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Average Ratings 0 Ratings
Description
Deequ is an innovative library that extends Apache Spark to create "unit tests for data," aiming to assess the quality of extensive datasets. We welcome any feedback and contributions from users. The library requires Java 8 for operation. It is important to note that Deequ version 2.x is compatible exclusively with Spark 3.1, and the two are interdependent. For those using earlier versions of Spark, the Deequ 1.x version should be utilized, which is maintained in the legacy-spark-3.0 branch. Additionally, we offer legacy releases that work with Apache Spark versions ranging from 2.2.x to 3.0.x. The Spark releases 2.2.x and 2.3.x are built on Scala 2.11, while the 2.4.x, 3.0.x, and 3.1.x releases require Scala 2.12. The primary goal of Deequ is to perform "unit-testing" on data to identify potential issues early on, ensuring that errors are caught before the data reaches consuming systems or machine learning models. In the sections that follow, we will provide a simple example to demonstrate the fundamental functionalities of our library, highlighting its ease of use and effectiveness in maintaining data integrity.
Description
IOMETE is a sovereign data lakehouse platform built to support modern data analytics and AI-driven workloads at enterprise scale. The platform allows organizations to store, manage, and process massive datasets within infrastructure they fully control. Unlike traditional cloud-only solutions, IOMETE can be deployed on-premises, in private clouds, public clouds, or hybrid environments. This flexible architecture helps organizations maintain full ownership of their data while avoiding vendor lock-in. The platform integrates data lakehouse capabilities with tools such as Spark processing, SQL query editors, Jupyter notebooks, and orchestration engines. These components allow data engineers, analysts, and data scientists to build pipelines, analyze datasets, and develop machine learning models in one environment. IOMETE also provides a centralized data catalog to help teams discover, manage, and understand their data assets. Advanced security controls allow organizations to manage access permissions across users, teams, and datasets with detailed governance rules. By reducing reliance on SaaS-based infrastructure, the platform can also help organizations optimize storage and compute costs. Overall, IOMETE delivers a flexible and secure data platform built specifically for the growing data demands of the AI era.
API Access
Has API
No
API Access
Has API
No
Integrations
Amazon Web Services (AWS)
No
Apache Spark
Yes
Looker
No
Tableau
No
Integrations
Amazon Web Services (AWS)
Yes
Apache Spark
No
Looker
Yes
Tableau
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
Yes
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Deequ
Website
github.com/awslabs/deequ
Vendor Details
Company Name
IOMETE
Founded
2020
Country
United States
Website
iomete.com
Product Features
Product Features
Data Governance
Access Control
No
Data Discovery
No
Data Mapping
No
Data Profiling
No
Deletion Management
No
Email Management
No
Policy Management
No
Process Management
No
Roles Management
No
Storage Management
No