with Andrew Foad - Episode #63" of the Agile Data podcast.
Date: 5 May 2025 (Approximate date of recording based on podcast reference)
Subject: Review of the Hook data modeling pattern, its origins, core concepts, and benefits, as discussed by Andrew Foad (creator of Hook) and Shane Gibson (podcast host).
Executive Summary:
The Hook data modeling pattern is presented as a simpler, more agile approach to data warehousing, developed by Andrew Foad to address perceived problems with traditional data modeling techniques, particularly Data Vault. Hook focuses on organising raw data by aligning it to business concepts using "hooks" (formalised business keys) rather than transforming the data structure upfront. This approach aims to reduce bottlenecks, increase agility, and make data more accessible for self-service analytics with "just enough modeling" (JEM) in the consumption layer.
Key Themes and Ideas:
Origins and Motivation: Hook was developed by Andrew Foad out of frustration with the complexity and bottlenecks encountered on Data Vault projects. A key moment was an engineer expressing a preference for a simple satellite table over a fully modelled Data Vault structure, enabling quicker report building. This highlighted the desire to decouple modeling from data delivery and prioritize getting data into a usable format faster.
"Hook is my attempt to solve problems that I encountered with data vault."
"There has to be a better way... why do we have to do the modeling up front... is there a way that we could perhaps not have to do that?"
An engineer's response to receiving a satellite table without a full Vault structure: "'Yes give me that because if you give me that I can go and build the reports off the back of that.' He didn't care about the vaulty stuff It was a hindrance It was in the way He wanted to get to the right hand side as quickly as he possibly could."
Core Concepts of Hook:
Not Traditional Modeling: Foad argues that Hook isn't traditional modeling because it doesn't change the structure of the incoming data. It focuses on adding information and aligning data to business concepts.
"I don't like to say modeling because I don't think it really is modeling for me... With hook you don't do that Whatever comes in is what comes out the other end All you're doing is you're adding some additional information to it."
Aligning to Business Concepts: The core function is to align raw data objects to predefined business concepts (e.g., Customer, Order). This is similar to the concepts behind Data Vault hubs, but without the physical restructuring.
"Basically you're aligning those objects to business concepts... but you're not changing the underlying data You're basically tagging those assets aligning those and formalizing the business keys."
The 'Frame' Object: Hook primarily uses a single object type called a 'frame'. A frame is a wrapper around the raw or landed table. The data within the frame is not transformed, only augmented with "hooks".
"We only have really one object type and it's called a frame... Basically you take your raw or your landed table and you wrap it You frame it... The data isn't transformed It's just a wrapper that you put around it."
'Hooks' (Formalized Business Keys): Hooks are formalized business keys that are added as additional columns to the frame. These keys align the frame to business concepts and enable joining across different frames.
"The additional things that you add to that are formalized business keys which align to those business concepts... So basically you've got a big bus matrix then concepts and assets or frames."
Example: An HK customer field in both customer and orders tables allows them to be joined.
'Key Sets': Key Sets are prefixes applied to formalised business keys within a hook. They provide context about the origin or type of the business key, particularly useful when dealing with multiple source systems or different keys for the same concept (e.g., Customer ID vs. Customer Code).
"It's basically a predefined sequence of characters which tells you where that key came from... It's a bit more than that It's not just distinguishing between systems It's given us something that we can use to basically give a bit more context around that business key."
Key sets are defined in a reference table or metadata store.
Business Glossary First: A core rule in Hook is that a hook (formalised business key) cannot be created unless the corresponding business concept has a definition in the business glossary. This prioritises business understanding before applying it to data.
"One of the hard rules in hook is that it has to be in the glossery and you really should have a definition for it as well."
Agility and Implementation:
Hook can be implemented using views or by adding physical columns, making it very agile. Changes like adding, dropping, or renaming keys don't require refactoring the core structure.
"You could implement that as say a view... or you could have a physical table You just add a column calculate it drop a column if you want to get rid of it So it's really agile in that respect You don't have to refactor the model at any point."
The approach is lightweight, focusing on organization rather than complex transformation upfront.
"It's like data world I guess but you've just collapsed all those business keys into the satellite That's really all it is."
Layered Architecture Context:
Hook fits best in the "designed" or "silver" layer of a multi-layered data architecture (e.g., Raw, Designed, Consume or Bronze, Silver, Gold).
Raw Layer: Ingestion of source data as is.
Designed Layer (Hook): Applying the Hook pattern to the raw data. The physical structure remains source-aligned, but hooks provide logical alignment to business concepts. This is the "organize" step in the ELO (Extract, Load, Organize) pattern.
"Hook is about the data warehouse component... it's the inman criteria the subject oriented integrated time variant and then immutable it's that bit after that you've got a consumption bit that's when you have to do some modeling but the idea is because you've organized things in the hook structure what we found is that you don't need to do too much modeling."
"The designed layer what we used to call the EDW that is where hook comes into its own."
Consume Layer: Specific modeling (the "small T" in ELO) is done here for particular use cases that cannot be easily met directly from the Hook layer. This is "just enough modeling" (GEM).
"So it's modeling light because you've organized You don't need to do too much modeling That's the idea."
This layer can produce different output formats (dimensional, flat wide tables, activity schemas) depending on the consumption tool's needs.
Complex logic, aggregations, measures, and metrics are typically handled in this layer.
Data can sometimes be consumed directly from the Hook (Designed) layer if the use case is simple and users are familiar with the source structure and the added hook context.
Handling Complexity:
Historical Data: While not explicitly part of the Hook pattern itself, historical data (tracking changes over time using effective dates, is current flags, etc.) is handled in layers after the raw ingestion, typically in the Silver layer, separate from the core Hook organisation. This is a common problem across all modeling techniques.
"That isn't a hook thing but that's absolutely something that we've done... we applied that row effective from row effective to row is current row is deleted We added those fields on as well using those same techniques using the windowing functions."
Master Data Management (MDM) / Deduplication: Inferring relationships between keys from different sources (e.g., mapping Customer 123 from System A to Customer ABC from System B) is seen as heavy lifting that happens outside the core Hook layer, likely producing another dataset that can then be ingested and have hooks applied.
"At the end of the day you've got to create an asset which does the mapping between one key to another key and that's just another asset."
Derived/Inferred Data: Calculations, aggregations, measures, and metrics are pushed down to the consume layer or a separate processing layer before consumption.
"The measures and the metrics because we're inferring or calculating things They're going to be in that consume layer That's where we're going to push all that heavy lifting again."
Metadata and Automation:
Hook relies on a simple metadata model to define concepts, key sets, hooks, and frames.
This metadata can be used to automate the generation of Hook structures (views or tables) and downstream assets, reducing manual effort and ensuring consistency.
"The meta model itself is pretty straightforward There's only a few tables... you've got hook you've got key sets you've got concepts and frames... you just tag stuff and then there's little templating engine in there which says how do you want to spit this out."
Automation can generate SQL scripts or configuration files (like YAML for DBT).
Key Facts:
Hook was created by Andrew Foad.
It emerged from experience with Data Vault projects and the desire for a simpler approach.
It aligns data to business concepts using formalised business keys called "hooks".
Data structures are typically not transformed from the source; Hook wraps the raw data in a "frame".
"Key sets" are used to prefix and provide context for business keys.
Implementation can be physical (adding columns) or virtual (using views).
It promotes a "just enough modeling" (GEM) approach in the consumption layer.
It fits well within a layered data architecture, primarily operating in the "designed" or "silver" layer.
It relies on a simple metadata model for automation.
Quotes of Note:
"Hook is my attempt to solve problems that I encountered with data vault."
"Whatever comes in is what comes out the other end All you're doing is you're adding some additional information to it... Basically you're aligning those objects to business concepts."
"We only have really one object type and it's called a frame... The data isn't transformed It's just a wrapper that you put around it."
"The additional things that you add to that are formalized business keys which align to those business concepts... So basically you've got a big bus matrix then concepts and assets or frames."
"One of the hard rules in hook is that it has to be in the glossery and you really should have a definition for it as well."
"So it's modeling light because you've organized You don't need to do too much modeling That's the idea."
"The designed layer what we used to call the EDW that is where hook comes into its own."
"It's not ELT it's ELO Extract load and organize You're not restructuring There's no T involved."
"We only model by exception We don't model absolutely everything."
Areas of Discussion/Further Exploration:
The practical limitations and "horrible use cases" where Hook might not be the best fit (though Foad suggests its scope is limited to the warehouse organisation layer, pushing complexity elsewhere).
Specific details on the metadata model and the capabilities of automation tools like "Hook Studio".
Detailed examples of how Key Sets handle various complexities.
Comparisons to other lightweight data organisation patterns.
Real-world case studies beyond the initial project mentioned.
This briefing provides a foundational understanding of the Hook data modeling pattern as described in the podcast episode, highlighting its core principles, benefits, and architectural fit.
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