Supporting a World-class Documentation Technique with Atlan
The Lively Metadata Pioneers collection options Atlan clients who’ve accomplished a radical analysis of the Lively Metadata Administration market. Paying ahead what you’ve discovered to the subsequent knowledge chief is the true spirit of the Atlan neighborhood! In order that they’re right here to share their hard-earned perspective on an evolving market, what makes up their fashionable knowledge stack, progressive use instances for metadata, and extra.
On this installment of the collection, we meet Tina Wang, Analytics Engineering Supervisor at Tala, a digital monetary providers platform with eight million clients, named to Forbes’ FinTech 50 checklist for eight consecutive years. She shares their two-year journey with Atlan, and the way their robust tradition of documentation helps their migration to a brand new, state-of-the-art knowledge platform.
This interview has been edited for brevity and readability.
Might you inform us a bit about your self, your background, and what drew you to Knowledge & Analytics?
From the start, I’ve been very fascinated with enterprise, economics, and knowledge, and that’s why I selected to double main in Economics and Statistics at UCLA. I’ve been within the knowledge area ever since. My skilled background has been in start-ups, and in previous expertise, I’ve all the time been the primary particular person on the information group, which incorporates organising all of the infrastructure, constructing reviews, discovering insights, and plenty of communication with individuals. At Tala, I get to work with a group to design and construct new knowledge infrastructure. I discover that work tremendous fascinating and funky, and that’s why I’ve stayed on this subject.
Would you thoughts describing Tala, and the way your knowledge group helps the group?
Tala is a FinTech firm. At Tala, we all know in the present day’s monetary infrastructure doesn’t work for a lot of the world’s inhabitants. We’re making use of superior know-how and human creativity to unravel what legacy establishments can’t or gained’t, in an effort to unleash the financial energy of the World Majority.
The Analytics Engineering group serves as a layer between back-end engineering groups and varied Enterprise Analysts. We construct infrastructure, we clear up knowledge, we arrange duties, and we be certain knowledge is simple to seek out and prepared for use. We’re right here to ensure knowledge is clear, dependable, and reusable, so analysts on groups like Advertising and Operations can concentrate on evaluation and producing insights.
What does your knowledge stack seem like?
We primarily use dbt to develop our infrastructure, Snowflake to curate, and Looker to visualise. It’s been nice that Atlan connects to all three, and helps our technique of documenting YAML information from dbt and routinely syncing them to Snowflake and Looker. We actually like that automation, the place the Analytics Engineering group doesn’t want to enter Atlan to replace data, it simply flows via from dbt and our enterprise customers can use Atlan instantly as their knowledge dictionary.
Might you describe your journey with Atlan, thus far? Who’s getting worth from utilizing it?
We’ve been with Atlan for greater than two years, and I imagine we had been one among your earlier customers. It’s been very, very useful.
We began to construct a Presentation Layer (PL) with dbt one yr in the past, and beforehand to that, we used Atlan to doc all our previous infrastructure manually. Earlier than, documentation was inconsistent between groups and it was usually difficult to chase down what a desk or column meant.
Now, as we’re constructing this PL, our objective is to doc each single column and desk that’s uncovered to the tip person, and Atlan has been fairly helpful for us. It’s very straightforward to doc, and really simple for the enterprise customers. They’ll go to Atlan and seek for a desk or a column, they will even seek for the outline, saying one thing like, “Give me all of the columns which have individuals data.”
For the Analytics Engineering group, we’re usually the curator for that documentation. Once we construct tables, we sync with the service homeowners who created the DB to grasp the schema, and after we construct columns we set up them in a reader-friendly method and put it right into a dbt YAML file, which flows into Atlan. We additionally go into Atlan and add in Readmes, in the event that they’re wanted.
Enterprise customers don’t use dbt, and Atlan is the one method for them to entry Snowflake documentation. They’ll go into Atlan and seek for a specific desk or column, can learn the documentation, and might discover out who the proprietor is. They’ll additionally go to the lineage web page to see how one desk is said to a different desk and what are the codes that generate the desk. One of the best factor about lineage is it’s absolutely automated. It has been very useful in knowledge exploration when somebody shouldn’t be conversant in a brand new knowledge supply.
What’s subsequent for you and your group? Something you’re enthusiastic about constructing?
Now we have been trying into the dbt semantic layer prior to now yr. It can assist additional centralize enterprise metric definitions and keep away from duplicated definitions amongst varied evaluation groups within the firm. After we largely end our presentation layer, we’ll construct the dbt semantic layer on high of the presentation layer to make reporting and visualizations extra seamless.
Do you might have any recommendation to share together with your friends from this expertise?
Doc. Positively doc.
In one among my earlier jobs, there was zero documentation on their database, however their database was very small. As the primary rent, I used to be a powerful advocate for documentation, so I went in to doc the entire thing, however that might dwell in a Google spreadsheet, which isn’t actually sustainable for bigger organizations with tens of millions of tables.
Coming to Tala, I discovered there was a lot knowledge, it was difficult to navigate. That’s why we began the documentation course of earlier than we constructed the brand new infrastructure. We documented our previous infrastructure for a yr, which was not wasted time as a result of as we’re constructing the brand new infrastructure, it’s straightforward for us to refer again to the previous documentation.
So, I actually emphasize documentation. If you begin is the time and the place to actually centralize your information, so at any time when somebody leaves, the information stays, and it’s a lot simpler for brand spanking new individuals to onboard. No one has to play guessing video games. It’s centralized, and there’s no query.
Generally completely different groups have completely different definitions for comparable phrases. And even in these instances, we’ll use the SQL to doc so we are able to say “That is the formulation that derives this definition of Revenue.”
You need to go away little or no room for misinterpretation. That’s actually what I’d like to emphasise.
The rest you’d prefer to share?
I nonetheless have the spreadsheet from two years in the past after I seemed for documentation instruments. I did numerous market analysis, 20 completely different distributors and each device I might discover. What was essential to me was discovering a platform that might connect with all of the instruments I used to be already utilizing, which had been dbt, Snowflake, and Looker, and that had a powerful help group. I knew that after we first onboarded, we might have questions, and we’d be organising numerous permissions and knowledge connections, and {that a} robust help group can be very useful.
I remembered after we first labored with the group, all people that I interacted with from Atlan was tremendous useful and really beneficiant with their time. Now, we’re just about working by ourselves, and I’m all the time proud that I discovered and selected Atlan.
Photograph by Priscilla Du Preez 🇨🇦 on Unsplash