Marker.io integration Marker.io integration END

Cloud-ready or on-prem?

SparkBeyond enables users to use cloud-native, multi-cloud, and on-premise platforms that seamlessly work with existing data storage, enterprise source systems, tools, and underlying infrastructure.

SparkBeyond enables users to use cloud-native, multi-cloud, and on-premise platforms that seamlessly work with existing data storage, enterprise source systems, tools, and underlying infrastructure.

SparkBeyond's product suite is designed to seamlessly connect to and work in harmony with major cloud infrastructure and service providers without compromising stringent data governance, compliance, and security requirements. ​


Microsoft Azure™ Services
  • Use the C3 AI Suite to leverage the full power of Azure services for the rapid design, development, deployment, and operation of next-generation AI applications​
  • Drive predictive insights, add business value, and solve previously unsolvable problems
AWS™ Services
  • ​Leverage the power of Amazon Web Services as part of a single-, multi-, or hybrid-cloud environment for developing, deploying, and operating next-generation AI applications​
  • Scale across a multiplicity of data sources to generate and operationalize predictive insights
Google Cloud™ Services

Seamlessly deliver the C3 AI Suite on Google Cloud Platform, leveraging its cutting-edge infrastructure and AI services as part of a multi or hybrid-cloud environment​.

Deployment Requirements for SparkBeyond Discovery Platform

Operating Systems

The following Linux distributions are supported:

  • CentOS 7.x
  • RHEL 7.x

Note: It is highly recommended to always run the latest version

Deployment Options
  • On-Premise
  • Client’s Public Cloud Instance (Azure, AWS and GCP)
  • Client’s Private Cloud Instance
  • SparkBeyond-Managed Instance (Azure, AWS and GCP)
Delivery Methods
  • Amazon Machine Image (AMI)
  • Azure VM Image
  • Google GCP VM Image
  • VMware Image
Deployment time estimates
  • 1-2 hours if deployed using a disk image (Azure, AWS, GCP and VMware)
  • 1-2 business days if deployed on-premise*

Note: Deployment on hardened systems may take more than two business days

Hardware Requirements for SparkBeyond Discovery

Example: 4 Users

On-Prem configuration for up to four concurrent jobs per machine
  • 160 GB RAM
  • 2 TB attached storage (SSD drive is highly recommended; storage size must be at least 10 times greater than the total estimated data size)
  • 20 vCPUs (10 cores with hyper-threading)
Cloud configuration for up to four concurrent jobs per machine
  • AWS:  minimum instance type is m4.10xlarge
  • Azure:  minimum instance type is E20s_v3
  • Google Cloud:  minimum instance type is n2-standard-48
  • For cloud deployment, the minimum instance specs are:
  • 160 GB RAM
  • 2 TB attached storage
  • 32 vCPUs (16 cores with hyper-threading)

Note: When it is not possible to allocate a single disk for the whole root filesystem (e.g Azure), the installation can be divided between two disks:

  1. A root disk (with a minimum of 32GB) mounted under /. In addition, it is highly recommended to allocate a sufficient amount of space for the /tmp directory (around 200GB),  so a root disk with 250GB in total would be ideal.
  2. An installation disk, mounted under /opt/sparkbeyond with the recommended size (500GB minimum).

Features

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It was easier in this project since we used this outpout

Business Insights

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Predictive Models

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Micro-Segments

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Features For
External Models

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Business Automation
Rules

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

Root-Cause
Analysis

Apply key dataset transformations through no/low-code workflows to clean, prep, and scope your datasets as needed for analysis

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