By David Andrzejek, head of Monetary Companies, DataStax
Capital One could be the sixth-largest financial institution in the US, however it’s working arduous to harness its knowledge and the cloud to execute far more like a fintech. The corporate is on a mission to revolutionize the banking business by way of expertise and knowledge and serves as a mannequin for harnessing the facility of information for progress.
In the present day, Capital One is a tech-forward monetary companies enterprise, using open-source cloud applied sciences just like the extremely scalable NoSQL database Apache Cassandra® to enhance buyer expertise, drive innovation, and speed up speed-to-market for his or her purposes. We not too long ago spoke with Capital One senior director David Concord about transferring to the cloud, constructing a buyer knowledge platform, and the significance of real-time knowledge.
Inform us about Capital One and your position
Capital One is among the nation’s largest banks that gives conventional banking merchandise, in addition to on-line banking companies. We provide auto loans and bank cards. Past that, we’ve industrial lending, in addition to near-term companies, like Capital One Buying.
A couple of years in the past, the management realized that the banking business goes to be dominated by nice tech corporations that handle danger exceptionally nicely. Threat administration was all the time one of many core foundations of the corporate. In order that they got down to enhance our software growth and dev ops practices. Round 2015, Capital One went to AWS re:Invent and set forth our aspirational objective to modernize our complete expertise infrastructure.
Principally, we wished to get out of our knowledge facilities and run in a public cloud. One of many core parts I labored on was the client platform. It was such an enormous transfer for us. There was a lot change related to transferring to the cloud.
I joined Capital One 10 years in the past, on the cusp of its digital transformation. All through the years, I used to be tremendous fortunate to work with nice groups on difficult initiatives. After I initially joined, I labored on creating the API fashions that might help the purposes we run at present. We’ve labored with the appliance groups to construct out the APIs for our present cell software out there on the app retailer. I used to be actually pleased with how a lot work we did. I realized loads from the ecosystem.
After that undertaking we moved our digital companies – as a part of the migration – into AWS. Then, I went over to work on our buyer platform, certainly one of our main techniques the place we migrated the client system off the mainframe and transferred it into the cloud. This buyer knowledge platform initiative had a number of engagement with DataStax [a managed database service built on Cassandra].
What challenges did you face with the client knowledge platform and the way did transferring to the cloud assist?
Whether or not you log into the web site, the cell machine, or work together with an agent, the client system is queried to find out your relationship with the financial institution and the way you wish to work together with us. We persist that info to provide the proper service.
The Capital One buyer knowledge platform used to run on a centralized relational database administration system (RDBMS) mannequin that would solely launch, at most, 4 new incorporates a month. This brought about delays in resolving points that software groups have been having with the platform, in addition to the corporate’s efforts to introduce extra seamless options to the market.
Capital One additionally had issue in scaling up its outdated infrastructure. On-premise capability planning was an enormous undertaking. The fee and lead instances of scaling the capability of the mainframe hindered software upgrades and slowed their potential to carry new options to market, making expertise a barrier for enterprise options. Throughout vacation seasons, the corporate needed to scramble to make sure there was enough capability to satisfy spikes in demand.
Capital One adopted a microservice architectural model, which consequently pulled a bunch of information out of central areas and separated it into totally different elements of the client software ecosystem. Now, the parts we beforehand ran on our mainframe are actually operating on DataStax. We adopted this structure to assist us mitigate danger of failures, generate clear traces of separation to scale independently, and, most significantly, allow groups to construct and deploy our purposes independently.
Now, we will simply do 100 releases a month for a few of our parts. This enables us to get extra options to market at a quicker charge with much less. We nonetheless have third-party distributors that depend on mainframes, however all our inner purposes are off the mainframe and fully operating inside AWS and on high of Cassandra. The cloud has given us the potential to launch options a lot quicker and scale out simply, altering the best way we function.
Why did Capital One select Cassandra for the client knowledge platform?
There are some things that come to thoughts. The entry patterns we want for the client platform are fairly easy and match completely with the important thing worth mannequin of Cassandra. We additionally make good use of Cassandra’s huge column implementation so as to add new attributes to our buyer knowledge and append them into the prevailing construction.
One of many greater benefits of Cassandra is resiliency. Since Cassandra leans in the direction of AP in CAP Theorem, it may possibly handle partition failures to stay out there round the clock. Cassandra’s masterless, peer-to-peer structure ensures that purposes by no means expertise downtime even throughout disastrous system failures.
The corporate itself has invested a number of effort and time into our resiliency and this dedication made Cassandra a terrific selection. It’s all the time out there. It’s all the time there for us. And it has carried out rock strong.
How do you measure the info platform’s ROI and what are the outcomes you’re seeing with Cassandra?
Once we discuss ROI, there are three main issues to contemplate: alternative prices, operational prices, and buyer expertise.
The funding in Cassandra could also be massive, however there’s additionally going to be some misplaced alternative prices staying the place you might be. On the mainframe, it was actually troublesome. We had constraints on what we may implement from the enterprise characteristic perspective, as a result of the mainframe funding hurdle was so excessive. Now we’re capable of scale our platform simply sufficient to carry new options to the market round the clock with sufficient capability.
Secondly, from an operational value standpoint: as a financial institution we purchase portfolios of huge corporations like Walmart and convey them into our ecosystem. Usually, these portfolio migrations took a number of weeks and even months. With Cassandra, we will do that over a weekend with none downtime. It’s reached a degree the place including 15 million new clients is now a regular day-to-day operation.
Lastly, due to the nice real-time insights we’ve gained from the fashionable structure, we have been capable of establish gaps in processes and expertise parts and compensate for them, driving down the quantity of instances that individuals contact customer support. In the end, our funding created a greater buyer expertise for the long-run and improved our cost-profile.
Particularly for our buyer knowledge platform, there are two metrics that we’ve actively tracked: restoration level and restoration time aims. The restoration level goal is the power to isolate from a single stage of failure and keep away from points whereas the restoration time goal is to ensure that no knowledge loss is persistent.
Beforehand, our RDBMS implementations had a tricky time assembly our restoration level aims, that are sometimes lower than 5 minutes for a regional failure. Moreover, with these implementations being lively, passive and never multi-master primarily based, we skilled further latency. This made us query the worth of operating two techniques if we all the time have to write down again to a single area. Now I’m actually pleased with the groups and the uptime they’ve achieved. We aspire to five-nines of availability and we are sometimes assembly our present SLAs. Our buyer group has additionally taken on a terrific stage of possession of the platform, which is tremendous superior.
Inside the buyer platform, the overwhelming majority of our visitors that goes to Cassandra is real-time. Including Apache Spark [an open source data analytics engine] into the Cassandra ecosystem helps us validate that our knowledge is constant throughout the ecosystem and achieve further insights into service and system gaps. We’ve now constructed a real-time knowledge heart and an analytical knowledge heart to help all our banking techniques, together with further machine studying fashions.
Migrating features off the mainframe is a notoriously difficult operation. How did you address this modification?
Transferring to the cloud is usually a very scary dialog. There’s a danger to creating nearly any change and it is advisable to be considerate and cautious to keep away from making the flawed decisions. The most important factor we did was knowledge testing. It was a big stage of overhead for us, however we have been capable of migrate our clients safely. It’s this stage of information testing that made our migration to DataStax very profitable.
One other necessary factor is to place a number of thought into your knowledge mannequin, particularly inside Cassandra. Assume arduous about your knowledge fashions and just be sure you be ok with them. Additionally, there’s no good system and it is advisable to be ready for failures. Attempt to perceive beforehand the way you’re going to compensate for them and the way you’ll appropriate them when the failures do arrive.
Final however not least, you completely should put money into your individuals on the groups. They’re very gifted and so they’re those who will drive innovation in your software ecosystem.
With DataStax 100% invested in the place we’re, and with our strong relationship inside Cassandra, I really feel like we’re in place. I’ve been tremendous happy with the efficiency and availability that’s now supplied on our platforms.
Hearken to the full dialog with David Concord to be taught extra about how DataStax helps Capital One leverage the seamless scalability of Cassandra to drive quicker innovation and enhance buyer expertise.
About David Andrzejek:
David has spent 25 years serving to corporations undertake expertise to realize outsized enterprise transformation outcomes.
