Sold by: IntuzÂ
Deployed on AWS
Free Trial
AWS Free Tier
This product has charges associated with it for configuration, and maintenance of the Great Expectations and JupyterLab environment. A pre-configured AWS AMI that simplifies data validation using Great Expectations and JupyterLab - ideal for data engineers and analysts.
Overview
This is a repackaged open source software product wherein additional charges apply for support, configuration, and maintenance.
The Great Expectations AMI with JupyterLab by Intuz provides a ready-to-use data validation environment on AWS. It comes pre-installed with Great Expectations, JupyterLab, and all necessary Python dependencies, enabling you to launch and begin building, testing, and documenting Expectation Suites in minutes without manual setup.
Whether you're validating ETL workflows, profiling data for machine learning, or ensuring data integrity and compliance, this AMI delivers a secure, browser-accessible workspace. Designed to accelerate your data quality practices, it reduces setup overhead and integrates seamlessly into cloud-native pipelines.
Highlights
- Pre-configured Great Expectations with JupyterLab for instant data validation workflows
- Secure, browser-accessible environment ideal for building and documenting Expectation Suites
- No setup required-launch in minutes and focus on profiling, testing, and pipeline integrity
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
Try this product free for 5 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
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. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
t3a.medium Recommended | $0.09 |
t2.micro AWS Free Tier | $0.09 |
m6a.24xlarge | $0.09 |
t3a.small | $0.09 |
m6a.12xlarge | $0.09 |
m5a.24xlarge | $0.09 |
m5a.12xlarge | $0.09 |
m6a.xlarge | $0.09 |
m6a.32xlarge | $0.09 |
t3a.micro | $0.09 |
Vendor refund policy
Intuz will not refund money in any case.However, you can cancel your subscription any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Pre-installed and configured Great Expectations for automated data validation and profiling
Includes Jupyter Notebook for interactive development and testing of Expectation Suites
Ready-to-use examples and templates for validating CSV, Pandas, and SQL-based data sources
Python 3.x environment with essential data libraries such as Pandas, SQLAlchemy, and PyYAML
Secured instance access with web-based Jupyter interface exposed on port 8888
Designed for use with AWS EC2 instances; compatible with t3a.medium and higher
Additional details
Usage instructions
After launching the Intuz Great Expectations JupyterLab AMI, wait 5-10 minutes for the application to fully initialize. To access JupyterLab, open a browser and navigate to http://<public-ip>:8080, then log in using the EC2 instance ID as the password. For SSH access, connect using ssh -i your-key.pem ubuntu@<public-ip>. Once logged in, you can immediately start using Great Expectations within JupyterLab for data validation, profiling, and testing.
Support
Vendor support
We provide best effort technical support for this product. We will do our best to respond to your questions within the next 24 hours in business days. For any technical support or query, you can drop an email here: cloudsupport@intuz.com or fill up this form:
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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