The Paradox of Perfect: An Intern's Take on Database Decisions

The Problem Isn't Lack of Choice

Coming out of college, I felt pretty confident about my understanding of databases. I had worked on several projects, learned about transactions, indexing strategies, locking, and writing more efficient queries by analyzing execution behavior. Three months into my internship at Modern Treasury, I discovered a database landscape with over 400 options, not through any assignment to evaluate them all, but through the simple reality of working in an environment where different database names drop casually in engineering meetings, Slack discussions, and architecture reviews.

Trying to learn more wasn’t just a matter of reading blog posts comparing SQL and NoSQL or browsing through common database decision frameworks. Instead, it was a fragmented universe where every new tool promised to be the perfect fit for a problem I didn't even know I had. This isn't just choice paralysis. I started calling this the paradox of perfect: the fear that somewhere in those 400+ options is a revolutionary system that could make everything infinitely better, if only you knew about it.

This is a field note about how a new generation of databases are solving problems in ways that make you go, "Wait, you can do that?" More importantly, it's about what I learned watching real database decisions unfold.

The Explosion Nobody Talks About

An entire universe of specialized databases has quietly emerged with entirely new approaches to data problems that make you question everything you thought you knew.

The Specialists Arrived

• ParadeDB brings BM25 full-text search directly into PostgreSQL without the complexity of maintaining a separate database technology for performant search.
• DragonflyDB achieves 25x higher throughput than Redis with multi-threaded architecture that delivers up to 80% lower infrastructure costs.

Distributed Databases Became Accessible

• CockroachDB achieves Google Spanner's consistency without atomic clocks through Hybrid Logical Clocks on commodity hardware.
• YugabyteDB proves you can have strongly consistent global secondary indexes across distributed nodes without sacrificing ACID guarantees.

New Paradigms Emerged

• Vector databases like Pinecone, Qdrant, and Milvus reshape how we think about storing and searching embeddings for semantic search and retrieval-augmented generation.
• Multi-model databases like SurrealDB and ArangoDB combine relational, graph, and document capabilities.

Analytics Went Wild

• DuckDB runs analytical queries directly in your browser with zero setup.
• ClickHouse delivers blazing-fast, real-time analytics on massive datasets with its columnar architecture.

The innovation happening in databases is similar to vertical innovations happening elsewhere—it’s about building tools that understand the unique shape of different problems. Each database represents years of engineering work solving domain-specific challenges that general-purpose databases handle poorly.

The Shiny Object Trap

All this innovation is exciting, but it creates a new problem: shiny object syndrome, the tendency to chase new tools simply because they are new. It is easy to get distracted by marketing claims and assume that switching databases will solve performance problems. However, many slowdowns actually stem from common patterns:

• N+1 query problems
• Missing indexes on frequently queried columns
• Over-normalization that force complex joins
• Poor connection pooling
• Schema designs that do not align with access patterns.

This is not an exhaustive list, but these challenges are rooted in query optimization, schema design, and application architecture rather than the database technology itself.

Sometimes a new tool truly is the right fit. But more often than not, the root cause lies elsewhere. I found myself excited about cutting-edge database technologies, only to realize that the real issues had nothing to do with the database itself. In those cases, switching would have been the wrong move. It is easy to be swayed by benchmarks without asking the more important question: does your specific workload actually match the conditions those tests were based on?

When every database promises to be "1000 times faster" or "infinitely scalable," it becomes difficult to evaluate which improvements would truly matter for your use case. And even when a tool seems like a perfect technical match, it is worth factoring in the broader ecosystem: community support, documentation, and operational maturity. Choosing a niche database isn’t inherently wrong, but it should be a conscious trade-off, not a side effect of excitement.

Boring Foundations, Strategic Specialization

I learned that this polyglot approach is a strategic philosophy. Watching database decisions unfold at Modern Treasury, where we process billions in payments monthly, I saw that we don't chase the new shiny object for every problem. The foundation is simple and "boring": PostgreSQL.

Coming in as an intern surrounded by exciting new database technologies, this initially seemed conservative. But I learned that decades of optimization, battle-tested reliability, and robust security make mature solutions a pragmatic choice.

The Reality of Financial Scale

When databases reach massive scale, routine maintenance operations become multi-hour engineering challenges. Edge cases that seem theoretical in textbooks become real operational concerns requiring careful planning. This is where the ACID properties—Atomicity, Consistency, Isolation, and Durability—become critical. They ensure transactions either complete fully or fail without side effects, maintain data integrity, operate without interfering with each other, and persist through system failures. For payment systems, this means no partial or inconsistent states—there is no room for eventual consistency when real funds are at stake.

At Modern Treasury, we take a targeted approach to specialization. As search demands grew with customer usage, we made a deliberate choice to deliver sub-second search performance at scale without compromising our core architecture. Rather than adding Elasticsearch as a separate search database, we extended PostgreSQL with ParadeDB—a PostgreSQL extension that provides powerful search capabilities. This choice was driven by practical engineering considerations: less ETL pipeline work needed to keep data in sync, consistent database models with our existing platform, and familiar SQL syntax that developers were already proficient with. The result was faster time to market while preserving the reliability and operational simplicity of our foundation. Core financial logic continues to reside in PostgreSQL, while search-heavy workloads leverage ParadeDB's capabilities within the same system.

This is the real-world strategy I discovered: the path to scalability and resilience is about thoughtfully matching specialized tools to specific problems while maintaining a reliable, "boring" foundation. This foundation-first approach doesn't mean never adopting new systems—sometimes the workload genuinely requires different architecture entirely, but the key is always diagnosing the problem first.

Diagnosis Before Database Shopping

During my internship, I gained firsthand insight into database decision-making through a real production issue. We had a billing job that calculated usage-based metrics for our customers, which would occasionally spike CPU usage on our main PostgreSQL database, creating a risk that the entire application could become unresponsive. This was not a flaw in PostgreSQL itself. The diagnosis revealed we were running heavy analytical workloads—scanning and aggregating large datasets—on a database optimized for fast transactional queries.

The solution reflected smart polyglot persistence, the practice of using multiple database technologies within a single system, each chosen for its specific strengths. We rebuilt the pipeline using Snowflake, a database built for analytical workloads that benefit from columnar storage and the separation of compute and storage. We did not replace PostgreSQL but kept it for what it does best while bringing in a purpose-built system for analytics.

The takeaway is not that one database is better than another but that different workloads need different tools. PostgreSQL excels at real-time transactional reads and writes. Snowflake is ideal for scanning and aggregating massive datasets. This experience taught me that the real engineering skill is not just in writing code but in understanding the shape of the problem and choosing the tool that is purpose-built to solve it.

What I Learned

The database landscape will continue to fragment as developers demand purpose-built solutions. The number of options is irrelevant. What matters is developing the judgment to use them strategically.

This internship transformed how I think about database decisions. I came in with textbook knowledge and left understanding that real engineering is about architectural trade-offs, not perfect solutions. As an intern, my goal shifted: it was no longer about learning every new database. It became about recognizing when a problem genuinely needs a specialized solution versus being distracted by impressive benchmarks.

The paradox of perfect that initially overwhelmed me resolved into something practical: strategic ignorance becomes a strength. I learned to focus on understanding the problem deeply and reasoning from first principles, then identifying the tools that best fit the actual constraints and goals. The most effective solutions are often a toolkit of complementary systems working together.

Sometimes the boring choice is optimal, sometimes it's insufficient—the skill is recognizing which situation you're in.

As I continue building my career, I know this mindset will matter more than any specific technical knowledge. This applies to decisions beyond databases such as choosing frameworks, cloud providers, architectural patterns, or deployment strategies. These tools will keep evolving, but the ability to cut through the noise and focus on understanding what you’re trying to solve—that’s what will make a real difference.

Working in an environment where these decisions affect billion-dollar payment flows taught me how to think like an engineer who ships systems people depend on. If you're looking for a place that pushes your thinking and helps you grow quickly, this kind of environment makes a real difference.