How Bay Area Startups Are Fighting Data Catalogue Issue

It's 2 AM in a SoMa co-working space, and Sarah Chen is staring at her laptop screen with a mixture of frustration and disbelief. Her AI-powered customer service startup has just landed its biggest client—a Fortune 500 retailer—but there's a problem. The client's data lives in three different clouds, uses five different database systems, and is governed by compliance requirements that span GDPR, CCPA, and industry-specific regulations she's never heard of.

Chen says, recalling that sleepless night six months ago:

We built an amazing product. Our AI models were state-of-the-art. But none of that mattered because we couldn't actually access and unify our client's data. We had two weeks to solve a problem that normally takes companies six months.

She's not alone. Across the Bay Area, from Palo Alto to Oakland, a new generation of startup founders is discovering a harsh truth: in the age of AI, data management isn't just a backend concern—it's an existential threat.

The Data Problem Nobody Talks About

Walk into any Sand Hill Road venture capital office, and you'll hear the same pitch over and over: "We're using AI to transform [industry]." Healthcare. Finance. Logistics. Education. Every sector is being reinvented by artificial intelligence, or so the pitch decks claim.

But there's a dirty secret behind those glossy presentations. Most AI startups spend less than 20% of their time on AI and machine learning. The rest? Data wrangling, data cleaning, data integration—the unglamorous work of making data accessible and usable.

Marcus Rodriguez, a former Google engineer who now advises early-stage startups, laughs:

Everyone wants to be the next OpenAI or Anthropic, but they end up spending all their time being a data janitor. I've seen companies with brilliant ML teams completely stalled because they can't figure out how to connect to a client's Snowflake instance and their on-premise Oracle database at the same time.

The problem has gotten exponentially worse in recent years. A decade ago, a startup might integrate with a handful of data sources—mostly APIs and relational databases. Today's startups face a bewildering landscape:

• Multi-cloud chaos: Clients using AWS, Google Cloud, and Azure simultaneously
• Hybrid environments: On-premise systems that can't be migrated due to compliance or cost
• Data silos: Marketing data in one system, sales in another, product analytics in a third
• Format fragmentation: Parquet files, JSON documents, relational tables, vector embeddings, streaming data
• Governance nightmares: Different access controls, compliance requirements, and data residency rules for each system

Rodriguez says:

It's like every company has its own unique snowflake of data chaos. And if you're a startup trying to sell into the enterprise, you have to support all of it.

The Breaking Point

For Enception.ai, a San Francisco-based startup building AI-powered data analysis tools, the breaking point came during their third enterprise sales cycle. Founder Quanlai Li, who had spent years working on Uber's massive data platform, thought he'd seen it all.

He was wrong.

Li recalls, sitting in Enception's office near the Ferry Building:

We were talking to this major financial services company. They had data in twelve different systems. Twelve! And they wanted our AI to analyze all of it together—financial transactions from one system, customer data from another, market data from a third. Each system had different access controls, different security requirements, different schemas.

The traditional approach would have been to build custom integrations for each system—weeks or months of engineering work for every new client. But Enception was a startup with five engineers and runway to match. They needed a different approach.

Li continues:

I remembered the problems we had at Uber. When you're operating at that scale, you can't just copy data around. You need a unified metadata layer that lets you treat all your data sources as one logical system, even when they're physically distributed across the world.

That realization led Li to Apache Gravitino, an open-source project that had recently graduated to top-level status at the Apache Software Foundation. Gravitino promised something that sounded almost too good to be true: a way to unify metadata across disparate data systems without actually moving the data.

The Metadata Revolution

To understand why metadata matters, consider how you use Google. When you search for something, Google doesn't go out and fetch every webpage in real-time. Instead, it searches its index—metadata about what's on the web. The actual webpages stay where they are; Google just knows how to find them.

Gravitino applies the same concept to enterprise data. Instead of forcing companies to migrate all their data to one place—an expensive, risky, and often impossible task—it creates a unified metadata layer that knows what data exists, where it lives, what it means, and how to access it.

Jerry Shao, co-founder of Datastrato, the Bay Area company behind Gravitino's initial development, explains. Shao, a longtime Apache Spark committer, had seen firsthand how metadata fragmentation created bottlenecks for data teams at companies like Alibaba and Hortonworks:

Think of it as a 'catalog of catalogs.' Every data system has its own catalog—Hive Metastore for data lakes, proprietary catalogs for cloud warehouses, schema registries for streaming data. Gravitino federates all of these under one API, so your applications can discover and access data across your entire infrastructure through a single interface.

For startups like Enception, this architectural approach solved a critical problem: they could support complex enterprise environments without building and maintaining dozens of custom integrations.

Li says:

We integrated with Gravitino in about two weeks. Suddenly, instead of supporting twelve different systems, we supported one API that could talk to all of them. Our engineering team could focus on building our AI models instead of writing data connectors.

The Bay Area Open Source Advantage

The story of how Bay Area startups are solving their data problems is, in many ways, a story about open source. While enterprise giants like Databricks and Snowflake offer proprietary catalog solutions, a new wave of founders is betting on open standards and community-driven development.

Datastrato, headquartered in the East Bay, is at the center of this movement. Founded by veterans of the Hadoop and Spark ecosystems—Junping Du (ex-Cloudera/Hortonworks) and Jerry Shao (Apache Spark committer)—the company built Gravitino from the ground up as an Apache project.

Du explains from Datastrato's office, where whiteboards are covered with architecture diagrams:

We could have built a proprietary catalog and tried to compete with Unity Catalog or AWS Glue. But that would just create another silo. We wanted to build the open standard for metadata management—something that would benefit the entire ecosystem, not just our customers.

That philosophy resonates in the Bay Area, where open source has been integral to the tech ecosystem since the early days of Linux and Apache. The Gravitino GitHub repository has attracted contributors from Uber, Apple, Intel, Pinterest, and dozens of other companies—many of them Bay Area based.

Rodriguez, who has watched the project's growth, says:

There's something about the culture here that values open collaboration over proprietary lock-in. Especially among startups, where you need to move fast and can't afford to bet on the wrong vendor.

Real-World Impact: Three Startup Stories

Story One: The Healthcare AI That Almost Wasn't

MedInsight, a Redwood City startup building AI for medical diagnostics, nearly collapsed under the weight of their data integration challenges. They needed to analyze patient data from electronic health records, imaging data from PACS systems, lab results from multiple providers, and insurance claims data—all while maintaining HIPAA compliance.

CTO James Park recalls:

Every hospital we worked with had a different setup. Different EHR vendors, different imaging systems, different data warehouses. We were looking at eighteen months of integration work before we could even start training our models.

After adopting Gravitino, MedInsight reduced that timeline to six weeks. The unified metadata layer allowed them to apply consistent governance policies across all data sources while maintaining the security and compliance requirements of each individual system.

Park says:

The breakthrough was realizing we didn't need to move the data. The patient records stay in the hospital's system, under their control, governed by their security policies. Our AI accesses the data through Gravitino's API, which handles all the permissions and access control automatically.

Today, MedInsight is processing data from forty hospitals across three states. Park estimates that without Gravitino, they would have needed a team of fifteen data engineers just to maintain integrations. Instead, they have three engineers supporting the entire data infrastructure.

Story Two: The Climate Tech Dilemma

ClimateOS, a Berkeley-based startup analyzing satellite imagery and climate data, faced a different challenge: their data was scattered across the globe—literally.

Founder Dr. Maya Patel explains:

We're pulling in satellite data from NASA, NOAA, ESA, and private providers. Some of it is in AWS, some in Google Cloud, some in on-premise archives in Europe because of data sovereignty requirements. The data sets are massive—we're talking petabytes—so moving it all to one place was completely impractical.

ClimateOS needed to process this distributed data together—correlating satellite imagery with ground sensors, weather models, and historical climate data—to generate accurate predictions. Traditional approaches would have required either massive data transfer costs or maintaining separate analysis pipelines for each data source.

Gravitino's geo-distributed architecture became the solution. The startup could treat all their data sources as one logical catalog, running distributed queries that executed close to where the data lived, minimizing transfer costs and latency.

Patel says:

We saved literally millions in data transfer costs. More importantly, we cut our analysis time from weeks to hours. When you're tracking rapid climate changes, that speed matters.

Story Three: The Financial Services Breakthrough

SecureFinance, a San Mateo fintech startup, needed to solve what seemed like an impossible problem: process sensitive financial data from multiple institutions while maintaining strict compliance and security controls.

CEO David Wong says:

Banks are incredibly protective of their data, and rightfully so. They're not going to copy their transaction data into our cloud environment, no matter how secure we say it is. We needed a way to analyze their data in place, under their control, while still being able to correlate it with data from other sources.

Gravitino's federated architecture provided the answer. Each bank's data remained in their own secure environment, governed by their own access controls. SecureFinance's analytics ran as distributed queries coordinated by Gravitino, with each institution's data processed locally and only aggregated results being shared.

Wong notes:

It was the only architecture our compliance team would approve. And it's actually more secure than any centralized approach because the raw data never leaves the originating institution's control.

The Competitive Landscape

Not everyone is taking the open-source route. The data catalog space has become intensely competitive, with well-funded players offering proprietary solutions:

Databricks Unity Catalog dominates among companies heavily invested in the Databricks platform, offering tight integration with their lakehouse architecture.

Snowflake Polaris, recently open-sourced, appeals to Snowflake-centric organizations but still carries the assumption that Snowflake is your primary data warehouse.

AWS Glue, Google BigLake, and Microsoft OneLake each offer cloud-native catalog solutions—excellent if you're all-in on one cloud, problematic if you're not.

For Bay Area startups trying to serve enterprise clients with heterogeneous environments, these platform-specific solutions create a dilemma. Do you limit your market to companies using a specific platform? Or do you build and maintain integrations with all of them?

Sarah Chen, the founder we met at the beginning of this story, says. Her company eventually adopted Gravitino and successfully onboarded the Fortune 500 retailer whose complex data environment had nearly derailed them:

The genius of Gravitino is that it doesn't force that choice. Our clients use whatever data systems make sense for their business—Snowflake for analytics, Databricks for ML, AWS for storage, on-premise systems for legacy applications. Gravitino lets us support all of it through one API. We don't have to pick a side in the platform wars.

The AI Catalyst

The explosion of interest in AI has accelerated the data catalog market dramatically. Large language models and other AI systems are notoriously data-hungry, and they need diverse datasets that often span multiple systems.

Shao observes:

AI is exposing the weaknesses in how companies manage data. Traditional business intelligence could work with a data warehouse. But training a good AI model often requires combining structured transaction data, unstructured documents, customer interaction logs, external market data—stuff that's never all in one place.

Gravitino's roadmap reflects this AI-first reality. The project is building capabilities specifically designed for AI workloads:

Statistics and Metadata AI can Understand: Instead of just tracking table schemas, Gravitino will capture statistics about data distributions, quality metrics, and semantic relationships—the kind of information LLMs need to understand what data is useful for what purposes.

Agentic Workflows: Upcoming versions will support "data agents"—AI systems that can autonomously discover, evaluate, and access data across an organization's entire data estate.

Vector Store Integration: Native support for vector databases and embeddings, treating them as first-class citizens alongside traditional tabular data.

Du explains:

We're building the infrastructure for a future where AI agents, not just humans, need to navigate and understand data. That requires metadata to be not just machine-readable, but machine-understandable.

The Economics of Open Source

For cash-strapped startups, the economics of open source are compelling. While proprietary catalog solutions can cost tens of thousands of dollars per year in licensing fees, Gravitino is free to use.

But free doesn't mean without cost. Companies still need to deploy, configure, and maintain the system. That's where Datastrato's business model comes in—offering commercial support, managed services, and enterprise features built on top of the open-source foundation.

Du says:

We follow the model that's worked for companies like Databricks with Spark or Confluent with Kafka. The core technology is open source and free. Companies that want enterprise support, managed hosting, or advanced features can pay for those services.

For startups, this model offers flexibility. Early-stage companies can self-host Gravitino using the open-source version, keeping costs low. As they grow and need enterprise features or want to offload operational burden, they can engage with Datastrato commercially.

Li says:

We used the open-source version for our first year. Once we closed our Series A and started onboarding larger clients, we switched to Datastrato's managed service. It made sense at that point to pay for support rather than have our engineers managing infrastructure.

Challenges and Growing Pains

The Bay Area startup community's embrace of Gravitino hasn't been without challenges. As with any relatively young project—Gravitino only graduated to Apache top-level status in May 2025—there are rough edges.

Park from MedInsight admits:

The documentation was sparse early on. We had to dig into source code sometimes to figure out how things worked. But that's getting better, and the community has been responsive when we've had questions.

Performance optimization is another area of active development. While Gravitino handles metadata federation elegantly, query planning and optimization across federated sources is complex.

Patel from ClimateOS notes:

We've hit some performance issues when running queries that span many data sources. The team is working on it, and we've seen steady improvements in each release. But it's something to be aware of if you're doing really complex federated queries.

There's also a learning curve. Gravitino introduces concepts—metalakes, catalog federation, unified namespace—that are new to many engineers.

Wong from SecureFinance says:

It took our team a few weeks to really grok the mental model. But once you understand it, it's actually simpler than managing dozens of individual catalog integrations.

The Broader Movement

Gravitino is part of a broader movement in the Bay Area startup ecosystem toward open, interoperable data infrastructure. Other projects in this space include:

Apache Iceberg, an open table format that's become the de facto standard for data lakes, is heavily used by Bay Area companies and has strong local community support.

DuckDB, the "SQLite for analytics," emerged from research but has found enthusiastic adoption among startups that need fast, embedded analytics.

LanceDB and other vector database projects are building open alternatives to proprietary embedding stores for AI applications.

Rodriguez says:

There's a recognition that data infrastructure is too important to be controlled by any single vendor. The Bay Area has always been the birthplace of open infrastructure—from Apache and Linux to Kubernetes and beyond. This latest wave around data catalogs and AI infrastructure is continuing that tradition.

Looking Forward: The Next Chapter

As AI continues to reshape industries, the data management challenges facing startups will only intensify. But the solutions emerging from the Bay Area—driven by open-source collaboration and battle-tested in real-world startup environments—offer hope.

Sarah Chen, whose late-night data crisis we opened with, now advises other founders on data architecture. Her message is simple: don't underestimate the data challenge.

She says:

Everyone focuses on the AI model or the product features. But if you can't access and unify data at scale, none of that matters. The good news is that tools like Gravitino mean you don't have to solve this problem from scratch. The open-source community has done a lot of the heavy lifting.

For Datastrato, the journey is just beginning. The company recently raised a Series A round led by Bay Area VCs who believe the metadata layer will be as critical to the AI era as databases were to the internet era.

Du says, gesturing to a whiteboard diagram showing Gravitino at the center of a complex web of data sources, AI agents, and analytics tools:

We're building infrastructure for a fundamentally new kind of data architecture. One where metadata is the organizing principle, where open standards enable interoperability, and where AI agents can navigate data as easily as we navigate the web.

Back at Enception's office, Li is demonstrating their latest feature to a potential client—an AI system that can answer complex questions by automatically discovering and querying data across the client's entire infrastructure.

"What were our top-selling products by region last quarter, and how did weather patterns correlate with sales?" the client asks.

The AI agent, powered by Gravitino's unified metadata layer, springs into action. It discovers sales data in Snowflake, regional information in PostgreSQL, and weather data in S3. It understands the schemas, joins the data, and returns an answer in seconds.

Li says, watching the demo:

This would have been impossible six months ago. We would have needed custom integrations for each data source, manual schema mapping, complicated ETL pipelines. Now it just works.

He pauses, then grins:

That's the power of getting the metadata layer right. Everything else becomes possible.

The Takeaway for Founders

For Bay Area founders wrestling with data challenges, the lessons from these startup stories are clear:

Start with Metadata: Before building custom integrations or complicated ETL pipelines, consider whether a unified metadata layer could solve your problem more elegantly.

Embrace Open Standards: Proprietary solutions may offer slick interfaces, but open standards like Gravitino offer flexibility and avoid vendor lock-in—critical for startups that need to adapt quickly.

Don't Reinvent the Wheel: The Bay Area open-source community has already solved many common data challenges. Leverage those solutions instead of building from scratch.

Plan for AI: Even if you're not building an AI product today, the architecture decisions you make now will impact your ability to adopt AI in the future. Metadata-centric architectures are AI-ready by design.

Community Matters: Choose technologies with strong open-source communities. The Gravitino community's responsiveness and rapid development have been crucial for startups adopting the platform.

As the AI revolution accelerates and data continues to grow exponentially, the startups that thrive will be those that master the data management challenge. In the Bay Area, where open source and entrepreneurship have long been intertwined, solutions like Gravitino are showing the way forward.

It's 2 AM again, but this time Sarah Chen is sleeping soundly. Her data infrastructure is humming along, automatically federating metadata across a dozen different systems, serving AI-powered insights to clients around the world. The data problem that nearly killed her startup is now her competitive advantage.

That's the power of getting the foundation right. That's the promise of the metadata revolution. And that's how Bay Area startups are turning their biggest challenge into their greatest strength.

Learn More:

• Apache Gravitino: github.com/apache/gravitino
• Datastrato: datastrato.com

About This Story: This article is based on interviews with Bay Area startup founders, engineers, and data infrastructure leaders conducted in June 2025. Some names and identifying details have been changed to protect confidential business information.