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 information chief is the true spirit of the Atlan group! So that they’re right here to share their hard-earned perspective on an evolving market, what makes up their trendy information 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 providers platform with eight million clients, named to Forbes’ FinTech 50 record 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 information platform.
This interview has been edited for brevity and readability.
May you inform us a bit about your self, your background, and what drew you to Knowledge & Analytics?
From the start, I’ve been very interested by enterprise, economics, and information, and that’s why I selected to double main in Economics and Statistics at UCLA. I’ve been within the information house ever since. My skilled background has been in start-ups, and in previous expertise, I’ve all the time been the primary individual on the info crew, which incorporates organising all of the infrastructure, constructing studies, discovering insights, and many communication with folks. At Tala, I get to work with a crew to design and construct new information 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 information crew helps the group?
Tala is a FinTech firm. At Tala, we all know in the present day’s monetary infrastructure doesn’t work for many of the world’s inhabitants. We’re making use of superior expertise and human creativity to resolve what legacy establishments can’t or received’t, with a view to unleash the financial energy of the World Majority.
The Analytics Engineering crew serves as a layer between back-end engineering groups and varied Enterprise Analysts. We construct infrastructure, we clear up information, we arrange duties, and we be certain that information is straightforward to search out and prepared for use. We’re right here to verify information is clear, dependable, and reusable, so analysts on groups like Advertising and marketing and Operations can concentrate on evaluation and producing insights.
What does your information stack appear to be?
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 means of documenting YAML recordsdata from dbt and routinely syncing them to Snowflake and Looker. We actually like that automation, the place the Analytics Engineering crew doesn’t want to enter Atlan to replace data, it simply flows by means of from dbt and our enterprise customers can use Atlan immediately as their information dictionary.
May you describe your journey with Atlan, to this point? Who’s getting worth from utilizing it?
We’ve been with Atlan for greater than two years, and I imagine we have been one in every of 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 purpose is to doc each single column and desk that’s uncovered to the top 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’ll even seek for the outline, saying one thing like, “Give me all of the columns which have folks data.”
For the Analytics Engineering crew, we’re usually the curator for that documentation. Once we construct tables, we sync with the service homeowners who created the DB to know the schema, and once we construct columns we manage 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 may 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 totally automated. It has been very useful in information exploration when somebody isn’t conversant in a brand new information supply.
What’s subsequent for you and your crew? Something you’re enthusiastic about constructing?
We now have been wanting into the dbt semantic layer prior 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 principally 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’ve any recommendation to share together with your friends from this expertise?
Doc. Undoubtedly doc.
In one in every of 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 robust advocate for documentation, so I went in to doc the entire thing, however that would stay 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 information, 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 simple for us to refer again to the previous documentation.
So, I actually emphasize documentation. Once you begin is the time and the place to essentially centralize your data, so every 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 completely different groups have completely 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 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.
Anything 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, taking a look at 20 completely different distributors and each software 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 robust assist crew. I knew that once we first onboarded, we might have questions, and we might be organising numerous permissions and information connections, and {that a} robust assist crew can be very useful.
I remembered once we first labored with the crew, all people that I interacted with from Atlan was tremendous useful and really beneficiant with their time. Now, we’re just about operating by ourselves, and I’m all the time proud that I discovered and selected Atlan.
Picture by Priscilla Du Preez 🇨🇦 on Unsplash
