Sold by: DataPelago Accelerator for Spark
Save costs by up to 80% and accelerate your Apache Spark workloads 10x, depending on workload.
Overview
DataPelago Accelerator for Spark (DPA-S) seamlessly integrates with Apache Spark, enabling high-performance CPU/GPU acceleration for your Data Engineering/ETL, BI, and AI workloads. Benefits: 1. Reduce Costs by up to 80%: Minimize costs by maximizing hardware efficiency. 2. Up to 10x Faster Performance: Process massive datasets at lightning speed. 3. Zero Friction: Works out-of-the-box with your existing applications, data, tools, and security policies. 4. Seamless Integration: A simple plugin to Spark clusters with no other changes.
Highlights
- Seamless Integration: Simple plugin for your Spark clusters. Deploy without any changes to your current applications, security frameworks, or data infrastructure.
- Zero Friction: Deploy at scale with zero IT overhead and zero business disruption. Works out-of-the-box with your existing Spark clusters, jobs, and workflows with plug-and-play efficiency.
- Investment Protection: No vendor lock-in or proprietary formats. Preserve your existing data lakehouse, catalogs, and infrastructure investments while gaining next-generation processing capabilities across any hardware platform.
Details
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
vCPUhour | Contract term for using the DataPelago Accelerator for Spark | $100,000.00 |
Dimensions summary
This listing uses a contract pricing model with one dimension: vCPU-hour. You commit to a contract term and pay based on the virtual CPU hours your Spark workloads consume. Pricing scales directly with usage, so costs align with actual compute processed. There are no separate tiers or instance sizes to choose from. The accelerator runs alongside your existing Spark clusters, and billing flows through AWS Marketplace. Because charges track consumption, you pay more only as you process more, keeping costs tied to real workload activity.
Top-of-mind questions for buyers
A vCPU-hour is one virtual CPU running for one hour inside your Spark clusters. The accelerator meters the compute your workloads actually consume. If a job uses more virtual CPUs or runs longer, it records more vCPU-hours. Counting tracks running compute time, not the number of jobs or clusters.
Charges track the virtual CPU hours your workloads consume. When no Spark jobs run, no compute is processed, so no vCPU-hours accrue. Costs align with actual usage, so you pay based on active processing time rather than idle periods.
No. The accelerator plugs into your existing Spark clusters, jobs, and workflows using standard settings. Your existing notebooks and applications run unchanged, with no application rewrites or data migration. It supports PySpark, Scala, and Spark SQL across multiple Spark versions.
www.datapelago.ai
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Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Support
Vendor support
Please reach out to sales@datapelago.com with any questions or for options on contract or pricing terms. Technical Support: For help setting up your account, connecting to data, or exploring the service please reach out to support@datapelago.com
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