Supporting a World-class Documentation Technique with Atlan
The Lively Metadata Pioneers sequence options Atlan prospects who’ve accomplished an intensive analysis of the Lively Metadata Administration market. Paying ahead what you’ve realized to the subsequent 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 fashionable 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 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 sturdy 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 Information & Analytics?
From the start, I’ve been very desirous about 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 at all times been the primary particular person on the information workforce, which incorporates organising all of the infrastructure, constructing reviews, discovering insights, and plenty of communication with folks. At Tala, I get to work with a workforce to design and construct new information infrastructure. I discover that work tremendous fascinating and funky, and that’s why I’ve stayed on this discipline.
Would you thoughts describing Tala, and the way your information workforce helps the group?
Tala is a FinTech firm. At Tala, we all know at this time’s monetary infrastructure doesn’t work for many of the world’s inhabitants. We’re making use of superior know-how and human creativity to unravel what legacy establishments can’t or received’t, to be able to unleash the financial energy of the World Majority.
The Analytics Engineering workforce 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 information is simple to seek 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 deal with evaluation and producing insights.
What does your information 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 robotically syncing them to Snowflake and Looker. We actually like that automation, the place the Analytics Engineering workforce doesn’t want to enter Atlan to replace data, it simply flows by means of from dbt and our enterprise customers can use Atlan instantly as their information 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 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 12 months 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 aim 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 will 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 workforce, we’re usually the curator for that documentation. After we construct tables, we sync with the service homeowners who created the DB to grasp the schema, and once 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 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 may discover out who the proprietor is. They will 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 information exploration when somebody just isn’t accustomed to a brand new information supply.
What’s subsequent for you and your workforce? Something you’re enthusiastic about constructing?
We now have been trying into the dbt semantic layer previously 12 months. 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 high of the presentation layer to make reporting and visualizations extra seamless.
Do you may have any recommendation to share together with your friends from this expertise?
Doc. Undoubtedly 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 robust advocate for documentation, so I went in to doc the entire thing, however that might stay 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 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 12 months, 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. While you begin is the time and the place to essentially centralize your data, so at any time when somebody leaves, the data 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.
Generally 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 method 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 appeared for documentation instruments. I did lots of market analysis, 20 totally different distributors and each instrument I may discover. What was vital to me was discovering a platform that might hook up with all of the instruments I used to be already utilizing, which had been dbt, Snowflake, and Looker, and that had a robust help workforce. I knew that once we first onboarded, we might have questions, and we might be organising lots of permissions and information connections, and {that a} sturdy help workforce can be very useful.
I remembered once we first labored with the workforce, 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 at all times proud that I discovered and selected Atlan.
Photograph by Priscilla Du Preez 🇨🇦 on Unsplash
