Yuri Bukhan: Our goal is to provide direction to the market and to demystify people’s approach to working with big data and to showcase that the two leaders in the big data space, MongoDB and Cloudera, are working together to help enable companies to build more applications that can leverage the two systems.
DBTA: By go-to-market, do you mean co-marketing and co-selling and greater integration, as well?
YB: We have alignment at the executive level. We have alignment across marketing and so there are going to be a variety of go-to-market activities, whether it is webinars or training for the respective field teams to make sure that our messaging is aligned and consistent. And then on the sales side, to get each of the respective sales teams to collaborate on opportunities, but also to engage in the existing accounts where there is overlap and maybe the two technologies are currently in silos – to look at how we can bring them together to drive value for that joint customer.
On the technology side, as Kelly mentioned, there is an existing connector already in place today that is certified on the latest version of Cloudera, but that particular connector has some limitations which we are addressing with an updated version. This updated version will make it easier to quickly export data from MongoDB to Cloudera’s EDH [Enterprise Data Hub] to store process and analyze the data with less negative operational impact on MongoDB. That is something we are actively working on from an engineering collaboration standpoint. And then on the support side, we are looking at how we can deliver the best possible service to our joint customers. As part of the certification process, we identified the key contacts within each organization so we have an open line of communication in case one of our joint customers runs into an issue with a joint solution.
DBTA: You mentioned the need to demystify big data. Do you see confusion on how big data can be maximized within an organization?
YB: You can look at it from a couple of different angles. It is a space that is pretty crowded and a lot of companies get lumped into a particular category. MongoDB gets lumped into NoSQL, and Hadoop is also another key category that is part of big data. It is important to articulate to the market how these technologies work together to ultimately provide the best solution. One of Kelly’s colleagues, Matt Asay [vice president of business development and corporate strategy at MongoDB], who spoke at Strata about some of the capabilities and use cases for MongoDB, got approached after his presentation by quite a few folks looking for clarity around that presentation.
KS: The reality is that big data is a term that is tossed around almost endlessly by every technology company at this point and when you actually dig into what big data means, it really depends on who you are talking to. The analysts have a take on it, the media has a take on it, and each vendor has a take on it.
And, from what we have seen in the market, most people are pretty confused: There are lots of technologies and everybody is talking about big data but what should I be thinking about? What are the questions I should be asking to help me make good decisions for my organization?
Matt Asay’s presentation was about how people are using MongoDB and how it is complementary to Hadoop, and a number of people came up after the presentation and said that they had no idea that MongoDB and Hadoop were absolutely complementary. They thought they competed with one another - and that couldn’t be further from the truth.
DBTA: What do you want customers to understand?
KS: The reality is that for decades we have distinguished between systems interaction with customers where you are capturing data about users or other systems and then there is a different system where you analyze that data. That distinction still applies today and it applies to big data where there are NoSQL systems - in particular MongoDB - where customer-facing applications are being developed and users are interacting with data in MongoDB, and then that data flows into an analytical system like Cloudera, where all kinds of data from different systems can come together, and analysis can be performed, new insights can be derived. And then, that insight can be fed back into the online system for a better customer experience.
Good examples of that are recommendation engines where, based on all customers interacting with a site, we know that you are probably going to be interested in the following things based on your purchasing history or based on your browsing history. And, a second example is fraud detection, where, based on all the patterns of people interacting with my system, we can anticipate that these actions are symptomatic of fraud, and take corrective measures in real time with my online system. There is a cycle where data is created in the online system in MongoDB, it flows into Cloudera where new insights are divined, and that learning goes right back into the online system to improve the experience and reliability of the online service.