The Data Warrior

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Archive for the tag “Data Vault”

16 Dec 2021
12 Comments

3 Key Resources for Data Vault on Snowflake

By now you surely know that you can build a Data Vault on Snowflake. In fact we have many customers doing so today. So much so that we formed a Snowflake Data Vault User Group.

Over the years I have had hundreds of calls and meetings with organizations around the world discussing this topic from just basic Data Vault 101 type questions to best practices to who is doing Data Vault on Snowflake. Because of that we developed a Data Vault Resource Kit that points you to all the key blog posts, videos, and customer stories on the topic (scroll down to see everything!). Be sure to bookmark that page. Most of your questions on this topic can be answered there.

To take it a step further and to a deeper level, I partnered up with Snowflake Field CTO Dmytro Yarashneko (CDVP2) and wrote a post with reference architectures and discussions related to doing real time feeds into a Data Vault 2.0 on Snowflake. Check that out here. This article even has code!

And, at long last, for those that want to jump in feet first and try it for yourself, the team built a Data Vault Quickstart , based on the above article and a hands on lab from WWDVC 2021, that gives you a step-by-step guide and all the code to build and load a Data Vault 2.0 system, including an information mart on top of the Data Vault, all in your very own Snowflake account.

So, what is your excuse now? You have all the resources you need to give it a go!

And please, bookmark this post and/or the links above so you don’t lose them!

Model on!

Kent

The Data Warrior

Posted in Data Vault, SnowflakeDB and tagged , , , , , , , ,
19 Apr 2021

Building a Real-time Data Vault in Snowflake?

Yes you can! The #DataCloud loves #DataVault!

In this day and age, with the ever-increasing availability and volume of data from many types of sources such as IoT, mobile devices, and weblogs, there is a growing need, and yes, demand, to go from batch load processes to streaming or “real-time” (RT) loading of data. Businesses are changing at an alarming rate and are becoming more competitive all the time. Those that can harness the value of their data faster to drive better business outcomes will be the ones to prevail.

One of the benefits of using the Data Vault 2.0 architecture is that it was designed from inception not only to accept data loaded using traditional batch mode (which was the prevailing mode in the early 2000s when Dan introduced Data Vault) but also to easily accept data loading in real or near-realtime (NRT). In the early 2000s, that was a nice-to-have aspect of the approach and meant the methodology was effectively future-proofed from that perspective. Still, few database systems had the capacity to support that kind of requirement. Today, RT or at least NRT loading is almost becoming a mandatory requirement for modern data platforms. Granted, not all loads or use cases need to be NRT, but most forward-thinking organizations need to onboard data for analytics in an NRT manner.

See all the details (and some code) in the full post over on Data Vault Alliance.

Happy Vaulting!

Kent

The Data Warrior

Posted in Data Vault, SnowflakeDB and tagged , , , , , , , , , , | Leave a comment
11 Dec 2020

Data Vault 2.0 Automation with erwin and Snowflake

I am seeing a HUGE uptick in interest in Data Vault around the globe. Part of the interest is the need for agility in building a modern data platform. One of the benefits of the Data Vault 2.0 method is the repeatable patterns which lend themselves to automation.  I am please to pass on this great new post with details on how to automate building your Data Vault 2.0 architecture on Snowflake using erwin! Thanks to my buddy John Carter at erwin for taking this project on.

The Data Vault methodology can be applied to almost any data store and populated by almost any ETL or ELT data integration tool. As Snowflake Chief Technical Evangelist Kent Graziano mentions in one of his many blog posts, “DV (Data Vault) was developed specifically to address agility, flexibility, and scalability issues found in the other mainstream data modeling approaches used in the data warehousing space.” In other words, it enables you to build a scalable data warehouse that can incorporate disparate data sources over time. Traditional data warehousing typically requires refactoring to integrate new sources, but when implemented correctly, Data Vault 2.0 requires no refactoring.

Successfully implementing a Data Vault solution requires skilled resources and traditionally entails a lot of manual effort to define the Data Vault pipeline and create ETL (or ELT) code from scratch. The entire process can take months or even years, and it is often riddled with errors, slowing down the data pipeline. Automating design changes and the code to process data movement ensures organizations can accelerate development and deployment in a timely and cost-effective manner, speeding the time to value of the data.

Snowflake’s Data Cloud contains all the necessary components for building, populating, and managing Data Vault 2.0 solutions. erwin’s toolset models, maps, and automates the creation, population, and maintenance of Data Vault solutions on Snowflake. The combination of Snowflake and erwin provides an end-to-end solution for a governed Data Vault with powerful performance.

Get the rest of the details here: Data Vault Automation with erwin and Snowflake

Vault away my friends!

Kent

The Data Warrior

Posted in Data Vault, SnowflakeDB and tagged , , , , , , , , , , , , , | Leave a comment
17 Apr 2020

Tips for Optimizing the #DataVault Architecture on Snowflake (Part 3) 

For this last post in my current Data Vault (DV) series, I will discuss two more cool features of Snowflake Cloud Data Platform (@Snowflakedb) that you can take advantage of when building a DV on our platform. If you are not familiar with the DV method, please read my introductory blog post and part 1 of this series before reading this post.

Get the details here: Tips for Optimizing the Data Vault Architecture on Snowflake (Part 3)

Happy Data Vaulting!

Kent

The Data Warrior

Posted in #cloud, Data Modeling, Data Vault, Data Warehouse, SnowflakeDB and tagged , , , , , , , , | Leave a comment
18 Mar 2020

Tips for Optimizing the #DataVault Architecture on #Snowflake (Part 2)

SETTING UP FOR MAXIMAL PARALLEL LOADING!

In this post, I discuss how to engineer your Data Vault load in Snowflake Cloud Data Platform for maximum speed.

Because Snowflake separates compute from storage and allows the definition of multiple independent compute clusters, it provides some truly unique opportunities to configure virtual warehouses to support optimal throughput of DV loads.

Along with using larger “T-shirt size” warehouses to increase throughput, using multi-cluster warehouses during data loading increases concurrency for even faster loads at scale.

Get the details –  Tips for Optimizing the Data Vault Architecture on Snowflake (Part 2)

Enjoy!

Kent

The Data Warrior & Chief Technical Evangelist for Snowflake

Posted in Data Modeling, Data Vault, Data Warehouse, SnowflakeDB and tagged , , , , , , , , , ,

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