Supporting a World-class Documentation Technique with Atlan
The Energetic Metadata Pioneers sequence options Atlan clients who’ve accomplished a radical analysis of the Energetic Metadata Administration market. Paying ahead what you’ve realized to the following 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 fashionable knowledge stack, progressive 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 record 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.
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 thinking 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 workforce, which incorporates establishing all of the infrastructure, constructing stories, discovering insights, and many communication with folks. At Tala, I get to work with a workforce 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 discipline.
Would you thoughts describing Tala, and the way your knowledge workforce 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 resolve what legacy establishments can’t or gained’t, as a way to unleash the financial energy of the International Majority.
The Analytics Engineering workforce 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 verify knowledge is clear, dependable, and reusable, so analysts on groups like Advertising and Operations can give attention to 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 strategy 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 straight as their knowledge 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 consider we have 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 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 purpose is to doc each single column and desk that’s uncovered to the top consumer, and Atlan has been fairly helpful for us. It’s very straightforward to doc, and really easy 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 house owners who created the DB to grasp 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 manner 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 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. The perfect 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 workforce? Something you’re enthusiastic about constructing?
We have now been trying into the dbt semantic layer up 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. 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 would dwell in a Google spreadsheet, which isn’t actually sustainable for bigger organizations with hundreds of 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 straightforward for us to refer again to the outdated documentation.
So, I actually emphasize documentation. Once you begin is the time and the place to actually centralize your data, so every time somebody leaves, the data stays, and it’s a lot simpler for brand spanking 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 comparable phrases. And even in these circumstances, we’ll use the SQL to doc so we will say “That is the formulation 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 after I regarded for documentation instruments. I did a variety of market analysis, taking a look at 20 totally different distributors and each software I might discover. What was vital 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 help workforce. I knew that after we first onboarded, we might have questions, and we’d be establishing a variety of permissions and knowledge connections, and {that a} sturdy help workforce could be very useful.
I remembered after 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
