Sold by: Code Creator
Deployed on AWS
This is a repackaged open source software product wherein additional charges apply for Code Creator integration of Apache Spark, JupyterLab and Spark Connect, automated service startup, first boot access configuration, private internal Spark services, browser based analytics access, and AWS Marketplace AMI engineering.
Apache Spark Analytics Workbench with JupyterLab and Spark Connect is a ready to use data analytics server on Ubuntu that gives users a fast way to launch Apache Spark, work in JupyterLab, run distributed data jobs, and explore analytics through a browser using the instance public IP address
Overview
Code Creator charges apply for the integrated Apache Spark analytics environment, JupyterLab and Spark Connect configuration, automated service startup, first boot customer guidance, private internal Spark service configuration, public IP based access workflow, and AWS Marketplace ready AMI engineering. Apache Spark Analytics Workbench with JupyterLab and Spark Connect is a preconfigured analytics environment built for teams and developers who want a faster path to working with Apache Spark on AWS. It provides a browser based JupyterLab workspace, Apache Spark master and worker services, Spark Connect for modern client server workflows, and a Spark History Server for reviewing completed jobs. The product is designed for simple launch and access by public IP address so customers can begin working without requiring a domain name.
This AMI is built on Ubuntu and is designed to reduce setup time for data engineering, notebook based experimentation, distributed job execution, ETL development, analytics learning, and Spark testing. Internal Spark interfaces are kept private for safer operation while the main user experience is delivered through JupyterLab. The environment is configured to auto start on boot and includes first boot guidance to help users quickly retrieve login details and begin using the platform.
Apache Spark Analytics Workbench with JupyterLab and Spark Connect is well suited for developers, analysts, data engineers, students, and organizations that want a practical Spark workspace on AWS without building the full environment by hand.
Highlights
- Ready to use Spark analytics workspace Launch Apache Spark with JupyterLab and Spark Connect on Ubuntu 24.04 and start working from your browser using the instance public IP address with no domain name required.
- Faster setup for data and notebook workflows Run distributed Spark jobs, explore data in Jupyter notebooks, and test analytics workloads without spending time building and wiring the environment by hand.
- Built for practical AWS use Includes auto start on boot, first boot access guidance, private internal Spark services for safer operation, and a cleaner user friendly experience for developers analysts and data teams
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
r7i.xlarge Recommended | $0.035 |
m7i.2xlarge | $0.035 |
t3a.2xlarge | $0.035 |
r7i.2xlarge | $0.035 |
m7i.large | $0.035 |
t3a.xlarge | $0.035 |
r7i.large | $0.035 |
m7i.xlarge | $0.035 |
c7i.2xlarge | $0.035 |
Dimensions summary
You pay by the hour based on the EC2 instance type you run. This software comes pre-built for instant deployment on AWS. Each dimension maps to a different instance size and family, so you choose the compute profile that fits your workload. The t3a options give general-purpose capacity, while m7i options offer balanced compute and memory. The r7i options add more memory for data-heavy tasks, and the c7i.2xlarge targets compute-focused work. Larger sizes (xlarge, 2xlarge) cost more per hour than smaller ones (large). You run only what you need, and billing scales with your usage.
Top-of-mind questions for buyers
Each name maps to a specific AWS EC2 instance you rent hourly. The family letter signals the profile: t3a and m7i balance compute and memory, r7i adds memory for data-heavy jobs, c7i favors compute. The size (large, xlarge, 2xlarge) sets how much CPU and memory you get.
Hourly software charges apply only while the instance runs. Fully stopped instances stop accruing software charges. Stopped instances may still incur underlying AWS storage fees, but the software meters running hours only. You pay for what you actually use.
Each dimension bills independently per hour. You can launch a different instance type when your needs change. There is no commitment tying you to one size. Larger sizes within a family carry more CPU and memory, so you pick the profile that fits each workload.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
n/a
Additional details
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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