Sick of Your BigQuery Bill? ParaQuery Is the Cure

In the modern data-driven world, businesses generate and process vast volumes of information daily—fueling everything from product recommendations to financial models. Yet, working with this data at scale comes with a painful tradeoff: speed vs. cost. Organizations either pay a premium to cloud data platforms like BigQuery and Databricks or suffer through sluggish query performance and delayed analytics.

Win Wang, founder of ParaQuery, has experienced these frustrations firsthand—from enduring 18-hour physics simulations to waiting endlessly for 20-minute analytical queries in corporate environments. This inefficiency is more than a nuisance—it’s a productivity bottleneck and a financial liability. Whether it’s a ballooning BigQuery bill or the stress of optimizing Spark clusters, current data infrastructure demands significant engineering effort or budgetary compromise.

ParaQuery was born to eliminate this compromise. Its core proposition is simple: run your big data queries faster and cheaper using GPU acceleration, without needing to migrate your data or rework your stack.

How Does ParaQuery Work?

At its core, ParaQuery is a fully-managed, GPU-accelerated big data platform that combines the best of Apache Spark and SQL-based data processing with breakthrough improvements in performance and cost-efficiency. The system is architected to run on parallel, throughput-optimized GPUs, making it ideal for compute-intensive workloads.

Instead of relying on legacy CPU-based infrastructure, ParaQuery embraces modern cloud GPUs, delivering:

• 2x speed
• ½ the cost
• Up to 5x efficiency gains

But power is only one part of the equation. ParaQuery abstracts away the infrastructure layer entirely, enabling users to run serverless data workloads without worrying about provisioning, scaling, or maintenance.

The system supports a simple 4-step process:

1. Define inputs
2. Specify logic
3. Declare the output
4. Hit Run

There’s no need for complex pipeline orchestration or vendor-specific optimizations. It works out-of-the-box with any Spark connector, making it cloud-agnostic and free of vendor lock-in.

Why Are GPUs a Game-Changer for Data Processing?

For years, GPUs were relegated to graphics and gaming, but over the past decade, they’ve transformed industries ranging from AI to scientific research. Their architecture, which supports massive parallelism, makes them uniquely suited to data analytics tasks that can be executed in parallel across vast datasets.

ParaQuery capitalizes on this latent power. Whereas traditional platforms rely on CPU-heavy clusters that process queries in a serial or semi-parallel fashion, ParaQuery’s GPU-based engine unlocks ultra-fast processing with dramatically lower energy and compute costs.

It’s the same kind of transformation we’ve seen in machine learning workloads—only now applied to enterprise analytics, reporting, ETL, and more.

What Makes ParaQuery Different from Databricks and BigQuery?

Databricks and BigQuery are undeniably powerful, but they come with limitations:

• Cost: Bills can skyrocket as data volumes increase.
• Latency: Long-running queries can delay product development cycles and frustrate teams.
• Complexity: Tuning and managing infrastructure is often a full-time job.
• Vendor Lock-In: Both platforms tie you to their specific ecosystem and cloud provider.

ParaQuery flips the model by offering:

• Fully Spark-compatible environment: If your tool works with Spark, it works with ParaQuery.
• No data migration: Run directly on your existing infrastructure—your data stays put.
• Cloud-agnostic deployment: Use any major cloud provider or GPU-optimized infrastructure.
• Vendor independence: Freedom to integrate across systems, platforms, and storage layers.

In short, ParaQuery provides all the flexibility of open-source tools, with the performance and ease-of-use of a proprietary platform, minus the baggage.

Who Is Behind ParaQuery?

At the helm of ParaQuery is Win Wang, a former tech lead at Twitter, where he specialized in massively parallel distributed build systems. His career has revolved around solving complex efficiency problems—from GPU optimization in crypto to scaling big data systems at one of the largest social networks in the world.

Win’s obsession with performance isn't theoretical. He's waited for hours—sometimes days—for simulations and queries to complete. His mission with ParaQuery is deeply personal: build the fastest, most cost-effective data infrastructure possible, so engineers can focus on innovation, not waiting.

This founder-led ethos translates directly into ParaQuery’s product decisions. Everything is built for efficiency, effectiveness, and exceptional user experience.

How Easy Is It to Integrate ParaQuery?

Ease of use is a cornerstone of ParaQuery’s value proposition. Unlike traditional platforms that require specialized teams for configuration and optimization, ParaQuery is built for plug-and-play simplicity.

Whether you’re running a Spark pipeline, managing SQL queries, or using BI tools, the process is seamless:

• Minimal changes to SQL syntax or logic.
• No need to refactor your Spark jobs.
• Compatible with every major cloud provider.
• Works with any system that has a Spark connector.

ParaQuery is particularly valuable for companies operating multi-cloud or hybrid environments, where vendor lock-in and data migration present serious roadblocks.

Who Should Use ParaQuery?

ParaQuery is ideal for:

• Data Engineers tired of tuning clusters and watching costs balloon.
• Analytics Teams frustrated by long query times.
• CTOs and Infra Leads looking to reduce cloud expenses without sacrificing performance.
• Startups and Enterprises that want scalable, serverless solutions for growing data workloads.

Whether you’re running ETL jobs, real-time analytics, large-scale joins, machine learning prep, or ad-hoc queries, ParaQuery’s GPU-powered backend provides a dramatic improvement in throughput and responsiveness.

What’s Next for ParaQuery?

Currently active and operating out of New York, ParaQuery is part of the Spring 2025 batch, backed by YC and supported by primary partner Diana Hu. Though still small in team size (just 1 founder), the ambition is enormous.

Win Wang is not just building a faster Spark engine—he’s spearheading a broader movement toward hardware-aware software platforms that take full advantage of modern compute architectures.

In the near future, expect ParaQuery to roll out:

• Advanced query optimizations using AI/ML techniques
• Expanded integrations with cloud-native storage
• UI and dashboard tools for query monitoring and cost tracking
• Self-service deployment options for enterprises

ParaQuery’s roadmap isn’t just about technical performance—it’s about reimagining how companies handle data at scale.

Why Does ParaQuery Matter in the Big Data Ecosystem?

As data volumes explode and real-time insights become business-critical, the pressure on infrastructure continues to mount. Enterprises are looking for smarter, leaner, and faster ways to process information, without multiplying their cloud spend.

ParaQuery represents a rare trifecta: performance, affordability, and flexibility. It doesn’t require a PhD to operate, doesn’t lock users into one vendor, and doesn’t cost a fortune to scale. By building on modern GPU infrastructure and fully embracing open standards like Spark, it offers a forward-looking foundation for the next decade of data engineering.

In Win Wang’s own words, “ParaQuery is productizing an arbitrage of hardware efficiency—and I’m not waiting any longer to do so.”

With ParaQuery, the future of big data is not just faster—it’s smarter, simpler, and more cost-effective. As organizations continue to demand more from their data infrastructure, ParaQuery is poised to deliver where others fall short: real performance at real scale, without real pain.

For those tired of watching queries crawl or cloud bills soar, ParaQuery offers a compelling answer: Don’t wait. Run faster—at half the cost.