Qubole: To Soon Introduce Additional Cloud and Data Engines In New Verticals Like IoT. Life Sciences and Retail

BWDisrupt interacted with Qubole's team and founders Ashish Thusoo and Joydeep Sharma to know in-depth how things are working for their startup. 

Qubole has quickly become recognized as a leader in the rapidly expanding big data in the cloud space, and offers one of the most comprehensive, economical and easy-to-use solutions for enterprises. Qubole was founded by Ashish Thusoo and Joydeep Sen Sarma, who built andled the original Facebook Data Service Team that built Facebook’s data architecture from 2007-2011 and authored the Apache Hive Project. Ashish and Joydeep started Qubole to address the same issues with scalability and accessibility that they saw at Facebook and created Hive to solve, but for the enterprise. The cloud was purpose-built to handle massive workloads, and processing big data is a
natural fit for the cloud where real analytics happens. The Qubole Data Service (QDS) helps enterprises take full advantage of the cloud to make processing more accessible in an enterprise setting while reducing complexity and cost.
Qubole allows users to leverage the three major public clouds and numerous leading data engines within the platform so data scientists can choose the right tools for the job, not just what’s available to them.It also removes concerns about the price of a cloud deployment as it manages the costs of data projects by providing automatic scaling of clusters based on the need for compute power.It further consolidates resources as with the cloud many teams can share a single platform instead of each team creating its own platform. This is the multitenant platform which caters to a glowing customer base across the globe.Over the past year, Qubole has made significant enhancements to its QDS, its foundational self-service big data platform, to meet its customers’ needs, remain innovative, and make its platform attractive to an even wider group of potential users. It introduced Spark-as-a- Service to support the incredibly popular new data engine, with support for Spark on AWS and Google Cloud Platform. As one of the biggest concerns around the cloud is security,

QDS also added major security functionalities like the ability to deploy in a Virtual Private Cloud. On the business side, Qubole continues to grow its customer base, with big brands such as Autodesk, Datalogix (Oracle), BloomReach, Pinterest, TubeMogul, Universal Music Group, Under Armour Connected Fitness and Fanatics all using Qubole to underpin their data.

What’s the idea behind genesis of Qubole?
Before starting Qubole, Ashish and I were working at Facebook to build and run the Data Infrastructure team around the Apache Hadoop stack which was used by the whole company for large scale data processing and analysis. While at Facebook, we understood that running the stuff was really hard that the complexity had shifted from writing software - which was becoming free and abundant thanks to open-source. We also felt that it is important to make data analysis easy and collaborative for users to use these new technologies. Also around 2011, the AWS cloud became popular and younger companies started taking their data to the cloud for storage and processing. At that point we could imagine offering Big Data Analytics as a service running in the Cloud. We started Qubole in 2011 with our primary focus on ease of use and administration, self-serve data analytics and a highly optimized offering purpose-built for the Cloud. Early adopters such as Quora and Mediamath gave validation to the technology and the rest is history.

How is Qubole serving Indian clients in particular?
Qubole currently serves around 150 paid customers worldwide, 15% of which are in India. We have some popular companies such as Capillary Technologies, Hike Messenger, Ola Cabs and Saavn as our clients in India. Indian companies are also leveraging the power of cloud for storage and processing of data. Indians are producing more and more data which is being used by companies to gain business insights.

Traditionally, corporates had to build their own data centres to store and analyse their data which was time consuming and cost ineffective. This was a disadvantage for many since they had to incur a huge cost at the very beginning and also had to maintain a good pool of data architects and data scientist to build and service the data infrastructure. However, with the rise of cloud and entry of big players such as Amazon Web Services, Google Cloud and Microsoft Azure, this problem has been solved to a large extent. Data storage has become easy and scalability is not a concern anymore. Qubole understood this and through our flagship product, the Qubole Data Service (QDS), helped enterprises deploy data analytics architecture quickly and save costs by taking analytics directly to the cloud where all their data is being stored.

One of our earliest customers in India was Capillary Technologies. They are a retail analytics company and their Consulting and Data Analytics team uses Qubole's browser based interfaces for customized analytics and reports for their clients. As is the case with the Retail market, data sources for Capillary are distributed. The first step for them is to use Qubole to extract data from these different data sources all over the organization and centralize them. Analysts and Consultants perform interactive analytics against these large data sets to deliver insights for customers all over the world - and Qubole's global capabilities allows them to perform analytics in different regions and keep data local to such regions in keeping with legal requirements. The Qubole platform achieves this without any Capillary admin/ dev-ops help whatsoever un-constraining the Analysts.

Please share the kind of monetization model Qubole has experimented with?
Our model from the beginning has been a subscription model for our SaaS offering. We do not charge clients for the compute and storage - for those they have a direct relationship with the Cloud vendor. Services are also not a major monetization component for us - we work with SIs who can provide application development and other services to our users. Within the subscription model - we have gradually evolved to offer different types of options - starting from simple Elastic pricing for smaller customers to longer term licenses with committed usage. While the vast majority of our subscription revenues are based on per-CPU licensing - we also have some revenues from user licenses.

What is the current round of investment in the firm?
In January 2016, Qubole closed a $30m Series C funding round led by IVP, bringing its total funding to $50m. The funding will allow Qubole to introduce additional cloud and data engine integrations, as well as support its expansion into new verticals, including Internet of Things (IoT), life sciences and retail.

What kind of marketing efforts has Qubole being putting in?
We have increased our presence at major industry events and have aggressively invested in meetups and lunch-and-learn events across many different cities in the US. Like other enterprise software firms - we have also been working with the analyst community to get the word of our products out to the customer base. Specifically for India - we now have a marketing firm we work with for driving awareness in this market.

What are the market opportunities Qubole is eyeing in the near future? (Future Plans)
Qubole’s primary market is North America with a healthy business in India as well. In the near future, we would like to increase our presence in the Europe, broader APAC beyond India and the Middle-East. We are also investing in strengthening our products in Azure and Google Clouds and see a growing opportunity in addressing customers in those clouds. We have also recently announced a partnership with Oracle to make Qubole available in their cloud. We are also seeing rapid growth of new user segments like data scientists and are ramping up to address their needs.

Tell us about the kind of challenges that Qubole had to fight in its initial stages & how did it overcome them?
We faced challenges on multiple fronts. When we were small - the Public Cloud ecosystem was also small and a lot of investors regarded this as an early market and were hesitant to invest. Raising early rounds was thus not easy. We also faced challenges as first time entrepreneurs and made mistakes in the process. Creating a high performance Sales and Marketing teams and strategy was something that took multiple iterations. In our initial years - business was often soft and that created a lot of pressure on the whole company. Finding great people is always a huge challenge. Looking back - persistence was the solution to most of these issues. With enough tries - one eventually gets lucky and finds the solution - whether it's finding a great investor, a great employee or a great customer. Thankfully our overall strategy and market has worked out reasonably well.

Please share with us how do you plan to innovate you present product/service offerings to make it unique & stay competitive?
Our core value proposition is fairly simple. We have a comprehensive big-data offering that is self serve, scales to hundreds of users without intervention and is tightly mated to public cloud features that makes it by the most cost-efficient and reliable of big-data offerings. We also offer great support to customers using big data technologies. We continue to invest in this core value proposition and fund new projects that add to it. As an example - we have invested in systems for accelerating data access in the Cloud and will be able to, in future, cache entire result sets and improve the experience for analysts. We are investing in better tools for our core user segments like analysts and data scientists. We also continue to bring in new open-source projects - like Airflow - to enhance our offering. We have recently added industry-first functionality like heterogeneous clusters that take unprecedented advantage of the power of public clouds to provide a cost efficient solution for our customers.

What are you current traction details in the recent months? How do you think is your growth pace?
We have grown at a healthy pace over the past few years. We have been recognized for our work through numerous awards and accolades and have been able to solve the data analysis needs of our clients. We were able to grow 3x (worldwide) in the past year and aim to grow 2x to 2.5x (worldwide) this year.