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Syntasa

Customer story · National Security

Billions of records, analyzed inside the mission's own cloud.

A U.S. intelligence agency needed a big data tool with an Authority to Operate in its Commercial Cloud Enterprise. Syntasa now runs there in production, at terabytes a day, with analysts building their own analyses and dashboards.

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Results

Mission scale, in production.

  • Records in production15B+records in one production workload, at 1.5 TB a day
  • Daily volume2 TBa day in another workload, across 1B+ records
  • Benchmark15%better performance than a proprietary Spark platform
  • AccreditationATOAuthority to Operate in the agency's C2E environment

Context

Vast datasets, and a mission that can't wait.

The agency employs thousands of people, including civilians, military members and contractors, and its analysts work with vast datasets inside the agency's Commercial Cloud Enterprise (C2E) environment.

It needed a big data tool that could process that data at scale, and let analysts create complex analyses, visualize them and improve their datasets, without waiting on engineering for every change.

The constraint

Accredited, at scale, inside C2E.

  1. An Authority to OperateAny tool had to hold an ATO to run in the agency's environment.
  2. Inside the agency's cloudProcessing had to happen in C2E, on the agency's own cloud services, not in an outside platform.
  3. Massive, mixed workloadsBillions of records in batch and streaming, across data engineering, data science and analytics.
  4. Costs hard to seeCloud processing costs needed to be visible and controlled, job by job.

Deployment

One platform, from raw data to the dashboard.

Syntasa was evaluated side by side against a proprietary Spark platform for data engineering, data science and analytics workloads, then deployed with an ATO in the agency's C2E environment.

  1. Step 1: Run on native cloud services

    The platform uses the cloud services already in C2E, so it scales with each workload.

  2. Step 2: Build without code

    Analysts build their own workflows and complex analyses in the interface, without writing code.

  3. Step 3: Validate mission use cases

    Natural language processing, geospatial analysis (mobility, trip and proximity with H3, AIS) and image processing such as object detection.

  4. Step 4: Automate and govern

    New data is brought in and every dataset and dashboard refreshed automatically, with per-job cost reporting and usage guardrails.

Outcome

What changed for the mission.

  • Cost transparency

    Granular insight into cloud processing costs for each job, reported by user or group, with guardrails and alerts when usage passes a threshold. The agency significantly lowered the cost of its workloads.

  • Analysts self-serve

    Analysts now build their own workflows without writing any code.

  • Faster decisions

    Dashboards give a visual understanding of the data, helping people make faster and better-informed decisions.

  • Always current

    New data flows in automatically, and every dataset and dashboard refreshes without manual work.

“We are proud to have partnered with this agency to bring our advanced data and AI capabilities to their mission. Our platform's ability to handle complex, large-scale data challenges and provide actionable insights in a user-friendly way demonstrates our commitment to empowering our customers to achieve their missions.”

Jay Marwaha, Founder and CEO, Syntasa

FAQ

Questions teams ask first.

Resources

Read more about Syntasa in C2E.

Case study · PDF · 4 pages

Read the full U.S. intelligence agency story.

The full story, with the architecture, how it works, a timeline and the detailed results.

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