We Were Just Solving a Problem. Then We Saw 1,000 Stars.

A few days ago, something unexpected happened. We noticed that the Zingg GitHub repository had crossed 1,000 stars. GitHub has over 420 million repositories, but only 30 million are actively maintained. Of those, just 53,800 have crossed 1,000 stars — that is only 0.013%. We were heads-down — working on scale, accuracy, and learning from our users. We had simply been building.

But this number — 1,000 — made us pause. It was not the milestone itself that struck us. It was the story behind it. And the realization that maybe, just maybe, we have created something meaningful for more people than we knew.

It Started With One Person, One Problem

Zingg was not built in a conference room with market research reports. It began when our founder faced the problem firsthand — the kind of data problem that does not show up cleanly in dashboards. One that quietly holds companies back.

She was trying to answer a basic question that should have been simple: Who is the same person across these different datasets? There were no good tools. The problem was too nuanced for rules, too large for manual intervention, and too dynamic for traditional MDM. So she did what many engineers do: she tried to build something herself.

What followed was months of grappling with complexity — blocking strategies, fuzzy logic, clustering, deduplication, record linkage. And then came the realization: this was not just her problem. If she was facing it, others were too. And they deserved a solution.

The Decision to Go Open Source

We made a choice early on: Zingg would be free and open source.

Not because we had a business plan around it — but because we wanted others to benefit from the hard-earned lessons we were learning. We believed that the best way to solve this deep data challenge was in the open, alongside a community that cares.

What we did not expect was how many people would care.

Why Identity Resolution Still Needs Attention

What is fascinating is not that identity resolution is rare — it is that despite its rarity, it is central to almost every serious data initiative.

Marketing teams need it for 360-degree customer views. Risk and compliance teams need it for watchlist deduplication and fraud detection. Governments and non-profits need it for program eligibility, household-based targeting, or population deduplication.

And yet, many still attempt to hand-code heuristics or manually match entities in spreadsheets or data frames. That is not sustainable. The problem is too complex, too large-scale, and too foundational to keep solving in ad hoc ways.

Identity Resolution: Rare, Real, and Required

GitHub is home to over 420 million repositories. Around 30 million of those are active, maintained codebases.

Out of these: only 11 repositories focus on identity resolution. Just 172 touch on entity resolution. 322 explore fuzzy matching. 457 cover deduplication. 17 mention customer data platforms. And 26 address master data management.

It is telling. For a problem so foundational — connecting fragmented data into unified records — very few have taken it on. Why? Because it is hard. And yet, most teams either ignore it or try to solve it themselves — often hitting walls.

We Felt That Pain — So We Built Through It

Zingg is not just a tool. It is the result of years of direct experience with the edge cases, the false matches, the scale bottlenecks, and the architectural decisions no one talks about.

We built algorithms that can handle uncertainty, sparsity, and duplication — and continuously learn from feedback. We built for datasets with millions of rows. We built for teams who do not want to label data just to get started. And we built with love — because we knew how frustrating this problem could be when you are facing it alone.

Why the 1,000-Star Moment Matters

Crossing 1,000 stars is not just a GitHub milestone. It is a signal. It tells us that this problem matters. That people are looking for better solutions. That maybe, in a sea of open source, someone found value in what we have created.

We have never claimed to have all the answers. But we do know this: we understand this problem deeply. We have lived it. We have architected for it. And now, we want to help others solve it too — faster, better, and more transparently.

If You Are Solving This Too, Do Not Go Alone

If you are a data engineer stuck stitching together fragmented records, a CTO wondering why your customer data still is not unified, or a team lead trying to resolve people, suppliers, or products across platforms — we see you.

You could build your own identity resolution pipeline. Many have. But it takes time. It takes experimentation. And it takes living this problem, not just coding for it. That is why we built Zingg.

For marketing, it powers your composable CDP — helping you finally unify that elusive customer view. For procurement, it lets you understand your suppliers better — and track who you are really doing business with. For compliance, it strengthens your response to CCPA, GDPR, KYC, and AML mandates — by starting with clean, resolved data.

Zingg is open source. And it is shaped by people who did not just analyze this problem — we could not stop thinking about it. You do not have to go it alone. You can build on what we have built.

Let Us Solve This, Together

Identity resolution is not a feature. It is a foundation. One that makes everything else — from analytics to personalization to governance — actually work.

If you have been looking for a way to solve it, we would love for you to explore Zingg on GitHub. Try it. Ask questions. Contribute. Or just reach out.

We will keep doing what we have always done: quietly building, learning, and sharing what we know — with everyone who needs it.

Because that is what open source is really about.

‍