Patrick Cuba

Data Mesh and Data Vault on Snowflake
Data Mesh is a social technical approach and not about technology; Data Vault is a discipline and not just a data model, Domain-Driven Design is about ensuring your application accurately represents the automation of your business’ Business Processes, how can all three integrate into one stack? Can they exist in harmony?

Taking a disciplined approach to analytics with intent, discover how all three formed parts of a five-year journey designed to… Show more

Data Mesh and Data Vault on Snowflake
Data Mesh is a social technical approach and not about technology; Data Vault is a discipline and not just a data model, Domain-Driven Design is about ensuring your application accurately represents the automation of your business’ Business Processes, how can all three integrate into one stack? Can they exist in harmony?

Taking a disciplined approach to analytics with intent, discover how all three formed parts of a five-year journey designed to scale as the business scales, and as technology scales on Snowflake at the centre Show less

Snowflake breathes new life into Data Vault!

A Data Vault model is intended to represent the business through Enterprise Architecture as repeatable patterns.
These are repeatable patterns in:
• Data modelling,
• Data engineering and model testing
• Data architecture
• and information consumption.

This talk is on what those patterns are and how you can use Snowflake native features to simplify your Data Vault patterns

Patrick Cuba
Solution Architect at… Show more

Snowflake breathes new life into Data Vault!

A Data Vault model is intended to represent the business through Enterprise Architecture as repeatable patterns.
These are repeatable patterns in:
• Data modelling,
• Data engineering and model testing
• Data architecture
• and information consumption.

This talk is on what those patterns are and how you can use Snowflake native features to simplify your Data Vault patterns

Patrick Cuba
Solution Architect at Snowflake

Influential Data Vault innovator and data modeller specialising on all things Snowflake Data Cloud, Solution Architecture, Data Architecture and Business Architecture. Author of "Data Vault Guru: a pragmatic guide to building a data vault" Show less

How can I make my Data Vault sing on Snowflake? This may be a question you ask yourself as an experienced Data Vault practitioner or a new one.

In this presentation, Patrick Cuba, a leading thought-leader on the use of Data Vault and author of “The Data Vault Guru: A Pragmatic Guide on Building a Data Vault”, shows you how to do just that!

The Snowflake Senior Solutions Architect shares different techniques and ideas that you can use in your own Data Vault, as well as explaining… Show more

How can I make my Data Vault sing on Snowflake? This may be a question you ask yourself as an experienced Data Vault practitioner or a new one.

In this presentation, Patrick Cuba, a leading thought-leader on the use of Data Vault and author of “The Data Vault Guru: A Pragmatic Guide on Building a Data Vault”, shows you how to do just that!

The Snowflake Senior Solutions Architect shares different techniques and ideas that you can use in your own Data Vault, as well as explaining his new workshop which contains add-on information from the new features introduced by Snowflake. Show less

How experimenting with Dynamic Tables on Snowflake led to improvements in the Data Vault patterns.

We have seen enormous interest in Dynamic Tables for Snowflake. So what exactly are Dynamic Tables and where should they fit into your Data Vault architecture?

"The Truth about Data Modelling"

“Data Modelling and Design is one of the key knowledge areas within the DAMA wheel; it is described as the process of discovering, analysing and scoping data requirements and then representing those requirements in a precise form. The data modelling process itself designs how data fits together. Data modelling is a critical component of data management and yet today the discipline of data modelling is under attack, some predicting its death and some… Show more

"The Truth about Data Modelling"

“Data Modelling and Design is one of the key knowledge areas within the DAMA wheel; it is described as the process of discovering, analysing and scoping data requirements and then representing those requirements in a precise form. The data modelling process itself designs how data fits together. Data modelling is a critical component of data management and yet today the discipline of data modelling is under attack, some predicting its death and some stating it’s on life-support! We’ll discuss what I think of this assessment and where I think the state of data modelling is.” Show less

Join us online for a free, live session with Snowflake’s Senior Solutions Architect and Data Vault expert, Patrick Cuba.

Data leaders and architects across all industries have shown growing interest in Data Vault’s proven repeatable method for data warehouse development. In the public sector, Data Vault is particularly appealing to comply with historical record tracking and auditability requirements while maintaining focus on a business-centric solution. Data Vault also enables agile… Show more

Join us online for a free, live session with Snowflake’s Senior Solutions Architect and Data Vault expert, Patrick Cuba.

Data leaders and architects across all industries have shown growing interest in Data Vault’s proven repeatable method for data warehouse development. In the public sector, Data Vault is particularly appealing to comply with historical record tracking and auditability requirements while maintaining focus on a business-centric solution. Data Vault also enables agile data warehouse development and helps avoid reengineering when data sources are upgraded or replaced.

During this webinar, we’ll take a deep dive to showcase some of the advanced techniques in the Data Vault 2.0 methodology that Snowflake technology enriches. With a Data Vault model forming the bedrock of your Data Cloud platform, data engineers can rapidly ingest and integrate data sources while collaborating with data science and analytics teams to empower your data on Snowflake.

The webinar will include:

A quick overview of Data Vault 2.0
An introduction to Data Vault modeling in SqlDBM
Staging of data for Data Vault
Loading of Data Vault artifacts
Testing patterns of Data Vault (+Snowsight Dashboard)
Data Vault querying techniques
A live Q&A Show less

Data Vault explicitly defines a set of standards called “hard rules” to automate data extraction, cleaning, and modeling into raw vault tables. Data Vault’s “soft rules” are those auditable transformations that use raw (and other business vault) data artifacts whose outcomes are loaded into a business vault based on the same business entity and unit of work as raw vault. Data Vault as a practice does not stipulate how you transform your data, only that you follow the same standards to populate… Show more

Data Vault explicitly defines a set of standards called “hard rules” to automate data extraction, cleaning, and modeling into raw vault tables. Data Vault’s “soft rules” are those auditable transformations that use raw (and other business vault) data artifacts whose outcomes are loaded into a business vault based on the same business entity and unit of work as raw vault. Data Vault as a practice does not stipulate how you transform your data, only that you follow the same standards to populate business vault link and satellite tables as you would to populate raw vault link and satellite tables. Show less

Snowflake continues to set the standard for data in the cloud by removing the need to perform maintenance tasks on your data platform and giving you the freedom to choose your data model methodology for the cloud.

Snowflake continues to set the standard for data in the cloud by removing the need to perform maintenance tasks on your data platform and giving you the freedom to choose your data model methodology for the cloud. Cloud offers limitless scalability, and with Snowflake’s unique cloud-based architecture we can increase platform efficiency to make your Snowflake credits go even further with Data Vault.

Snowflake continues to set the standard for data in the cloud by removing the need to perform maintenance tasks on your data platform and giving you the freedom to choose your data model methodology for the cloud. One possible integration issue is the need to deal with a batch file that arrives out of sequence. Does that mean you need to roll back the previous day’s batch data to get the data sequence in order? Does it mean that the dashboard reports need to be rolled back and the corrections… Show more

Snowflake continues to set the standard for data in the cloud by removing the need to perform maintenance tasks on your data platform and giving you the freedom to choose your data model methodology for the cloud. One possible integration issue is the need to deal with a batch file that arrives out of sequence. Does that mean you need to roll back the previous day’s batch data to get the data sequence in order? Does it mean that the dashboard reports need to be rolled back and the corrections explained? Show less

Snowflake continues to set the standard for data in the cloud by removing the need to perform maintenance tasks on your data platform and giving you the freedom to choose your data model methodology for the cloud. There will be scenarios where you may need to consider transaction isolation, and such a scenario does exist for Data Vault: the common hub table.

Snowflake continues to set the standard for data in the cloud by removing the need to perform maintenance tasks on your data platform and giving you the freedom to choose your data model methodology for the cloud. Data Vault supports multi-tenancy and, combined with Snowflake’s Row Access Policy (RAP), you can simplify the authorization access of the Data Vault tables itself and make the process data-driven.

In today’s blog post we will explore the appropriate place to use a conditional multi-table INSERT for your Data Vault, and where not to use it.

When attempting to keep the cost of data processing low, both data volume and velocity can make things challenging. To remedy this, Snowflake introduced streams as a highly scalable data object to track change data capture (CDC) data activity in order to essentially process only new data into one or multiple data pipelines from a single data object. Let’s see how this technology can be used to bring Data Vault closer to a Kappa (streaming first) architecture.

In this blog post we will explain how to make use of a technique unique to Snowflake to efficiently query large satellite tables for the current record by a parent key (hub or link).

Data Vault models are not built for consumption by business intelligence (BI) tools, they are built for automation and agility while allowing auditability; making changes to a Data Vault model does not destroy the existing model; rather, it augments it. In order to simplify and enhance querying a Data Vault model, we will discuss why you could consider building Point-in-Time (PIT) and Bridge tables.

Join us online for a free, live session with Snowflake’s Resident Solution Architect and Data Vault expert, Patrick Cuba.

During this Zoom webinar, we’ll deep-dive to showcase some of the advanced techniques in the Data Vault 2.0 methodology that Snowflake technology enriches. With a Data Vault model forming the bedrock of your Data Cloud platform, Data Engineers can rapidly ingest and integrate data sources while collaborating with Data Science and Analytics teams to empower your data… Show more

Join us online for a free, live session with Snowflake’s Resident Solution Architect and Data Vault expert, Patrick Cuba.

During this Zoom webinar, we’ll deep-dive to showcase some of the advanced techniques in the Data Vault 2.0 methodology that Snowflake technology enriches. With a Data Vault model forming the bedrock of your Data Cloud platform, Data Engineers can rapidly ingest and integrate data sources while collaborating with Data Science and Analytics teams to empower your data on Snowflake.

YOU’LL LEARN HOW TO:
Quick overview of Data Vault 2.0
Visualise Data Vault model in SQLDBM
Staging of data for Data Vault
Loading of Data Vault Artefacts
Testing patterns of Data Vault (+Snowsight Dashboard)
Data Vault querying techniques
Live Q&A Show less

In today’s episode of Data Vault Mysteries we demystify Zero Keys and Ghost Records!

The two have been mentioned in literature, and often, their purpose has been defined interchangeably; however, they are in Data Vault 2.0, two very different concepts serving two very distinct purposes! Join me in the classroom as we unveil what each is and how they should be thought of in the context of data modeling!

On today’s episode of Data Vault Mysteries, we discuss Driving Keys and Effectivity Satellites! One of the most complex and often misunderstood Data Vault 2.0 artifacts is the beloved Effectivity Satellite.

There are Data Vault implementations parading as Data Vault 2.0 implementations but do not follow the DV2.0 standards. DV2.0 is more than just using surrogate hash keys and omitting end dates….
Here we explore at a high level the seven deadly sins of Fake Vault

How to build a Business Vault is a common question in the data vault community, the article dives into what it is and how to build a Business Vault

The title and subject of this discussion is the contextual nature of business keys, keys are the immutable value that represents a thing, entity, interest, or business object uniquely and is used by a business to identify that thing forever. However, a key by itself is meaningless…

A discussion on Cognitive load, Data Vault and Beer!

Solving technical debt using Apache Spark and Data Vault

The data vault methodology presents a unique opportunity to model the enterprise data warehouse using the same automation principles applicable in today’s software delivery, continuous integration, continuous delivery and continuous deployment while still maintaining the standards expected for governing a corporation’s most valuable asset: data. This book provides at first the landscape of a modern architecture and then as a thorough guide on how to deliver a data model that flexes as the… Show more

The data vault methodology presents a unique opportunity to model the enterprise data warehouse using the same automation principles applicable in today’s software delivery, continuous integration, continuous delivery and continuous deployment while still maintaining the standards expected for governing a corporation’s most valuable asset: data. This book provides at first the landscape of a modern architecture and then as a thorough guide on how to deliver a data model that flexes as the enterprise flexes, the data vault. Whether the data is structured, semi-structured or even unstructured one thing is clear, there is always a model either applied early (schema-on-write) or applied late (schema-on-read). Today’s focus on data governance requires that we know what we retain about our customers, the data vault provides that focus by delivering a methodology focused on all aspects about the customer and provides some of the best practices for modern day data compliance.
The book will delve into every data vault modelling artefact, its automation with sample code, raw vault, business vault, testing framework, a build framework, sample data vault models, how to build automation patterns on top of a data vault and even offer an extension of data vault that provides automated timeline correction, not to mention variation of data vault designed to provide audit trails, metadata control and integration with agile delivery tools. Show less

What plans are in place after you meet your demise? This subject is not a comfortable discussion to have for most. What happens to your belongings, your estate, and all that you leave behind? Who will be notified? And, what of those who depend on you? Graveyards paint a picture of dread in horror films and usually the protagonist is not at the cemetery by choice. Whether it was a death by natural causes or the consequences of unfortunate events; those that are affected are left behind to pick… Show more

What plans are in place after you meet your demise? This subject is not a comfortable discussion to have for most. What happens to your belongings, your estate, and all that you leave behind? Who will be notified? And, what of those who depend on you? Graveyards paint a picture of dread in horror films and usually the protagonist is not at the cemetery by choice. Whether it was a death by natural causes or the consequences of unfortunate events; those that are affected are left behind to pick up the pieces. Show less

Analytics assumes that data movement will follow a linear path into a historical repository to represent the correct sequence of events. That is, if today is Thursday and we have loaded data for Wednesday, we then do not expect data from Tuesday to arrive today, because all the data up until today should have already been loaded. Whether it be a missing batch file or a missing data record – a Tuesday file may have arrived and loaded but could have been provided without a complete set of… Show more

Analytics assumes that data movement will follow a linear path into a historical repository to represent the correct sequence of events. That is, if today is Thursday and we have loaded data for Wednesday, we then do not expect data from Tuesday to arrive today, because all the data up until today should have already been loaded. Whether it be a missing batch file or a missing data record – a Tuesday file may have arrived and loaded but could have been provided without a complete set of records. There are a myriad of reasons why these scenarios might occur (a locked record due to an update in the database may cause a push file produced from the source system to be incomplete). These are real scenarios in loading a data warehouse and it can skew what we know of a business entity (e.g. customer) and may derive erroneous analytics for that business entity or worse, the wrong facts are reported to a regulatory body or to the customers themselves. Show less

Data Vault (DV) modelling technique is fast gaining popularity around the world as an easy to learn, easy to
manage, easy to maintain and easy to adapt (to business change) data modelling technique. In this paper
we will briefly explore what DV is; DV artifacts and we will explore how SAS can be used to automate its
data loading patterns.

No Batch Scheduler? No problem! This paper describes the use of a SAS DI Studio job that can be
started by a time dependent scheduler like Windows Scheduler (or crontab in UNIX) to mimic a batch
scheduler on SAS Grid.