Driving Self-service and Enhancing DataOps with Atlan
The Energetic Metadata Pioneers sequence options Atlan clients who’ve lately accomplished a radical analysis of the Energetic Metadata Administration market. Paying ahead what you’ve discovered to the following knowledge chief is the true spirit of the Atlan neighborhood! In order that they’re right here to share their hard-earned perspective on an evolving market, what makes up their fashionable knowledge stack, revolutionary use circumstances for metadata, and extra!
Within the first interview of this sequence, we meet Heidi Jones, knowledge evaluation and program administration extraordinaire, who explains the historical past of Docker’s knowledge workforce, how they evaluated the market, and the way they’ll use Atlan to assist their colleagues drive one of many world’s finest developer experiences.
This interview has been edited for brevity and readability.
Would you thoughts describing Docker and your knowledge workforce?
Docker is a platform designed to assist builders construct, share, and run fashionable purposes. We deal with the tedious setup, so builders can concentrate on the code.
Information professionals at Docker help a wide range of completely different departments. So now we have a core knowledge workforce with engineers and analysts, after which we even have knowledge engineers and analysts that help the main capabilities of Docker, resembling Advertising, Gross sales, the completely different merchandise at Docker, Finance, et cetera.
A number of skilled knowledge engineers and analysts who’ve joined Docker, have solely began within the final 9 months or so. So we’ve had fairly a little bit of development on the information workforce, and are actually at that stage the place we’re making an attempt to spend money on good processes. That approach, our knowledge workforce can be sure that everybody at Docker has the information that they should do their jobs, and may finally assist builders do theirs.
And the way about you? Might you inform us a bit about your self, your background, and what drew you to Information & Analytics?
I believe the primary motive I’ve been drawn to knowledge and analytics is as a result of I identical to with the ability to reply folks’s questions.
I got here into knowledge evaluation via a non-traditional route. I’ve been at Docker for about six months now, however I’ve been within the knowledge house for a few decade. It began with Excel and offering insights by way of spreadsheets, as much as PowerBI utilizing Snowflake, that kind of factor.
So I used to be all the time a knowledge analyst, however then additionally a mission supervisor. And so what I do at Docker combines each of these. The information of knowledge and the workflows required to get good knowledge and supply good insights, and likewise the mission administration and operations aspect of it. All of it permits knowledge professionals to concentrate on what they do finest, which is modeling knowledge and offering insights with out being blocked by something that has to do with workflow.
What does your stack appear to be? Why did you want an energetic metadata answer?
We ingest knowledge from a wide range of sources in a number of alternative ways, relying on the supply. After which our knowledge warehouse degree is Snowflake. Our modeling layer is dbt, that’s the place we do modeling and transformation. After which our important BI software is Looker, that’s the place we do visualization and evaluation.
We have been only a one-person workforce not too way back. So all of that knowledge work was on one particular person’s plate, together with documentation and understanding knowledge sources. That’s fairly a bit for one particular person.
A variety of that burden has been unfold out throughout a number of folks on the workforce by now. However we’re making an attempt to maneuver away from, “Oh, let me go ask my favourite knowledge particular person,” towards, “I can go examine this software and I do know there’s a licensed knowledge asset.”
And so, due to our stack, we have been drawn to Atlan due to issues just like the Looker Chrome extension plugin, the dbt integration, that kind of factor. As a result of proper off the bat we have been in a position to say, “Okay, any descriptions we put in our dbt layer will mechanically be uncovered in Atlan.”
So non-engineering customers who wish to know what the information means can go straight to Atlan and see what’s being accomplished within the modeling layer.
Did something stand out to you about Atlan throughout your analysis course of?
Atlan is a really cool software that has an excellent suite of options that we have been searching for, however the differentiator actually got here right down to the folks at Atlan.
You demonstrated very competent understanding of the issues within the knowledge house and likewise very mature buyer help. We may inform that your help was not simply one thing you have been promising for us, however one thing that you simply have been already actively doing with different clients.
We knew that it could be an actual partnership and that the shopper help org was ready to help the wants of a corporation like ours. And that maturity stood out to us once we made our determination.
However then once more, additionally the options like Playbooks, the integrations that I’ve already talked about with dbt, with Looker, and simply the fixed innovation as effectively that we have been in a position to observe even throughout the analysis processes, which I imagine took us about two months.
There have been a number of improvements and releases that occurred throughout that point interval and we may see the cadence the Atlan was on to constantly enhance. All of these have been promoting factors to us.
What do you plan on creating with Atlan? Do you’ve gotten an thought of what use circumstances you’ll construct, and the worth you’ll drive?
Our greatest worth that we’re making an attempt to drive with Atlan is to make it possible for professionals at Docker can get the data they want in regards to the knowledge that they should do their jobs.
We wish to transfer in the direction of self-serve analytics and permit each knowledge professionals, and those that simply need to have the ability to use knowledge extra freely of their work, to have the ability to achieve this with out having to get into the entire SQL and technical particulars of the information.
They know they will belief the information set, they know they will belief the information that they’re , and so they can go forward and make their choices. In the end, it ought to assist us help our mission of delighting builders, and growing instruments that they get pleasure from utilizing.
We’ll be supporting that with Atlan, and likewise supporting our knowledge engineering and analytics groups. They should have extra supported and standardized workflows, in order that they will concentrate on modeling, actually digging in and doing what they do finest with knowledge.
Did we miss something?
That’s an excellent query. I believe how we found Atlan was attention-grabbing. I’ve been following Prukalpa, really, for a few years simply as a knowledge skilled, simply type of watching Atlan.
And so after I joined Docker, they have been already knowledge catalog instruments, however hadn’t been Atlan but. And I stated, “Properly, how about Atlan? Ought to we have a look at Atlan as effectively?”
So one of many first issues I did at Docker was to start out up that dialog, and the explanation why I did that’s as a result of I had appreciated studying what she stated in these areas. In regards to the causes we want knowledge catalog instruments, and past only a catalog, the way it could possibly be a part of knowledge operations. And that piece of it actually had spoken to me over time.
And we noticed some spectacular instruments. It’s a burgeoning house. There’s some nice instruments on the market. However I’m glad that we additionally checked out Atlan as a result of finally it had an excellent mixture of what we wanted at Docker.