DataPelago
DataPelago helps enterprises unlock the full value of their structured, semi-structured, and unstructured data by accelerating processing 10× faster & reducing costs by up to 80%.
Case Details
Client: DataPelago
Country: United States
Industry: Data Processing
Tags: Data Processing, AI & Analytics
Duration: Running since 12 months
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Value Generated
DataPelago helps enterprises unlock the full value of their structured, semi-structured, and unstructured data by accelerating processing 10× faster & reducing costs by up to 80%.
For AI, GenAI, & analytics teams, this means training, fine-tuning, & deploying models at record speed without expensive infrastructure or vendor lock-in.
Companies like McAfee, Samsung SDS, ShareChat, & Akad Seguros trust DataPelago to run workloads at scale with superior price-performance, both on web platforms & mobile-ready dashboards.
Service Provided
- High-Speed Processing: 10× faster execution across GPUs, CPUs, & FPGAs.
- Cost Efficiency: Up to 80% cost reduction for AI & analytics.
- AI & GenAI Acceleration: Data prep (extract, filter, tokenize, embed), model training, fine-tuning, & RAG pipelines.
- Seamless Integration: Works with SQL, Python, Airflow, Tableau, Power BI, Spark, and Trino – no app or infra changes required.
- Cross-Platform Responsive Design: Web & mobile responsive for data teams working on-the-go.
Challenges Faced
- Legacy compute systems are too slow & costly for GenAI data.
- Fragmented pipelines for structured, semi-structured, & unstructured data.
- Vendor lock-in restricting flexibility.
- Heavy OLAP cube jobs impossible to complete on open-source Spark.
- Limited ability to scale AI workloads without re-engineering.
Solutions Implemented
- Built Nucleus, a heterogeneous compute engine mapping workloads dynamically across CPUs, GPUs, FPGAs.
- Delivered seamless integration with existing SQL, Spark, Trino, Gluten, Velox pipelines.
- Enabled real-time performance for AI/GenAI model deployment with responsive UI across devices.
- Supported multi-modal workloads (structured + unstructured data).
- Eliminated vendor lock-in by keeping infra & tools intact.
