Supporting a World-class Documentation Technique with Atlan
The Energetic Metadata Pioneers sequence options Atlan prospects who’ve accomplished an intensive analysis of the Energetic 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 fashionable knowledge stack, progressive use instances 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 prospects, named to Forbes’ FinTech 50 listing 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.
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 interested by 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 individual on the info workforce, which incorporates organising 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 fascinating and funky, and that’s why I’ve stayed on this subject.
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 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 purpose 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 knowledge, we arrange duties, and we make certain knowledge is simple to search out and prepared for use. We’re right here to verify knowledge is clear, dependable, and reusable, so analysts on groups like Advertising and marketing 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 recordsdata from dbt and mechanically 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 knowledge dictionary.
Might 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 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 typically 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 straightforward 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 workforce, we’re usually the curator for that documentation. Once 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. The most effective factor about lineage is it’s absolutely automated. It has been very useful in knowledge exploration when somebody just isn’t accustomed to a brand new knowledge supply.
What’s subsequent for you and your workforce? Something you’re enthusiastic about constructing?
Now we have been wanting into the dbt semantic layer previously yr. It is 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 are going to construct the dbt semantic layer on prime of the presentation layer to make reporting and visualizations extra seamless.
Do you may have any recommendation to share along 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 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 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 previous 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 previous documentation.
So, I actually emphasize documentation. If you begin is the time and the place to essentially centralize your information, so every time somebody leaves, the information stays, and it’s a lot simpler for brand spanking 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 related phrases. And even in these instances, we’ll use the SQL to doc so we are able to say “That is the system that derives this definition of Revenue.”
You wish to go away 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 seemed for documentation instruments. I did a number of market analysis, taking a look at 20 totally different distributors and each device 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 have 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 a number of permissions and knowledge connections, and {that a} robust help workforce could be very useful.
I remembered once we first labored with the workforce, 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.
Picture by Priscilla Du Preez 🇨🇦 on Unsplash
