Activating and Governing a Rising Information Platform with Atlan
The Energetic Metadata Pioneers sequence options Atlan prospects who’ve lately accomplished an intensive analysis of the Energetic Metadata Administration market. Paying ahead what you’ve discovered 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 sequence, we meet Surj Rangi, Enterprise Cloud Information Architect, Piyush Dhir, Senior Technical Lead, and Danni Garcia, Product Supervisor, at REA Group, the operator of main residential and business property web sites, mortgage brokering companies, and extra. Surj, Piyush, and Danni share REA’s evolving knowledge stack, their data-driven ambitions, and the standards and course of behind their selection of Atlan.
This interview has been edited for brevity and readability.
May you inform us a bit about yourselves, your backgrounds, and what drew you to Information & Analytics?
Surj Rangi:
I’m Surj Rangi, Architect in Information Companies, and I’ve been at REA for 2 years now. I graduated in IT from the UK, then labored in plenty of consultancy corporations in Information and Analytics and developed a powerful background in cloud platforms and knowledge structure. I migrated to Australia about seven years in the past, with 20 years of expertise in knowledge throughout numerous industries together with Media, Telecommunications, Finance, E-commerce and Banking.
I joined REA and was very eager on the position that I used to be supplied and the staff I used to be coming into. What actually enticed me was working with an organization that had a startup mentality, and have been excited to push and ship outcomes. Beforehand, I’ve labored with massive banks the place there’s a variety of forms and issues take time, and I used to be excited to see how issues work at a spot like REA.
Piyush Dhir:
I’m a Senior Technical Lead at REA. My journey goes again to school once I was ending my Bachelors in Software program Engineering and wanted to decide about what I needed to do subsequent.
I began as an Android developer again when it appeared like everyone’s subsequent factor was “What will be my subsequent Android challenge?” Once I was doing that, I got here throughout SQL Server, studying how you must do operational modeling if you’re creating one thing like a front-end software. That’s how I made my first step into knowledge. Since then, I’ve been working throughout plenty of totally different sorts of knowledge groups.
My first knowledge staff was a Information Administration staff for a public firm in Australia. They have been ranging from zero, constructing a whole greenfield ecosystem for his or her knowledge utilizing the SAP merchandise. I spent about 5 years in that world, then moved into a variety of small firms and massive firms. I did a little bit of consulting, I labored for a financial institution within the center, after which lastly ended up at REA.
Once I first joined an information staff again in 2012, what actually stood out to me on the time was that knowledge was mentioned to be “the brand new oil”, and that Information & Analytics have been going to be the subsequent large factor. Again then, some individuals began doing Machine Studying and taking part in round with R Studio, but it surely was by no means the “bread and butter” of any firm, simply a type of “north star” form of initiatives.
All of the sudden, now 10 years down the road, it’s change into not solely the “bread and butter” of the corporate, but it surely’s a possibility for monetization for lots of them, too. It’s good to see that transition occurring, and it’s been fascinating to look at.
Danni Garcia:
I’m a Product Supervisor in Information Companies with a selected background in Information research. I haven’t at all times been in Product. I’ve labored within the know-how trade for nearly a decade now throughout many various areas and roles in each massive and small organizations, however I began out as a Information Analyst.
Would you thoughts describing REA, and the way your knowledge staff helps the group?
Surj:
I feel it’s good to know that REA began in a storage in Australia within the early-to-mid ’90s, and since then the corporate has grown and scaled enormously throughout the globe. REA has a presence not solely in Australia, however Asia too and has robust ties with NewsCorp. We began by itemizing residential properties, and it’s grown from there to business properties and land, as properly. We’ve additionally performed a variety of mergers and acquisitions. For instance in Australia, we’ve purchased a agency known as Mortgage Selection that enables REA to be positioned not solely to promote listings, publications, and supply insights into property into the trade in Australia, but in addition present mortgage dealer companies.
So if you wish to promote your property, REA gives the entire bundle. You’ll be able to promote your property, and should you want financing, we may also help you fiscal your subsequent funding.
We’ve gone via an extended journey, and have had a Information Companies staff for an extended time period. The whole lot was decentralized, then it was centralized. Now it’s a little bit of a hybrid, the place now we have a centralized knowledge staff constructing out the centralized knowledge platform with key capabilities for use throughout the group, with decentralized knowledge possession. We are attempting to align with a Information Mesh strategy when it comes to how we construct out our platform capabilities and adoption of “knowledge as a product” throughout the group.
We’re multi-cloud, each AWS and GCP, which brings its personal challenges, and we do all the pieces from ingestion of knowledge, event-driven structure to machine studying. We’re constructing knowledge belongings to share with exterior firms within the type of an information market.
Danni:
Information Companies exists to assist the entire inside strains of companies throughout our group. We’re not an operational staff, however a foundational one, that builds knowledge merchandise and capabilities to assist assist groups to allow them to efficiently leverage knowledge for his or her merchandise. Our mission is to make it straightforward to know, shield and leverage REA knowledge.
Piyush:
I’ll add that over the past couple of years, REA has predominantly seen themselves as a listings enterprise. It’s nonetheless a listings enterprise, offering the most effective listings info attainable out to prospects and customers. However what’s occurred is that this wealthy knowledge evolution helps our enterprise change into data-driven. A number of the knowledge metrics you see on the REA web site and cell software are principally derived from the work that the group has put in to develop our Information & Analytics and ML follow to drive higher determination making.
We have now a variety of beneficial knowledge. There are a variety of initiatives happening now to broaden the utilization of knowledge, and over the subsequent two years, we are going to develop our panorama and derive even higher outcomes for our prospects and customers. to know, leverage, then showcase knowledge to our prospects and their prospects.
What does your knowledge stack appear like?
Danni:
We have now a real-time ingestion platform known as Hydro utilizing MSK, which is a custom-built streaming platform. Then now we have our batch platform, which ingests batch knowledge utilizing Breeze, constructed on Airflow. Our knowledge lake resolution is BigQuery.
Piyush:
We have a look at ourselves as a poly-cloud firm, utilizing each AWS and Google Cloud Platform, in the intervening time.
From an AWS perspective, now we have most of our infrastructure workloads operating there. We have now EC2 cases and RDS operating there. We have now our personal VPC. We have now a number of load balancers.
From a Information and Analytics perspective, the vast majority of our workloads are in GCP. We’re presently utilizing BigQuery as an information lake idea, and that’s the place most of our workloads run. We use SageMaker for ML, and there’s some groups which are experimenting with BigQuery ML on the GCP facet, as properly. We even have a self-managed Airflow occasion, in order that’s our knowledge platform.
We’re presently within the strategy of establishing our personal event-driven structure framework utilizing Kafka, which is on AWS MSK.
Other than that, our Tableau entrance finish is used for reporting, so now we have each the Tableau desktop and the server model, in the intervening time.
Why seek for an Energetic Metadata Administration resolution? What was lacking?
Surj:
We have now an current open-source knowledge catalog that now we have been utilizing for just a few years now. Adoption has not been nice. As we’ve scaled and grown, we realized that we wanted one thing that’s extra related for the fashionable knowledge stack, which is the route that we’re going in direction of.
There’s additionally a stronger push in our trade towards higher safety of knowledge. We retailer a variety of personally identifiable knowledge throughout the enterprise, and a few of our key methods now we have in Information Companies are that we need to first perceive the info, shield it, then leverage it. We would like to have the ability to catalog our knowledge, and perceive how dispersed it’s throughout our warehouses, numerous platforms, in batches, and streams.
We have now a variety of knowledge, e.g. we’ve bought over two petabytes of knowledge in GCP BigQuery alone. We would like to have the ability to perceive what knowledge is, the place it’s put collectively, and apply extra rigor to it. We have now good frameworks internally when it comes to governance, processes, and insurance policies, however we need to have the suitable tech stack to assist us use this knowledge.
Danni:
There have been some technical limitations, as our earlier knowledge catalog might solely assist BigQuery, however we actually needed to assist the route of the enterprise when it comes to scale and the way it might align extra broadly with our Information Imaginative and prescient and Technique.
Our technique needs to implement Information Mesh and ‘Information as a Product’ mindset throughout the group. Each staff owns knowledge, they leverage it they usually have a duty to handle it with governance frameworks.
So, in an effort to embed Information Governance practices and this cultural shift, we wanted a device to assist the frameworks, metadata technique, and tagging technique. We additionally wanted an answer to centralize all our Information Property so we might have visibility of the place knowledge is and the way it’s being categorised which helps our Privateness initiatives.
We’re nonetheless on a metamorphosis journey at REA, which could be very thrilling. A brand new knowledge catalog was an actual alternative to push ourselves additional into that transformation with a brand new Information Governance framework.
How did your analysis course of work? Did something stand out?
Surj:
We did some market analysis, talking to Gartner and reviewing accessible tooling throughout the trade. We might have clearly stored utilizing our present Information Catalog, however we needed to guage a large spectrum of instruments together with Atlan, Alation, and Open Metadata, to cowl Open Supply vs. Vendor managed.
We felt Atlan match the standards of a contemporary knowledge stack, offering us the capabilities we’d like, equivalent to self-service tooling, an open API, and integrations to quite a lot of know-how stacks which have been all crucial to us.
We had an overwhelmingly good expertise partaking with Atlan, particularly with the Skilled Companies staff. The arrogance that they gave us within the tooling after we went via our use circumstances drove a sense of robust alignment between REA and Atlan.
Piyush:
We did a three-phase analysis course of. Initially we went out to the market, did a few of our personal analysis, making an attempt to know which firms might match our use circumstances.
As soon as we did that, we went again and checked out totally different features equivalent to pricing and used that as a filtering mechanism. We additionally seemed on the future roadmap of these firms to determine the place every firm is perhaps going, which was our second filtering course of. After we have been performed choosing our choices, we had to determine which one would swimsuit us finest.
That’s after we did a lightweight proof of worth the place we created high-level analysis standards the place everyone concerned might rating totally different capabilities from 1-10. The staff included a supply supervisor, a product supervisor, an architect, and builders, simply to get a holistic view of the expertise everyone can be getting out of the device. After that scoring, we made a light-weight suggestion and introduced it to our executives.
A few of what we have been taking a look at within the analysis standards have been issues like understanding what knowledge sources we might combine to, what safety seemed like, and ideas like extensibility so we may very well be versatile sufficient to increase the catalog programmatically or through API. As a result of now we have our knowledge platform operating on Airflow, we additionally needed to know how properly every possibility labored with that.
Then we additionally checked out roadmaps and requested ourselves what may occur sooner or later, and if one thing like Atlan’s funding in AI is one thing we should be trying into, and different future enhancements Atlan or different distributors might present. We have been making an attempt to get an understanding of the subsequent two or three years, as a result of if we’re investing, we’re investing with a long-term perspective.
Surj:
For those who have a look at the time period “Information Catalog”, it’s been round for a really very long time. I’ve been working over 20 years, and I’ve used knowledge catalogs for a very long time, however the evolution has been vital.
When Piyush, Danni and I have been taking a look at distributors, that’s one thing we have been occupied with. Would you like a conventional knowledge catalog, which we’ve in all probability seen in banks which have a powerful, ruled, centralized physique, or would you like one thing that’s evolving with the instances, and evolving the place the trade is heading?
I feel that’s why it was good to listen to from Atlan, and we preferred the place they have been positioned in that evolution. We like that Atlan integrates with plenty of tech stacks. For instance, we use Nice Expectations for knowledge high quality in the intervening time, however we’re contemplating Soda or Monte Carlo, and we discovered Atlan already has an integration with Soda and Monte Carlo. We’re discovering extra examples of that, the place Atlan is turning into extra related.
Conversely, after we have been taking a look at addressing personally identifiable info, we needed to have the ability to scan our knowledge units. Atlan was fairly clear, saying “We’re not a scanning device, that’s not us.” It was good to have that differentiation. After we checked out Open Metadata, they mentioned that they had scanning functionality, but it surely wasn’t as complete as we have been anticipating, and we all know now that this use case is in a special realm.
It’s good to have that readability, and know which route Atlan goes to go.
How do you plan on rolling Atlan out to your customers?
Danni:
So usually in platforming and tooling, we’re very caught up specializing in the know-how and never specializing in the consumer expertise. That’s the place Atlan can actually assist.
We need to create one thing that’s tangible, and that folks need to use, so we will drive mass adoption of the platform. With our earlier catalog, we didn’t have a lot adoption, so we’re making {that a} success metric, and one of many nice options in Atlan is that we will customise it to fulfill the wants of differing personas. An idea that hasn’t been historically pushed within the Information Governance house!
We went out to the enterprise and undertook an enormous train, interviewing our stakeholders and potential customers. Now, we actually perceive the use circumstances, scale and what our customers need from the Information Catalog. Our personas – analysts, producers, house owners and customers will all be supported within the roll out of Atlan, ensuring that their expertise is custom-made inside the device they usually can all perceive and use knowledge successfully for his or her roles.
