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Circumference invests $483 million in Couchbase Inc. 💵|MySQL turns 30🎉|The 10 Database Platforms I'm keeping my eye on

Please excuse my analogies you will read about in the newsletter

Author

Gladstone Benjamin
May 19, 2025

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⁸What’s in today’s newsletter

Also, check out the the weekly Deep Dive - The 10 Database Platforms I’m keeping my eye on this year

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NOSQL

TL;DR: Circumference Group LLC's $483 million investment in Couchbase Inc. highlights confidence in its innovative NoSQL technology and growing importance of data solutions in a competitive tech landscape.

  • Circumference Group LLC has acquired a $483 million stake in Couchbase Inc., signaling strong investment confidence.  

  • Couchbase is recognized for its innovative NoSQL database technology, meeting modern application demands effectively.  

  • The investment underscores the growing significance of scalable and performance-oriented data solutions in a data-driven economy.  

  • This backing could attract more institutional investors, enhancing Couchbase's market position amidst increasing tech competition.  

Why this matters: Circumference Group's substantial investment in Couchbase highlights the critical role of scalable data solutions amid surging digital transformation. This move may boost Couchbase's market stature, signaling its potential to lead in the competitive database solutions arena, further drawing attention from institutional investors focused on tech innovation.

RELATIONAL DATABASE

TL;DR: MySQL turns 30 and remains valued for reliability, but faces competition from NoSQL and cloud databases, necessitating innovation and community engagement to retain relevance.

  • MySQL celebrated its 30th anniversary but faces competition from emerging NoSQL and cloud-native databases.  

  • Users appreciate MySQL's robustness, yet seek flexibility, prompting a shift in database preference and technology.  

  • The rise of alternatives like MongoDB and PostgreSQL indicates a diversifying trend in data management strategies.  

  • MySQL must innovate and engage its community to maintain relevance in a competitive and evolving landscape.

Why this matters: As technology evolves, MySQL's challenge to maintain relevancy underscores the necessity for innovation in established open-source systems. The diversification in data management strategies reflects the changing needs of organizations seeking flexibility and scalability, reinforcing the importance for traditional players to adapt to new paradigms.

DATA ARCHITECTURE

TL;DR: The article highlights the crucial role of high-quality data in AI success, warning that poor data leads to biases, flawed analyses, financial losses, and underscores the need for ethical data governance.

  • The article emphasizes that high-quality data is essential for the success of AI systems across industries.  

  • Poor data quality can lead to biased outputs and flawed analyses, potentially reinforcing existing discriminatory practices.  

  • Neglecting data integrity risks significant financial losses and reputational damage due to AI failures and mistakes.  

  • Investing in data quality builds consumer trust, drives innovation, and is vital for ethical AI practices.  

Why this matters: As AI adoption escalates, data quality becomes crucial in avoiding biased or erroneous outcomes. Organizations ignoring this risk not only financial but also ethical repercussions. Investing in robust data management practices ensures reliable AI performance, cultivates consumer trust, and fosters industry leadership in competitive markets, thus promoting sustainable growth.

TL;DR: Qlik's newly launched Open Lakehouse combines data lakes and warehouses, enhancing data management flexibility, enabling real-time analysis, and potentially transforming industry standards for analytics and decision-making.

  • Qlik's Open Lakehouse combines features of data lakes and warehouses to enhance data management and analysis.  

  • It supports both structured and unstructured data, offering flexibility that traditional solutions do not provide.  

  • The solution enables real-time data access and complex query execution without compromising performance.  

  • Qlik's innovation could transform data analytics practices, encouraging industry-wide shifts in data architecture approaches.  

Why this matters: Qlik's Open Lakehouse offers a transformative solution for data management, enabling rapid insights and improved decision-making by integrating the strengths of data lakes and warehouses. This innovation enhances data flexibility and real-time analysis, influencing industry data architecture trends and setting a new standard for efficient data utilization.

DATABASE DEVELOPMENT

Courtesy: tamr.com

TL;DR: DataOps is reshaping application development by promoting collaboration between data engineers and developers, enhancing data management, and fostering innovation and agility to meet evolving business demands.

  • DataOps is transforming application development by enhancing collaboration between data engineers and developers for better data management.  

  • The methodology integrates DevOps principles, offering continuous delivery and iterative approaches to manage data workflows effectively.  

  • Adopting DataOps fosters a culture of innovation and agility, allowing organizations to swiftly respond to changing data requirements.  

  • Increased collaboration within DataOps teams leads to improved data quality and better decision-making processes in competitive markets.  

Why this matters: DataOps revolutionizes data management, enabling businesses to leverage data for competitive advantage through agility and innovation. By dismantling silos, it aligns teams, enhances data quality, and refines decision-making. Organizations can now efficiently respond to and anticipate market shifts, strengthening their position in a data-driven world.

DEEP DIVE
The 10 Database Platforms I’m keeping my eye on this year

This Deep Dive is at the core of this newsletter. I have the good fortune of working hands-on with a lot of disparate database systems and platforms. I am expected in my “day job” to be at the vanguard of what is coming down the line in the database world.

I have no problem with that. It keeps me sharp, and in turn, keeps you the subscriber of this newsletter, informed with the latest database news, in a world awash in a deluge of AI news.

I always stress is that the large language models that are in vogue right now have to get their training data from somewhere. That’s why as data practioners, we have a very unique opportunity to be entrenched in the AI/ML ecosystem, without being de facto AI/ML practioners.

It’s kind of like being part of the Yukon gold, without being the poor sap panning for gold. Think of yourself as the one selling the pick axes, pans, dungarees, horses, etc. You will always have a place.

Enough analogies. Here is my list of the 10 Database Platforms I’m keeping my eye on this year:

Neo4j
A graph database optimized for storing and querying complex relationships using nodes and edges. It uses the Cypher query language and is widely used in fraud detection, recommendation systems, and network analysis. Highly scalable and ACID-compliant, supporting both native and embedded deployments.

Weaviate
An open-source vector database designed for semantic search and AI applications.
It supports hybrid search (keyword + vector), automatic vectorization, and integrates with popular embedding models. Weaviate offers modular architecture, RESTful APIs, and multi-tenant capabilities.

Pinecone
A fully managed vector database tailored for real-time similarity search and large-scale AI workloads. It provides fast, low-latency retrieval of high-dimensional vector data with automatic indexing and scaling. Used in recommendation systems, RAG pipelines, and semantic search applications.

Supabase
An open-source Firebase alternative built on PostgreSQL, offering instant APIs, auth, and real-time updates. It includes a hosted database, RESTful and GraphQL APIs, and edge functions for serverless computing. Popular among developers for quickly building full-stack apps with minimal configuration.

Redis
An in-memory data structure store commonly used as a cache, message broker, or real-time database. It supports data types like strings, hashes, lists, sets, and streams with microsecond latency. Redis also offers features like persistence, replication, and clustering for high availability.

DuckDB
An in-process SQL OLAP database optimized for analytics workloads on columnar data.
It runs embedded in Python, R, or other applications, enabling fast analytics without data movement. Ideal for local data exploration, data science, and interactive dashboards.

Couchbase
A distributed NoSQL database combining key-value and document storage with SQL-like querying via N1QL. Supports mobile sync, full-text search, eventing, and built-in caching for responsive apps. Designed for scalability, with high availability and flexible deployment options.

SQLite
A lightweight, embedded relational database used in mobile apps, IoT devices, and small-scale software. It is serverless, ACID-compliant, and stores the entire database in a single file. Widely adopted for simplicity, zero-configuration, and local data persistence.

SingleStore
A distributed SQL database built for real-time analytics on structured, semi-structured, and streaming data. It unifies transactional and analytical workloads with in-memory rowstore and disk-based columnstore. Offers MySQL compatibility and high performance for mixed OLTP/OLAP applications.

ScyllaDB
A high-performance, NoSQL database compatible with Apache Cassandra but built in C++ for lower latencies. It delivers high throughput and efficient use of modern hardware through asynchronous I/O and shard-per-core design. ScyllaDB is used in real-time apps needing massive scalability, such as IoT and time-series workloads.

Happy Victoria Day to all of my Canadian subscribers! Be careful with the fireworks.

Gladstone Benjamin