The Data Warrior

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Archive for the tag “#datamesh”

Panel Discussion: Data Modeling in a Data Mesh World

Happy New Year y’all!

Last month I had the pleasure of chatting with two of my data modeling pals about how to think about data modeling in a #DataMesh architecture. The recording was hosted by the Data Mesh Learning Community in partnership with Data Mesh Radio.

Juha Korpela (Chief Product Officer at Ellie Technologies) facilitated this panel with Veronika Durgin (Head of Data at Saks) and myself (The Data Warrior). Watch (or listen) to learn why Veronika and I consider Data Vault to be the best choice available right now, why only focusing on the technical aspects of data modeling instead of the business will lead you astray, how to build in iterations, change management for data modeling, and much more!

Watch the video replay here: Panel: Data Modeling in Data Mesh

If you want to just listen, check out the podcast audio, which has some nice notes from Scott Hirleman from the Data Mesh Learning Community.

Enjoy!

Kent

The Data Warrior

The Role of DataOps in a Data Mesh Architecture

Hot off the press – a new post discussing two of today’s hottest topics – #DataMesh and #DataOps.

Data Mesh is a decentralized architecture that organizes data by specific business domains and teams, leveraging a self-service approach. It’s designed to provide greater ownership and responsibility to Data Product teams and, it is hoped, more effective outputs in a timely manner. 

Data mesh is founded on four pillars: 

  • Empowered domain ownership – letting the experts who know the data best do their jobs. 
  • Cross organizational transport – enabling consumption through data products. 
  • Self-service analytical platform – reducing costs, improving agility. 
  • Federated governance – the ‘glue’ that holds the process together, enabling interoperability and compliance. 

Is DataOps simply an enabler for more sophisticated design patterns like Data Mesh?  If we carry on with DevOps thinking – considering how the involvement of end users in a product lifecycle went from passive to truly active, one starts to see that it’s more than that. 

Read the rest of the post here – The Role of DataOps in a Data Mesh Architecture

Happy reading.

Kent

The Data Warrior

The Hype:  Data Mesh Will Become Obsolete – Really?

Data Mesh is indeed a hot topic these days. Is it the answer to all our big data problems? Probably not, but it will help some organizations. Is it obsolete? See what I think in this recent blog post…

In the summer of 2022, Gartner® published its Hype Cycle on Data Management, and this was not without some controversy.

In particular, it asserted that Data Mesh will be obsolete before it reaches what Gartner calls the Plateau of Productivity. It is hard to fathom how they came to that conclusion when there is such a vibrant Data Mesh Community complete with a very active Slack channel, and when DataOps.live customers like Roche and OneWeb have successfully built a Data Mesh using DataOps.live and Snowflake. But we’ll come back to that in a moment.

Data Mesh is in essence a conceptual approach and architectural framework, not a specific off-the-shelf tool or technology. One of the main precepts for its creation is angled towards improving productivity and time to value by eliminating the perceived bottlenecks that have been experienced by organizations building out large-scale enterprise data warehouses and data lakes.  Zhamak Deghani, the creator of the Data Mesh concept, has referred to it as a decentralized sociotechnical approach – meaning it involves people, processes and technology. So, it seems likely that no two implementations will be the same by their nature. How one organization defines productivity based on its specific needs and outputs on its own application of Data Mesh will differ from another. Yes, Data Mesh is (kind of) new and yes, it is maturing, but that does not mean we should ignore or discount it this early in the game.

It is hard these days to separate the hype from the reality so check out the entire post over at the DataOps.live blog.

Cheers.

Kent

The Data Warrior

Data Vault and Data Mesh – A Match Made in the Cloud?

Check out my latest thoughts about Data Vault and Data Mesh:

Over the last six years (after I joined Snowflake basically), I have witnessed a massive increase in the interest and implementation of Data Vault 2.0. I have talked to literally hundreds of companies across the globe and across all industries about changing their approach to building an enterprise data platform. It was sort of mind boggling how many folks wanted to speak to me about this. So why, after almost two decades of successful data vault implementations, have so many people “suddenly” got interested in Data Vault?

Well, a few reasons:

1. They are moving to the cloud (in this case, Snowflake) and figured it was time to look at their approach to data warehousing and data lakes.

2. What they had been doing for decades, on critical review, really was not working (i.e., lots of expensive re-engineering all the time) and definitely could not scale.

3. Things are changing so rapidly, they needed to find a way to be more agile.

Read the rest of the post here on the Data Rebels site to find out how Data Vault relates to Data Mesh – Data Vault and Data Mesh

What do you think?

Have a great week!

Kent

The Data Warrior

Data Mesh Learning – Interview with The Data Warrior

Last week I had the privilege of being interviewed by Nick Heudecker (former Gartner analyst and current Senior Director at Cribl) for the Data Mesh Learning Community. In our interview, we covered the idea of empowering business domains to really own and manage their data via things like templates and a center of excellence, not to just give them the responsibility of owning their data and leaving them to figure the rest out on their own. We also discussed the need for organizations to focus on investing in growing a data culture, not just investing in the newest cloud based tooling. Really, how do we lower the barriers to accessing, sharing, and leveraging data and get people to really think about data-as-a-product.

Like Agile before it, Data Mesh is as much about changing the way an organization thinks and works as it is about technology. I argue that the people and organization aspects of adopting a data mesh approach are more important than the technology aspects. Without the right approach, the best technology (like Snowflake), is not going to solve your organization’s data problems.

See the full interview here:

So what do you think about all this data mesh stuff?

Cheers!

Kent

The Data Warrior

P.S. For much more on the thoughts about #datamesh, check the other podcasts and videos listed on my Snowflake Resources page.

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