Supporting a World-class Documentation Technique with Atlan
The Lively Metadata Pioneers collection options Atlan prospects who’ve accomplished a radical analysis of the Lively Metadata Administration market. Paying ahead what you’ve realized to the subsequent knowledge 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 knowledge stack, progressive use circumstances for metadata, and extra.
On this installment of the collection, we meet Tina Wang, Analytics Engineering Supervisor at Tala, a digital monetary companies platform with eight million prospects, 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.
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 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 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 staff, which incorporates establishing all of the infrastructure, constructing experiences, discovering insights, and many communication with folks. At Tala, I get to work with a staff to design and construct new knowledge infrastructure. I discover that work tremendous attention-grabbing and funky, and that’s why I’ve stayed on this area.
Would you thoughts describing Tala, and the way your knowledge staff 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 unravel what legacy establishments can’t or gained’t, with the intention to unleash the financial energy of the World Majority.
The Analytics Engineering staff serves as a layer between back-end engineering groups and numerous 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 ensure knowledge 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 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 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 staff doesn’t want to enter Atlan to replace data, it simply flows via from dbt and our enterprise customers can use Atlan straight as their knowledge dictionary.
May you describe your journey with Atlan, to date? Who’s getting worth from utilizing it?
We’ve been with Atlan for greater than two years, and I consider we had been certainly one 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 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 top person, and Atlan has been fairly helpful for us. It’s very simple to doc, and really easy 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 data.”
For the Analytics Engineering staff, 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 after we construct columns we arrange 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 approach for them to entry Snowflake documentation. They’ll go into Atlan and seek for a specific 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 expounded 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 isn’t acquainted with a brand new knowledge supply.
What’s subsequent for you and your staff? Something you’re enthusiastic about constructing?
We’ve been trying into the dbt semantic layer prior to now yr. It’ll assist additional centralize enterprise metric definitions and keep away from duplicated definitions amongst numerous evaluation groups within the firm. After we largely end our presentation layer, we are going to construct the dbt semantic layer on prime of the presentation layer to make reporting and visualizations extra seamless.
Do you might have any recommendation to share along with your friends from this expertise?
Doc. Undoubtedly doc.
In certainly one 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 powerful advocate for documentation, so I went in to doc the entire thing, however that would reside 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 actually centralize your information, so at any time when somebody leaves, the information stays, and it’s a lot simpler for brand new folks to onboard. No one has to play guessing video games. It’s centralized, and there’s no query.
Typically totally different groups have totally different definitions for comparable phrases. And even in these circumstances, we’ll use the SQL to doc so we are able to say “That is the components that derives this definition of Revenue.”
You wish to depart 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 once I appeared for documentation instruments. I did numerous market analysis, taking a look at 20 totally different distributors and each instrument I may discover. What was essential to me was discovering a platform that would connect with all of the instruments I used to be already utilizing, which had been dbt, Snowflake, and Looker, and that had a powerful assist staff. I knew that after we first onboarded, we’d have questions, and we’d be establishing numerous permissions and knowledge connections, and {that a} robust assist staff could be very useful.
I remembered after we first labored with the staff, 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.
Photograph by Priscilla Du Preez 🇨🇦 on Unsplash
