Organizations consistently struggle to manage what are known as “long-tail” data connectors. These are connectors that are less used and not available on many legacy data integration platforms. As a result, often organizations have had to build their own custom pipelines and, worse yet, maintain and rebuild those as data sources changed over time as requirements evolved.
This is inefficient, expensive, and simply untenable. We’re seeing a fundamental shift in the approach to analytics infrastructure to what I call the open data movement. It is transforming how organizations think about their data stack.
Why Community Development Changes Everything
Apache Spark emerged from a research project at UC Berkeley, not from billions of dollars spent on R&D at IBM or Oracle. It was a university project that anyone could contribute to, and Apache Spark has completely transformed how we process data at scale.
This isn't an anomaly, it's a pattern. Open source consistently delivers innovations years before proprietary vendors catch up. Today, Apache Spark has over 40,000 GitHub stars and 2,000 contributors. Why does this matter? Because when you have thousands of contributors working on a problem versus a closed team of even the best engineers, the math is simple: more minds, more perspectives, more breakthroughs.
This same pattern repeats across the technology ecosystem. Apache Kafka revolutionized real-time data streaming. dbt brought software engineering practices to SQL transformations. Apache Superset and Metabase democratized business intelligence. Each emerged from community needs, not corporate road maps.
How Open Source Solves the Long Tail Problem
Here is a challenge that keeps data leaders up at night: the long tail of connectors. Every organization uses a unique mix of tools, from mainstream platforms such as Salesforce to industry-specific applications that only a handful of companies use. Traditional vendors can't economically justify building connectors for niche tools that might only have 100 users globally.
This is where open source fundamentally changes the game. The math that doesn't work for proprietary vendors, where each connector needs to generate significant revenue, becomes irrelevant when the users themselves are the builders.
I've watched this transformation happen in real-time. Traditional ETL vendors typically plateau around 150 connectors because each new addition needs a business case. But open-source projects are reaching 600-plus connectors and growing, because every user with a need becomes a potential contributor.
How Long-Tail Connectors Enable AI Readiness
The truth about AI is that it isn’t about using the best LLMs or the most powerful GPUs. The real truth is that AI is only as good as the data it ingests. I've seen Fortune 500 companies with data locked in legacy ERPs from the 1990s, custom-built internal tools, and regional systems that no vendor supports. This data, often containing decades of business intelligence, remains trapped and unusable for AI training.
Long-tail connectors change this equation entirely. When the community can build connectors for any system, no matter how obscure, decades of insights can be unlocked and unleashed. This matters enormously for AI readiness. Training effective models requires real data context, not a selected subset from cloud native systems incorporated just 10 years ago. Companies that can integrate their entire data estate, including legacy systems, gain massive advantages. More data fed into AI leads to better results.
The open-source approach to connectors unlocks decades of previously siloed data, transforming it from a liability into an advantage.
Solving the Enterprise Trust Problem
That all sounds amazing, but enterprise leaders that I talk to often have reservations about open source because they need SLAs backed by support and they want to be assured of security. The good news, of course, is that open source doesn’t exclude any of those and so open source can be leveraged as the foundation for enterprise software.
According to market research, the open source services market is projected to reach $88.84 billion by 2030, growing at 16.8% CAGR. This explosive growth shows that enterprises have found their answer: hybrid models that provide both open source innovation and services-backed reliability.
It has become commonplace for companies, like ours, to offer 24/7 support with guaranteed response times, security patches and compliance certifications, advanced features for enterprise requirements, and smooth migration paths from community to enterprise editions.
The Future is Community-Driven
The open data movement represents a fundamental shift. Now, organizations don’t have to choose between innovation and reliability, or flexibility and control. The best solutions based on open source effectively combine all these qualities.
This revolution isn't coming. It's here. And the organizations embracing it are already seeing the benefits in their bottom line and their ability to innovate.