Heavy Data

Tensor9 for Heavy Data Scenarios

Many enterprises handle massive datasets that are too large or sensitive to transfer to the cloud, such as high-resolution video surveillance, genomics data, or financial transaction logs. These "heavy data" workloads present challenges for vendors whose traditional SaaS solutions rely on cloud-based data processing, which can be costly, slow, or impossible due to security policies.

A prime example of this is JPMorgan Chase, which manages 450+ petabytes of data—enough to dwarf even the largest AI training sets. For comparison, GPT-4 was trained on roughly 1 petabyte of data. This highlights the scale of enterprise data and the impracticality of moving or duplicating such vast datasets for cloud-based processing.

Tensor9's Any-Prem platform addresses these challenges by enabling vendors to deliver their software directly into the customer's environment, allowing local data processing without requiring data transfers.

Key Challenges with Heavy Data

1. Data Transfer Costs and Performance
   Large datasets require significant time and expense to transfer to a vendor’s cloud, especially when dealing with petabytes of data. Egress fees and network latency can make such transfers impractical.

2. Sensitive Data Requirements
   Certain datasets, such as government surveillance feeds or patient medical records, may be legally or contractually required to stay within a secure, local infrastructure.

3. Compute and Storage Constraints
   Vendors often face resource bottlenecks when processing massive datasets due to cloud-based compute and storage limitations, leading to throttling or degraded performance.

How Tensor9 Helps

1. Local Data Processing
   Tensor9 enables vendors to deliver their software directly into the customer’s infrastructure (e.g., private cloud, on-prem, air-gapped networks). By running computations locally, Tensor9 eliminates the need to transfer large datasets to an external cloud, ensuring low latency and fast processing times.

2. Data Residency and Security Compliance
   With Tensor9, sensitive datasets remain within the customer’s environment, satisfying data residency regulations and reducing the risk of data breaches. This is particularly important for industries with stringent compliance requirements, such as finance, defense, and healthcare.

3. Seamless Integration with Customer Resources
   Tensor9 integrates with the customer’s existing compute and storage resources, scaling AI/ML workloads and other data-intensive operations without relying on external cloud services.

Example Scenario: JPMorgan Chase

JPMorgan Chase, one of the largest financial institutions in the world, manages over 450 petabytes of proprietary data across its global infrastructure. With 55 million active digital customers and relationships with over 50% of U.S. households, the bank cannot afford to transfer, duplicate, or store this data externally due to regulatory, security, and operational constraints.

By processing data locally within secure environments, organizations like JPMorgan Chase avoid the "public data wall" and customize AI/ML models using internal proprietary data without incurring costly data transfer fees or security risks. Tensor9 empowers vendors to bring their code to the data rather than bringing the data to the cloud, ensuring high performance and compliance.

Benefits

Example Scenarios

1. Video Surveillance Analytics
   A security software vendor uses Tensor9 to deliver real-time video analysis within a government agency’s secure data center, ensuring sensitive surveillance feeds never leave the premises.

2. Genomics Research
   A healthcare AI vendor uses Tensor9 to run genomic analyses directly within a hospital's research infrastructure, avoiding the need to transfer massive sequencing data to the cloud.

3. Financial Transactions Monitoring
   A fintech company deploys Tensor9 to perform fraud detection on billions of daily financial transactions within a bank’s private cloud, reducing compliance risks and network latency.

Summary

Tensor9 empowers vendors to overcome the limitations of cloud-based data processing for heavy data workloads by enabling local data processing within customer environments. By eliminating data transfers, reducing costs, and meeting regulatory requirements, Tensor9 makes it easier for vendors to serve customers with large and sensitive datasets.