Great Expectations: Automated Data Profiling on AWS

About Great Expectations Stack

Bad data quietly breaks more pipelines and dashboards than bad code does, and the problem usually isn’t caught until someone notices a report doesn’t add up weeks later. Intuz’s Great Expectations AMI addresses that directly by putting a fully configured data validation and profiling environment on Amazon EC2, with Great Expectations and Jupyter Notebook pre-installed, so testing and documenting data quality is possible from day one rather than something bolted on after a pipeline is already in production.

The platform’s expectation suites let data engineers define what “correct” data actually looks like, value ranges, null constraints, schema rules, and then validate incoming data against those rules automatically rather than relying on someone eyeballing a spreadsheet. Automated documentation generation turns those expectation suites into human-readable records of your data quality standards, which matters when a new team member needs to understand what’s actually being validated and why.

Because Jupyter Notebook access runs through the browser, building and testing validation rules happens interactively rather than through a slower edit-and-rerun cycle on a local machine.

The AMI is tuned to run efficiently across a range of EC2 instance types, so this scales from a single analyst validating one dataset to a data engineering team running checks across an entire pipeline without changing tools.

If your data quality needs are specifically at scale, validating massive datasets rather than moderate-sized ones, look at the Deequ with Apache Spark AMI instead, it’s built for constraint-based validation on distributed data using Spark and fits once your datasets have outgrown a single-instance validation tool.

Key Features of Great Expectations Stack

• One-Click Deployment: Instantly launch a fully functional data validation environment with Great Expectations and Jupyter Notebook on AWS EC2
• Pre-Configured for Data Quality: Includes Great Expectations, Jupyter, and essential Python libraries—ready to use without manual setup
• Web-Based Notebook Access: Create, run, and visualize your data tests and documentation directly from a browser-based Jupyter interface
• Optimized for AWS: Designed for high performance on a wide range of EC2 instance types to fit small-scale and enterprise data needs
• Interactive Data Profiling: Generate detailed data summaries and expectation suites to catch anomalies early in your pipeline
• Automated Data Documentation: Automatically create human-readable documentation from your expectation suites
• Customizable Validation Workflows: Tailor validation rules and batch processing to match your data sources and business logic
• Secure and Self-Hosted: Maintain full control over data privacy and access by running in your isolated AWS EC2 environment

Included With Application

Fully integrated data validation and profiling environment with browser-based Jupyter Notebook access, pre-installed Great Expectations, and essential Python libraries—ideal for building, testing, and documenting reliable data pipelines.