SQL’s decline in the Tiobe index
Over the decades, SQL has been the core protocol powering structured data across industries, from finance to logistics. It’s consistent, reliable, and practically universal when it comes to relational databases. But today, we’re seeing a shift in how the programming world views it. According to the June 2025 Tiobe Index, SQL has dropped to 12th place, its lowest position since being included in the ranking. That’s notable. Not because SQL is going away, it’s not, but because the terrain is changing.
The Tiobe Index uses metrics from over 20 online sources, including Google, Bing, Amazon, and Wikipedia, to gauge a language’s relative popularity among global developers and organizations. When SQL falls out of the top 10, leaders should pay attention, not to panic, but to prepare. It signals a pivot in developer demand and a shift in system design preferences.
This doesn’t mean SQL is obsolete. It’s still the backbone of most structured data systems, ERP systems, transactional databases, financial systems, the list is long. What’s happening instead is that the data landscape has become multidimensional. Fixed schemas and tabular rows are no longer sufficient for the kind of decision analytics or AI modeling businesses want. People want speed, flexibility, and distributed access. SQL serves many foundational roles, but it isn’t the best tool when handling unstructured or real-time data.
If your company is working only with structured datasets, SQL is still the standard. But if you’re building anything that needs to harness massive, unstructured data, like AI pipelines, log stream analysis, or real-time social data intelligence, then your system architecture needs to do more. The drop in rank is a signal, not of irrelevance, but of limitation. Tech leaders should interpret this not as a reason to discard SQL, but as a reason to invest in complementary systems that fill the gaps SQL doesn’t cover well.
Bottom line: SQL remains vital, but its lower ranking in developer usage metrics reflects a bigger shift toward multi-model, flexible data infrastructure. If you’re building with the future in mind, you should be planning for that now.
The rise of NoSQL and AI-driven demand
We’re seeing a clear momentum shift in how companies approach data systems, not because SQL is broken, but because new types of data and use cases have outgrown its original design. Artificial Intelligence is one of the major driving forces behind this. AI doesn’t run on clean, structured rows; it runs on large volumes of messy, unstructured data, images, logs, language, behavior patterns. For that kind of workload, NoSQL systems are more efficient.
Paul Jansen, CEO of Tiobe, made this point directly: “SQL will remain the backbone and lingua franca of databases for decades to come. However, in the booming field of AI, where data is usually unstructured, NoSQL databases are often a better fit.” He’s right. You don’t have to replace SQL. You just need to recognize that it’s not built to handle dynamic, loosely typed, or unstructured input at scale.
NoSQL platforms like MongoDB, Cassandra, and Redis offer a different kind of flexibility. They allow companies to adapt their data models more rapidly, without pre-defining complex table schemas or trying to fit inconsistent input into rigid models. This is especially useful in AI initiatives, where data variety and volume are both unpredictable and high.
This trend also connects to the growing dominance of dynamically typed languages like Python. Developers working in Python aren’t interested in verbosity or rigid structure if it slows prototyping and iteration. Systems that support fast, flexible development win in this environment. NoSQL is one of those systems.
For executives investing in digital transformation or AI, ignoring NoSQL means missing out on speed, scalability, and real-time responsiveness. It’s not about choosing one database type over another. It’s about using the right tool for the right data. Structured transaction? SQL. Variable content stream or high-volume sensor data? NoSQL.
The choice is strategic. As data grows more complex, companies that adopt modular, multi-model architectures are going to create competitive advantages faster. The rise of NoSQL isn’t a threat to SQL, it’s a reflection of expanded capability and new demands. The systems you design today need to handle structured and unstructured data side by side, without breaking under pressure. That’s not hype, it’s operational reality.
Debate over SQL’s status as a programming language
SQL’s role in the programming world has been debated for years, not because of its usefulness, but because of how people define what qualifies as a programming language. This debate has had less to do with function and more to do with classification. Back in 2004, SQL was removed from the Tiobe Programming Index after critics argued it wasn’t a true programming language. That decision held until 2018, when SQL was recognized as Turing-complete, a key criterion for programming languages, and was re-added.
This back-and-forth matters, especially when you’re leading an organization that invests heavily in its tech stack. Tools like SQL evolve within their ecosystems, but shifts in developer perception can affect hiring, training, and architectural decisions. When developers stop viewing a technology as “cutting-edge,” it doesn’t mean the tool lost its functionality, it just means it’s no longer where innovation momentum is strongest.
From a business perspective, the debate around SQL is less relevant than the outcomes it delivers. SQL continues to manage trillions of dollars in global transactions every day. The reason it feels “different” from other languages is because it serves a specific function: interacting with databases efficiently. That level of focus doesn’t strip it of legitimacy, it gives it clarity.
Still, the reclassification in 2018 reminds us that the tech sector isn’t static, it redefines itself regularly. Leaders should stay updated not just on technical developments, but also on these definitional shifts that influence hiring pipelines, curriculum design, system certifications, and platform investments.
Companies that rely heavily on structured data, for things like compliance reporting, supply chain operations, or customer relationship systems, should view SQL not as old tech, but as specialized tech. Whether or not it fits into a textbook definition of a programming language isn’t the point. What matters is that it delivers speed, clarity, and precision in areas where those still define the edge.
Documentation, standards, and professional development paths evolve when these kinds of classification shifts occur. Being aware of them means less catch-up later.
Python’s dominance in programming language rankings
Python continues to dominate both industry metrics and practical adoption. It’s ranked #1 in the June 2025 Tiobe index with a 25.87% rating, and it holds an even stronger lead in the PYPL index at 30.63%. These aren’t marginal leads, they’re significant. Python is clearly the preferred language for a broad range of developers, especially in high-growth areas like AI, machine learning, and data science.
Its simplicity and flexibility make it accessible to both new and experienced programmers. But its popularity also reflects deeper market shifts. Python is fast to deploy, supported by an expansive ecosystem of libraries, and highly adaptable to different domains, whether you’re building automation workflows, developing AI models, or integrating APIs.
For businesses, this matters because language preference drives talent decisions, framework compatibility, and longevity of codebases. If your teams are working in an environment where iteration speed, open-source support, and access to community-vetted tools are valued, Python will consistently show up high on the decision matrix. Vendors, developers, academic institutions, all are actively building around Python. That momentum only increases its value.
At the enterprise level, adopting Python is not just about aligning with developer preference. It’s about performance at scale: faster R&D cycles, easier onboarding for technical teams, and compatibility with modern data platforms. Major platforms like TensorFlow, PyTorch, and Pandas, all staples in AI and analytics, are Python-native. This alignment reduces friction in your architecture and accelerates actionable outcomes.
Decision-makers should understand that investments in Python-based systems are future-aligned. Maintaining internal capabilities in Python isn’t just a tactical move, it’s strategically sound. It’s where most of the AI and data innovation is already happening, and that pace is only going to accelerate. If your technical roadmap has AI, automation, or analytics anywhere on it, your teams should be fluent in Python, because the rest of the ecosystem already is.
Key highlights
- SQL’s declining popularity signals a shift in developer focus: SQL has dropped to 12th in the June 2025 Tiobe index, reflecting its reduced relevance in fast-changing data environments. Leaders should evaluate whether their architectures are too dependent on structured systems that don’t scale with today’s data challenges.
- NoSQL’s rise tracks directly with AI adoption and unstructured data needs: The acceleration of AI and real-time analytics has increased reliance on NoSQL systems, which offer the flexibility and scalability SQL lacks in these domains. Executives should invest in hybrid data infrastructure that supports both structured and unstructured workloads.
- SQL’s classification debate highlights the need for updated perspectives on legacy tech: Once excluded from major rankings due to its perceived limitations, SQL’s reclassification as a Turing-complete language underscores how legacy tools can evolve. Leaders should revisit outdated assumptions about their tech stack to avoid misaligned investment decisions.
- Python’s dominance shows where developer energy and innovation are headed: Python leads every major index due to its simplicity, adaptability, and deep integration with AI and data platforms. Executives should ensure their teams are fluent in Python to stay aligned with where technical talent and software ecosystems are concentrating.
