Today is New Year's Eve and we're somehow at the end of 2025.
Time truly flies.
But what better time to look back on the last year than...today?
So that's what I wanted to do in this week's video.
Today, I'm going to recap: - Some of the big happenings over the past 12 months - Common things I've noticed with clients - Some personal reflections as we head into 2026.
We'll cover things like AI, the dbt/Fivetran Merger and why access to unlimited information won't solve everything.
My hope is that by going through some of those things, they might be helpful for you as well.
Or simply serve as a good reminder to look back on in the future.
As data people, we naturally understand the value of what we’re building.
We’re often the first to see new information and the insights hiding inside it.
In many cases, we even have a better pulse on what’s really happening in the business than some executives do.
But despite that, we still usually have to convince someone else in the organization that this is a worthwhile place to invest time, money, and resources.
This is an especially common conversation for teams early in their data journey.
So in this video, I want to share a few ways to think about approaching that conversation more effectively.
I’ll also walk through some specific angles you can lean into to help get your point across.
If you're leading a small data team, building an architecture is both art and science.
On one hand, it's all about proper design and functionality.
And on the other, it's realistically MORE about budget.
Leadership doesn't want you overspending on tools you don't need.
This is a concern that comes up a lot with clients and a conversation I've had multiple times over the past year alone.
I find it most often comes up when talking about database selection.
So in this video I'm going to talk through the way I often approach that conversation.
In particular, we'll talk through the pricing considerations for using a "modern" tool like Snowflake vs. a more traditional option like Postgres or MySQL.
Along with some ways to limit costs to avoid blowing up the budget.
By the end, my hope is to help you come to your own conclusions on whether or not using a modern database is the right fit for you.
A common approach to building a data pipeline is to follow what's known as a Medallion Architecture.
At a high level, this gives you 3 clear layers for which to move your data along in the pipeline.
Bronze - Silver - Gold.
This might seem pretty straightforward when I just explain it like that.
But a lot of teams I speak to actually struggle to implement this correctly.
For example, they're not sure if they're building things in the right layers.
Or the naming gets thrown off to the point where it actually ends up becoming more complicated than it needs to be.
So in this video we'll talk a little bit more about this concept.
We'll cover: - What the Medallion Architecture is - Where it fits in the grand scheme of what we do as data people - A slightly alternative approach you could take
Kahan Data Solutions
Kahan Data Solutions
The topic of data modeling gets a lot of attention in our world of data engineering.
...But for good reason.
It's an area where we spend most of our time each day.
Plus it can make or break the success of a team and their architecture long-term.
But why do we spend so much time on this?
Why is it actually important?
That's what I'd like to talk through today.
In this video, I'll share 3 reasons why data modeling is STILL so important for any team.
And why you might want to consider giving it that attention if you're not already.
Enjoy!
5 days ago | [YT] | 2
View 0 replies
Kahan Data Solutions
Most data teams understand the idea of a Development environment.
It's a place to test changes and make sure you're not breaking anything before it gets to Production.
But while teams understand this in theory, quite a few still struggle to properly implement it.
And more specifically, I'm talking about how it's handled within the database itself.
What I've noticed is teams are quick to point to their central code base (ex. GitHub, BitBucket, etc.).
They have a Prod vs Dev environment or branching strategy in place.
But what's missing is a how these objects (ex. tables, views, etc.) actually get deployed.
How different environments look and feel in the database.
Not just how code is managed.
It's a bit of both art and science.
But the goal is to align your environment-specific code with the objects in the database itself.
So what I want to do in this video is give a brief overview of 3 environments I recommend for clients.
By the end, you can determine if you already have these in place today.
Or if there's an opportunity to rework your database layout for environments.
Enjoy!
1 week ago (edited) | [YT] | 7
View 0 replies
Kahan Data Solutions
If you're on a small data team or you're the only engineer at the whole company...
It's easy to feel like you can just look past certain things.
To believe some tasks are just more work than it's worth because of how small the team is.
(You're the only one who sees it anyway!)
But one spot I find this happens quite a bit in the area of Version Control.
And in particular, the git workflow of development.
In my opinion, overlooking this is actually a critical error.
I'd argue it's something that should be taken seriously whether you are a 1-person data team or a 100 person data team.
So in this video I want to talk through some of the common pitfalls I see small teams making as it relates to Version Control and the git workflow.
Along with some reasons why you might want to consider taking it more seriously, if you haven't already.
Enjoy!
2 weeks ago | [YT] | 4
View 0 replies
Kahan Data Solutions
Today is New Year's Eve and we're somehow at the end of 2025.
Time truly flies.
But what better time to look back on the last year than...today?
So that's what I wanted to do in this week's video.
Today, I'm going to recap:
- Some of the big happenings over the past 12 months
- Common things I've noticed with clients
- Some personal reflections as we head into 2026.
We'll cover things like AI, the dbt/Fivetran Merger and why access to unlimited information won't solve everything.
My hope is that by going through some of those things, they might be helpful for you as well.
Or simply serve as a good reminder to look back on in the future.
Enjoy and Happy New Year!
3 weeks ago | [YT] | 6
View 0 replies
Kahan Data Solutions
As data people, we naturally understand the value of what we’re building.
We’re often the first to see new information and the insights hiding inside it.
In many cases, we even have a better pulse on what’s really happening in the business than some executives do.
But despite that, we still usually have to convince someone else in the organization that this is a worthwhile place to invest time, money, and resources.
This is an especially common conversation for teams early in their data journey.
So in this video, I want to share a few ways to think about approaching that conversation more effectively.
I’ll also walk through some specific angles you can lean into to help get your point across.
Enjoy.
1 month ago | [YT] | 7
View 0 replies
Kahan Data Solutions
If you're leading a small data team, building an architecture is both art and science.
On one hand, it's all about proper design and functionality.
And on the other, it's realistically MORE about budget.
Leadership doesn't want you overspending on tools you don't need.
This is a concern that comes up a lot with clients and a conversation I've had multiple times over the past year alone.
I find it most often comes up when talking about database selection.
So in this video I'm going to talk through the way I often approach that conversation.
In particular, we'll talk through the pricing considerations for using a "modern" tool like Snowflake vs. a more traditional option like Postgres or MySQL.
Along with some ways to limit costs to avoid blowing up the budget.
By the end, my hope is to help you come to your own conclusions on whether or not using a modern database is the right fit for you.
Enjoy!
1 month ago | [YT] | 11
View 0 replies
Kahan Data Solutions
Building a solid data architecture is more than just the tools you pick.
Or the skills that you have.
A lot of times it has to do with your overall perspective and viewpoint on analytics as a whole.
Personally, much of my own thinking on this topic has been heavily influenced by the content put out by the folks at dbt Labs.
So in today's video, I'm actually going share with you a lesson directly from a training.
And despite the fact that this video was recorded 3-4 years ago, the concepts here are still as relevant as ever.
Whether or not you use dbt yourself, the ideas and strategies discussed will help you improve as a data engineer going forward no matter what.
Enjoy!
1 month ago (edited) | [YT] | 8
View 0 replies
Kahan Data Solutions
A common approach to building a data pipeline is to follow what's known as a Medallion Architecture.
At a high level, this gives you 3 clear layers for which to move your data along in the pipeline.
Bronze - Silver - Gold.
This might seem pretty straightforward when I just explain it like that.
But a lot of teams I speak to actually struggle to implement this correctly.
For example, they're not sure if they're building things in the right layers.
Or the naming gets thrown off to the point where it actually ends up becoming more complicated than it needs to be.
So in this video we'll talk a little bit more about this concept.
We'll cover:
- What the Medallion Architecture is
- Where it fits in the grand scheme of what we do as data people
- A slightly alternative approach you could take
Enjoy!
2 months ago | [YT] | 19
View 0 replies
Kahan Data Solutions
Creating any data architecture involves A LOT of moving parts.
So it's understandable that sometimes things get a little bit out of sync.
With that said, this is something I notice happens often with architectures built around dbt.
So what I'd like to do in this video is get back to basics a little bit.
To talk through ways to align your dbt project with your database setup.
And with your architecture as a whole.
By the end, my hope is to help you validate (or course-correct) your current design as it exists today.
Or if you're starting from scratch...
To give you an idea of how to approach it from the ground up.
Enjoy!
2 months ago | [YT] | 10
View 0 replies
Kahan Data Solutions
When I talk to teams about their data architecture, an area that I like to focus on is workflow.
But when we say "workflow", what does that really mean?
It's an area that isn't just a specific tool or a line of code...
But more about a strategy and process in the middle of all the action.
It can also be a bit of a gray area.
But it's something that's incredibly important, especially for teams looking to migrate to a more modern approach.
So in this video are some examples of things that come to mind when I talk about "data workflow".
Whether you're looking to build or upgrade something you have in place, it will help you think about ways to approach this topic.
Enjoy!
2 months ago | [YT] | 3
View 0 replies
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