Sold by: IntuzÂ
Deployed on AWS
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Deequ with Apache Spark Pre-configured Stack provides a ready-to-use environment for data quality verification and validation at scale. This AMI bundles Deequ, Apache Spark, Jupyter Notebook, and PostgreSQL for immediate data quality management without complex setup.
Overview
Deequ with Apache Spark Pre-configured Stack offers organizations a comprehensive solution for implementing data quality checks and validation at scale. Built on AWS infrastructure, this AMI comes with Deequ (a powerful data quality library), Apache Spark (distributed computing framework), Jupyter Notebook (for interactive development), PostgreSQL (for storing quality metrics), and a complete Python data science stack pre-installed and configured for immediate use. Organizations working with big data face significant challenges in maintaining data quality across large datasets. This stack addresses these challenges by combining Deequ's constraint-based quality verification capabilities with Spark's distributed processing power. Teams can immediately start implementing quality checks, generating metrics, and validating datasets without spending weeks on environment setup and configuration. Whether you're a data engineering team, data science group, or enterprise analytics department, this AMI provides the foundation for robust data quality processes.
Highlights
- One-click deployment of a complete data quality environment with Deequ and Apache Spark pre-configured for immediate data validation at scale.
- Interactive data quality development with pre-installed Jupyter Notebook, PostgreSQL, and Python data science stack (NumPy, Pandas, Matplotlib, PySpark).
- Enterprise-ready data quality solution with seamless AWS integration, optimized for performance and security with zero setup and configuration time.
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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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 |
|---|---|
m5a.large Recommended | $0.09 |
t2.micro AWS Free Tier | $0.09 |
t3.micro AWS Free Tier | $0.09 |
r7a.16xlarge | $0.09 |
z1d.large | $0.09 |
c4.4xlarge | $0.09 |
hpc6id.32xlarge | $0.09 |
r5dn.large | $0.09 |
c6in.12xlarge | $0.09 |
m7a.xlarge | $0.09 |
Vendor refund policy
Intuz will not refund money in any case.However, you can cancel your subscription any time.
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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
Latest Stable Release
Additional details
Usage instructions
After launching the AMI and once the instance is running, access the Jupyter Notebook interface by entering http://instance-public-ipv4:8888/Â into your browser. The Jupyter interface provides immediate access to example notebooks demonstrating Deequ functionality for data quality verification. To access the PostgreSQL database, connect to your instance via SSH using the command ssh -i yourpemkeyname.pem ubuntu@yourinstanceip, then access PostgreSQL with sudo -i -u postgres followed by psql. For interactive Spark development with Deequ, you can launch the Spark Shell by connecting to your instance via SSH and running the spark-shell command, which provides an interactive Scala environment with Deequ and Spark. Docker is also included with so you can dockerize you application.
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, 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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