Navigating the Database Ecosystem in 2025

By Charles Mahler / Developer
Oct 20, 2025

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In 2025, the database ecosystem is more diverse and interconnected than ever before. From AI-assisted natural language queries that analyze your data to open table formats that make it easy to bridge systems, data infrastructure is moving towards openness, intelligence, and composability. Modern databases are no longer isolated systems; they are part of a broader ecosystem where interoperability is as important as performance.

In this post we’ll explore the state of the database landscape in 2025, the trends to look forward to, the technical tradeoffs to consider when choosing a database, and the different types of databases. If you want even more detail on these topics, be sure to watch the full webinar this post is based on.

Interoperability

Open formats like Parquet, Arrow, and Iceberg have steadily increased interoperability between databases. This not only makes adopting new technologies easier but also reduces the risk of vendor lock-in for developers. It also allows for zero-copy data sharing across systems and enables OLTP and OLAP data to be combined for improved analytics.

Multi-Modal Databases

In the early days of NoSQL databases, many of them filled specific niche roles for a particular type of data. In recent years, trends have shifted in the other direction, with databases like Postgres extending to support analytics, vector embeddings, and semi-structured data.

AI-Enhanced Databases

AI is enabling new features at all database levels. At the highest level, LLMs are enabling users to query and analyze data without requiring technical expertise. Tools like the InfluxDB 3 MCP server make it possible to integrate InfluxDB with your favorite AI tools. AI is also being used to do tasks like recommend new indexes or materialized views to improve performance and optimize SQL queries automatically based on performance data. And companies like Google are researching entirely new types of indexes that could replace traditional B-tree indexes, improving performance and reducing storage costs.

What makes databases perform differently?

Every database is a set of engineering tradeoffs. Understanding how these tradeoffs affect performance can help you choose and tune the right system.

Storage Structure

The way a database organizes data on disk fundamentally determines its performance characteristics. Different engines use different data structures to optimize for read latency, write throughput, or a balance between the two. The two most common paradigms are B-trees and log-structured merge-trees (LSM).

  • B-trees: B-trees are one of the most widely used storage structures in relational databases such as PostgreSQL, MySQL, and SQLite. They organize data hierarchically, storing key-value pairs in sorted order with references to child nodes, keeping the tree balanced as inserts and deletes occur. B-Trees are optimized for random reads and point lookups, but struggle with high write workloads.
  • LSM-trees: LSM-trees take a radically different approach, designed primarily to optimize write performance. Instead of updating data in place, writes are first appended sequentially to an in-memory structure and then periodically flushed to disk as sorted segments, known as SSTables. Over time, background processes called compactions merge these SSTables to remove duplicates and maintain sorted order. LSM-trees are ideal for workloads involving heavy write volumes but are less efficient for certain types of queries.

Indexing

Indexes accelerate queries but come at a cost. Each index must be updated on write, which can slow down ingestion. The tradeoff can be seen as the choice between query latency vs. write throughput performance.

Storage Format

Another factor in database performance is data organization on disk. The first option is row-oriented storage, which works best for OLTP workloads. The second option is column-oriented storage, which is best for OLAP workloads.

Compression

Compression algorithms like ZSTD or Snappy reduce the storage footprint and I/O overhead by encoding and decoding data. Different compression algorithms have various tradeoffs, the most significant ones being compression ratio, CPU utilization, and latency.

Durability

Another factor in database performance is the durability requirements for your data. If you can’t afford to lose any data, you will take a performance hit by ensuring that all your data is persisted to disk as it is being written and replicated across multiple servers.

How to choose the right database

Selecting the right database for your use case can be as much of a business decision as a technical one. Here are some key factors to take into consideration:

Data Access Patterns

The most important thing to consider is what type of data you will be working with. If you know you will have a write-heavy workload, then you know upfront that you should go with a database designed to support that from the ground up. If you are going to be doing a lot of analytics on your data, you will want to use a database that stores data in a columnar format.

Transactional Requirements

Do you need strict transactions or can your application tolerate eventual consistency? If you don’t need ACID compliance, you can get significant performance benefits from choosing a database that has eventual consistency.

Scalability

Will your volume of data be small enough that simply scaling a server vertically will be sufficient? In that case you can avoid most of the complexity involved with distributed databases.

In-House Knowledge

If your team is already familiar and has expertise operating a specific database, it might be worth choosing that instead of a theoretically superior option that would require your entire team to learn a new tool.

Business Stage

If you are a startup that needs to be able to iterate and move fast, you might see more benefits from a NoSQL database that makes it easy to modify your data model as requirements change.

An overview of different database models
Database Type Overview
Relational Databases Structured around predefined schemas and ACID-compliant transactions, relational databases guarantee strong consistency and reliability. They excel at workloads involving complex joins, aggregations, and strict transactional integrity. Ideal for financial systems, ERP platforms, and traditional business applications.
Key-Value Stores The simplest database model, storing data as key-value pairs. They offer extremely low-latency lookups and are well-suited for caching, session management, and user profile storage. They trade query flexibility for speed and scalability.
Document Databases Store data in semi-structured JSON-like formats with flexible schemas that adapt as applications evolve. Popular for content management systems, user data, and applications with dynamic or evolving data models.
Time Series Databases Purpose-built for high-ingest, time-stamped data, such as metrics, logs, and IoT sensor readings. Use optimized storage engines for sequential writes, time-based compression, and downsampling. Excellent for monitoring, observability, and real-time analytics.
Graph Databases Represent data as nodes and edges, ideal for modeling relationships and networks such as social graphs, recommendation systems, and fraud detection. Use graph traversal algorithms for efficient link and path analysis.
Columnar Databases Store data by column rather than row, enabling massive scan and aggregation performance ideal for analytical workloads. Often include vectorized execution, advanced compression, and query acceleration for BI and analytics use cases.
In-Memory Databases Keep most or all data in RAM for microsecond-level latency. Common in real-time analytics, caching layers, and high-frequency trading systems. Trade extreme speed for higher infrastructure cost and volatility management.
Search Databases Built for full-text, fuzzy, and relevance-based search using inverted indexes and scoring algorithms. Commonly used for log analytics, e-commerce search engines, and application monitoring. Optimized for retrieval speed rather than transactional updates.
Vector Databases Store and query high-dimensional embeddings generated by ML models. Power semantic search, recommendation engines, and LLM context retrieval (RAG). Optimize for approximate nearest neighbor (ANN) search and hybrid retrieval combining text, metadata, and vectors.
NewSQL Databases Combine the SQL interface and ACID guarantees of traditional RDBMS with the horizontal scalability of NoSQL systems. Ideal for global-scale transactional applications that demand strong consistency and distributed fault tolerance.

The bottom line

The database ecosystem in 2025 is not about finding one perfect database, it’s about building a composable data architecture. Open formats like Parquet and Iceberg break down silos, while AI copilots unlock intelligent insights. Meanwhile, the fundamentals like storage engines, indexing strategies, and compression still determine raw performance.

The most successful organizations aren’t tied to a single engine. They choose the right tool for each job, blending transactional, analytical, and vector systems into one unified data ecosystem. To continue learning about the modern database ecosystem, be sure to watch the full webinar.