Breaking Down the Walls: Streamlining Strategy with RavenDB

If database technologies offered performance, flexibility, and security, most professionals would be happy to get two of the three, and they might have to expect to accept some compromises, too. Systems optimized for speed demand manual tuning, while flexible platforms can impose costs when early designs become constraints. Security is, sadly, sometimes, a bolt-on, with DBAs relying on internal teams’ skills and knowledge not to introduce breaking changes.

RavenDB, however, exists because its founder saw the cumulative costs of those common trade-offs, and the inherent problems stemming from them. They wanted a database system that didn’t force developers and administrators to choose.

Abstracting away complexity

Oren Eini, RavenDB’s founder and CTO, was working as a freelance database performance consultant nearly two decades ago. In an exclusive interview, he recounted how he encountered many capable teams “digging themselves into a hole” as the systems in their care grew in complexity. Problems he was presented with didn’t stem from developers not possessing the required skills, but rather from system architecture. Databases tend to guide their developers towards fragile designs and punish developers for following those paths, he says. RavenDB was a project that began as a way to reduce friction when the unstoppable force of what’s required meets the mountain of database schema.

The platform’s emphasis is on performance and adaptability without (ironically) at some stage requiring the services of people like Oren. Armed with a bag full of experience and knowledge, he formed RavenDB, which has now been shipping for more than fifteen years – well before the current interest in AI-assisted development.

The bottom line is that over time, the RavenDB database adapts to what the organization cares about, rather than what it guessed it might care about when the database was first spun up. “When I talk to business people,” Eini says, “I tell them I take care of data ownership complexity.”

For example, instead of expecting developers or DBAs to anticipate every possible query pattern, RavenDB observes queries as they are executed. If it detects that a query would benefit from an index, it creates one in the background, with minimal overhead on extant processing. This contrasts with most relational databases, where schema and indexing strategies are set by the initial developers, so are difficult to alter later, regardless of how an organization may have changed.

Oren draws the comparison with pouring a building’s foundations before deciding where the doors and support columns might go. It’s an approach that can work, but when the business changes direction over the years, the cost of regretting those early decisions can be alarming.

Speaking ahead of the company’s appearance at the upcoming TechEx Global event in London this year (February 4 & 5, Olympia), he cited an example of a European client that struggled to expand into US markets because its database assumed a simple VAT rate that it had consigned to a single field, a schema not suitable for the complexities of state and federal sales taxes. From seemingly simple decisions made in the past (and perhaps not given much thought – European VAT is fairly standard), the client was storing financial pain and technical debt for the next generation.

Much of RavenDB’s attractiveness is manifest in practical details and small tweaks that make databases more performant and easier to address. Pagination, for example, requires two database calls in most systems (one to fetch a page of results, another to count matching records). RavenDB returns both in a single query. Individually, such optimizations may appear minor, but at scale they compound. Oren says. “If you smooth down the friction everywhere you go, you end up with a really good system where you don’t have to deal with friction.”

Compounded removal of frictions improves performance and makes developers’ jobs simpler. Related data is embedded or included without the penalties associated with table joins in relational databases, so complex queries are completed in a single round trip. Software engineers don’t need to be database specialists. In their world, they just formulate SQL-like queries to RavenDB’s APIs.

Compared to other NoSQL databases, Raven DB provides full ACID transactions by default, and reduced operational complexity: many of its baked-in features (ETL pipelines, subscriptions, full-text search, counters, time series, etc.) reduce the need for external systems.

In contrast with DBAs and software developers addressing a competing database system and its necessary adjuncts, both developers and admins spend less time sweating the detail with Raven DB. That’s good news, not least for those that hold an organization’s purse strings.

Scaling to fit the purpose

RavenDB is also built to scale, as painlessly as it handles complex queries. It can create multi-node clusters if wanted so supports huge numbers of concurrent users. Such clusters are created by RavenDB without time-consuming manual configuration. “With RavenDB, this is normal cost of business,” he says.

In February this year, RavenDB Cloud announced version 7.2, and this being 2026, mention needs to be made of AI. Raven DB’s AI Assistant is, “in effect, […] a virtual DBA that comes inside of your database,” he says. The key word is inside. It’s designed for developers and administrators, not end users, answering their questions about indexing, storage usage, or system behavior.

AI as a professional tool

He’s skeptical about giving AIs unconfined access to any data store. Allowing an AI to act as a generic gatekeeper to sensitive information creates unavoidable security risks, because such systems are difficult to constrain reliably.

For the DBA and software developer, it’s another story – AI is a useful tool that operates as a helping hand, configuring and addressing the data. RavenDB’s AI assistant inherits the permissions of the user invoking it, having no privileged access of its own.