Supporting a World-class Documentation Technique with Atlan
The Lively Metadata Pioneers sequence options Atlan clients who’ve accomplished an intensive analysis of the Lively Metadata Administration market. Paying ahead what you’ve discovered to the following 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, revolutionary use circumstances for metadata, and extra.
On this installment of the sequence, we meet Tina Wang, Analytics Engineering Supervisor at Tala, a digital monetary companies 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 sturdy 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 excited about 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 house ever since. My skilled background has been in start-ups, and in previous expertise, I’ve at all times been the primary particular person on the info group, which incorporates establishing all of the infrastructure, constructing reviews, discovering insights, and plenty of communication with folks. 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 expertise and human creativity to unravel what legacy establishments can’t or received’t, with a purpose to unleash the financial energy of the International 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 straightforward to seek out and prepared for use. We’re right here to verify knowledge is clear, dependable, and reusable, so analysts on groups like Advertising and Operations can deal with evaluation and producing insights.
What does your knowledge stack appear 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 recordsdata from dbt and robotically 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 info, it simply flows by way of 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 consider we have been one in all 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 outdated 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 aim is to doc each single column and desk that’s uncovered to the tip person, and Atlan has been fairly useful for us. It’s very simple 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 folks info.”
For the Analytics Engineering group, we’re usually the curator for that documentation. After we construct tables, we sync with the service house owners who created the DB to grasp the schema, and once 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 selected 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 expounded to a different desk and what are the codes that generate the desk. One of the best factor about lineage is it’s totally automated. It has been very useful in knowledge exploration when somebody is just not accustomed to a brand new knowledge supply.
What’s subsequent for you and your group? Something you’re enthusiastic about constructing?
We now have been wanting into the dbt semantic layer up to now yr. It’s going to 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 prime of the presentation layer to make reporting and visualizations extra seamless.
Do you’ve any recommendation to share along with your friends from this expertise?
Doc. Positively doc.
In one in all 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 would dwell in a Google spreadsheet, which isn’t actually sustainable for bigger organizations with thousands and thousands 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 outdated infrastructure for a yr, which was not wasted time as a result of as we’re constructing the brand new infrastructure, it’s simple for us to refer again to the outdated documentation.
So, I actually emphasize documentation. While you begin is the time and the place to essentially centralize your data, so each time somebody leaves, the data stays, and it’s a lot simpler for brand new folks to onboard. No person has to play guessing video games. It’s centralized, and there’s no query.
Typically totally different groups have totally different definitions for related phrases. And even in these circumstances, we’ll use the SQL to doc so we are able to say “That is the system that derives this definition of Revenue.”
You need to depart little or no room for misinterpretation. That’s actually what I’d like to emphasise.
The rest you’d wish to share?
I nonetheless have the spreadsheet from two years in the past once I seemed for documentation instruments. I did lots of market analysis, 20 totally different distributors and each instrument I might discover. What was essential to me was discovering a platform that would hook up with all of the instruments I used to be already utilizing, which have been dbt, Snowflake, and Looker, and that had a powerful assist group. I knew that once we first onboarded, we’d have questions, and we might be establishing lots of permissions and knowledge connections, and {that a} sturdy assist group can be very useful.
I remembered once we first labored with the group, everyone 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 at all times proud that I discovered and selected Atlan.
Photograph by Priscilla Du Preez 🇨🇦 on Unsplash