On Engagement Database. Interview with Ravi Mayuram
“The efficacy of any recommendations or results is entirely dependent on ensuring the right data is being fed into purpose-built models — not simply enabling a connection to Google TensorFlow or Apache Spark MLlib.”– Ravi Mayuram
I have interviewed Ravi Mayuram, Senior Vice President of Engineering and CTO of Couchbase. Main topics of the interview are: how the latest technology trends are influencing the database market, what is an engagement database, and how Couchbase plan to extend their data platform.
Q1. How are the latest technology trends- such as for example cloud-native, containers, IoT, edge computing- influencing the database market?
Ravi Mayuram: Businesses today are tasked with solving much harder technological problems than ever before.
A massive amount of data is being generated at an unprecedented pace, and companies are pursuing several technology trends (cloud-native architectures, containerization, IoT data management solutions, edge computing) to maintain or uncover new competitive advantages.
This wide array of trends require a combination of several types of solutions. Common approaches of adding yet another backend toolkit are no longer competitive. Instead, bringing the power of high speed data interaction out of the database and into the hands of users has stretched developers and the tools they use.
What’s becoming more apparent is that while the latest technologies can certainly address capturing and managing this data explosion, the hard part is to minimize database sprawl by meeting different use cases in a consolidated platform. Only then can you get the full benefit of intelligently combining different data sources and technologies. And that’s precisely where I see these trends influencing the database market: a need to consolidate multiple point solutions into a single platform that will allow us to cover a much wider range of use cases, and at the same time, contain the sprawl.
With a database technology like Couchbase, we’ve recognized this challenge and built a single platform to manage that convergence, giving you access to your data in a flexible, intuitive way. The database itself is more intelligent than ever before – self-managing, more easily deployable, and handling failures better. We’ve focused on introducing new features that allow developers to extract more value (intelligently!) from their data sources via new analytics, eventing, and text search services – all in a single platform. The end result is a more seamless experience across a wider range of technology trends and endpoints, helping our customers gain actionable insights from data captured and stored in Couchbase yet pushed out to the edge to enable user interaction better than ever before.
Q2. How have databases changed over the last 5 years?
Ravi Mayuram: Over the last 7-8 years, the NoSQL movement has matured tremendously. Initially, there was a vast divide in what traditional database systems offered and what NoSQL databases held promise to deliver. While the new databases solved the scale and performance problems, they were not mature in their industrial strength or were not enterprise-grade. These issues have been addressed, and more and more business-critical data now sits in NoSQL systems. These modern database systems are also getting battle tested under production workloads, across every industry imaginable. This has made our engagement database increasingly robust and dependable for developers to stand up far more complex applications, while delivering significant value to the customers they serve.
Q3. What are the main use cases where organisations will benefit in transitioning workloads from relational databases to non relational multi-cloud environments?
Ravi Mayuram: Enterprises have chosen Couchbase to run their most mission critical applications for its rich set of capabilities – from the cloud to the edge.
Today’s database capabilities are increasingly defined by the end user application of the tool. For example, due to the dynamic nature of applications as they mature, the database must have a flexible schema that can adapt as needed. Similarly, it must support both clustered server environments as well as in “always on” mobile applications. The database must also be able to grow and scale as needed along with supporting highly available environments and global, replicated, environments.
At a high level, our customers are building user profiles, session stores, operational dashboarding, and personalization services for their Customer 360, Field Service, Catalog & Inventory Management, and IoT Data Management solutions.
And that’s because relational databases can’t keep up with the demands of these types of applications anymore. More data than ever before is now being generated at every single customer and employee touch point, and the ability to capture new types of data on the fly, and securely move, query, and analyze that data requires a flexible, geo-distributed, robust data platform. Couchbase Data Platform consolidates many tiers into one – caching, database, full text search, database replication technologies, mobile back end services, and mobile databases. This consolidation of tiers enables architects and developers to build and deliver application that have not been brought to market before, and at the same time, modernize existing applications efficiently and quickly.
Specifically, some use-cases include content entitlement, site monitoring, shopping cart, inventory/pricing engine, recommendation engine, fleet tracking, identity platform, work order management, and mobile wallet to name a few.
Q4. How do you define an end-to-end platform?
Ravi Mayuram: From a technical requirements perspective, there are six key concerns I believe a true end-to-end platforms solves for:
- Intuitive: Accessing data has to be easy. It must follow industry conventions that are familiar to SQL database users. Using standard SQL query patterns allows applications to be developed faster by leveraging existing database knowledge whether for ad hoc querying, real-time analytics, or text search.
- Cloud: The platform must be built for any type of cloud: private, public, hybrid, on-premises. And it has to be global, always available, all the time.
- Scale: The platform must be built for scale. This is a given. As your user demand spikes, your data platform needs to support that.
- Mobile: The platform must be seamlessly mobile. Data must be available at the point of interaction in today’s digital world, and that has grown ever-so important as more customers and employees have moved to mobile devices for their everyday activities.
- Always-on availability: The platform should always be on (five nines availability), and always be fast. No downtime, because who can afford downtime in today’s global economy?
- Security across the stack: The platform needs to be secure, end-to-end. A lot of customer and business data sits in these databases. You must be able to encrypt, audit, protect, and secure your data wherever it lives – on the device, over the internet, in the cloud.
Based on these criteria, I’d define an end-to-end platform as one just like Couchbase provides.
Q5. You have positioned Couchbase as the ‘engagement database’. How would you define an engagement database? What are the competitive differentiators compared with other types of databases?
Ravi Mayuram: An engagement database makes it easier to capture, manipulate, and retrieve the data involved in every interaction between customers, employees, and machines. The exponential rise of big data is making it more costly and technically challenging for massively interactive enterprises to process – and leverage – those interactions, especially as they become richer and more complex in terms of the data, documents, and information that are shared and created.
Many organizations have been forced to deploy a hard-to-manage collection of disparate point solutions. These overly complex systems are difficult to change, expensive to maintain, and slow, and that ultimately harms the customer experience.
An engagement database enhances application development agility by capitalizing on a declarative query language, full-text search, and adaptive indexing capabilities, plus seamless data mobility. It offers unparalleled performance at scale – any volume, volatility, or speed of data, any number of data sources, and any number of end users with an in-memory dataset process, smart optimization, and highly performant indexing. And it does all this while remaining simple to configure and set up, easy to manage across the multi-cloud environments common in today’s enterprises, as well as globally reliable and secure in context of the stringent uptime requirements for business-critical applications.
Q6. Often software vendors offer managed services within their own cloud environments. Why did you partner with Rackspace instead?
Ravi Mayuram: One of the key tenets of Couchbase Managed Cloud was to offer our customers maximum flexibility without compromise – with respect to performance, security and manageability. By deploying within the customer’s cloud environment, we can achieve all three without any compromises:
- Co-locating applications and databases within the same cloud environment eliminates expensive hops of traversing cloud environment boundaries thus offering the maximum performance possible at the lowest possible latency.
- Enables the infrastructure and data to reside within the security boundaries defined by the customer to ensure a consistent security and compliance enforcement across their entire cloud infrastructure.
- Lastly it gives our customers choice and flexibility to get the best pricing on their cloud infrastructure from their provider of choice without a vendor in the middle charging a premium for the same infrastructure as some of our competitors force them to.
Along with our design principles, it also became evident to us early on, that instead of building this on our own we would serve our customers better by partnering with someone who has developed significant managed services expertise. We quickly zoned in on Rackspace – a pioneer in the managed services industry – as our partner of choice. We believe this best of breed combination of Couchbase’s database expertise with our powerful NOSQL technology and Rackspace’s fanatical support model and dev-ops expertise offers our customer a compelling option as evidenced by the overwhelming response to the product since its launch.
Q7. What technical challenges do developers need to overcome as they begin to integrate emerging technologies such as AI, machine learning and edge computing into their applications?
Ravi Mayuram: AI/ML brings together multiple disciplines from data engineering to data science, and the cross-disciplinary nature of these implementations is often at the core of the technical challenges for developers. Combining the knowledge of how the models and algorithms work with a firm and grounding in the data being fed into those models, is critical yet challenging. Moreover, with machine learning we have a fundamentally difficult debugging problem, rooted in requisite modeling creativity and extensive experimentation. Thus the efficacy of any recommendations or results is entirely dependent on ensuring the right data is being fed into purpose-built models — not simply enabling a connection to Google TensorFlow or Apache Spark MLlib.
Add in edge computing, and we are further confounded by the challenges of big data, from streaming analytics requiring active queries where the answers update in real-time as the data changes, to long-term storage and management of real-time data, both on the cloud and on the edge.
Q8. Talking with your customers, what are their biggest unmet, underserved needs?
Ravi Mayuram: For many of our customers, it comes down to a matter of scale. Information architectures in enterprises have evolved over time to include many solutions, all aimed at different needs. That makes it hard to really capitalize on the data that is now an asset for every business. As traffic grows, it can be impossible to adequately scale performance, a headache to manage multiple complex software solutions, avoid duplication of data, and difficult to quickly develop applications that meet the modern expectations for user experience.
As we continue to evolve our platform, we look for opportunities to solve for these challenges. We architected the platform from the ground up to meet the demands of enterprise performance. We are consolidating more services in the database tier, bringing logic to the data layer to make sure these businesses are more efficient about how they capitalize on their data assets. We make sure we leverage language familiar to developers and we contribute to and build toward industry standards.
Ultimately, we want to provide a data platform that both empowers architects to solve their near-term issues and supports their long-term digital strategy, whatever that may be.
Q9. What advice would you offer enterprises for managing database sprawl?
Ravi Mayuram: “Database sprawl” has continued to be one of the biggest issues facing companies today.
As applications continue to evolve, rapidly changing requirements have led to a growing number of point solutions at the data layer. The organization is then forced to stitch together a broad array of niche solutions and manage the complexity of changing API’s and versions. Without a platform to contain this sprawl, companies are moving data between systems, inexplicably duplicating data, changing the data model or format to suit each individual technology while working to learn the internal skills necessary to manage all of them. That’s why so many companies are choosing a platform like Couchbase, to consolidate these technologies, enabling them to bring their solutions faster to market with streamlined data management.
Q10. How do you plan to extend your platform?
Ravi Mayuram: As our customers continue to converge data technologies onto Couchbase, we will remain steadfast on building the most robust, highly-performant enterprise platform for data management. At the same time, systems are expected to become more and more intelligent. As we automate more and more database services, we envision increasingly autonomous systems – that can self-manage, and be self-healing. We’ve already built tools like our Autonomous Operator for Kubernetes that help with the heavy lifting in cloud environments. We’re providing new capabilities like the Couchbase Analytics service that will allow users to get real-time analytics from their operational data, and Couchbase Eventing for server-side processing.
Meanwhile, as the amount of data grows, so does the need to extract more value from that data. We are aiming to further decrease the total cost of ownership by reducing operational complexity and supporting more multi-tenancy and high application density scenarios. All of these features will extend our platform into a more manageable, responsive, and intelligent system for our users.
As Senior Vice President of Engineering and CTO, Ravi is responsible for product development and delivery of the Couchbase Data Platform, which includes Couchbase Server and Couchbase Mobile. He came to Couchbase from Oracle, where he served as senior director of engineering and led innovation in the areas of recommender systems and social graph, search and analytics, and lightweight client frameworks. Also while at Oracle, Ravi was responsible for kickstarting the cloud collaboration platform. Previously in his career, Ravi held senior technical and management positions at BEA, Siebel, Informix, HP, and startup BroadBand Office. Ravi holds a Master of Science degree in Mathematics from University of Delhi.
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