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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
Pepperdata autonomous, application-level cost optimization delivers 30-47% greater cost savings for data-intensive workloads such as Apache Spark on Amazon EMR and Amazon EKS with no application changes. Using patented algorithms, Pepperdata Capacity Optimizer autonomously optimizes CPU and memory in real time with no application code changes.
Pepperdata automatically analyzes resource usage in real time, identifying where more work can be done, enabling the scheduler to add tasks to nodes with available resources and spin up new nodes only when existing nodes are fully utilized. The result: CPU and memory are autonomously and continuously optimized, without delay and without the need for recommendations to be applied, and the need for ongoing manual tuning is safely eliminated.
Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing Spark utilization, and freeing developers from manual tuning to focus on innovation.
API Access
Has API
No
API Access
Has API
No
Integrations
Apache Spark
Yes
AWS Marketplace
No
Amazon EKS
No
Amazon EMR
No
Google Cloud Managed Service for Apache Spark
No
Integrations
Apache Spark
Yes
AWS Marketplace
Yes
Amazon EKS
Yes
Amazon EMR
Yes
Google Cloud Managed Service for Apache Spark
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
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
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
No
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)
No
In Person
Yes
Vendor Details
Company Name
Deequ
Website
github.com/awslabs/deequ
Vendor Details
Company Name
Pepperdata, Inc.
Founded
2012
Country
United States
Website
www.pepperdata.com
Product Features
Product Features
Application Performance Monitoring (APM)
Baseline Manager
Yes
Diagnostic Tools
Yes
Full Transaction Diagnostics
Yes
Performance Control
Yes
Resource Management
Yes
Root-Cause Diagnosis
Yes
Server Performance
Yes
Trace Individual Transactions
Yes
Cloud Cost Management
Cost Reduction Optimization
Yes
Dashboard
Yes
Data Import/Export
No
Data Storage
No
Data Visualization
Yes
Resource Usage Reporting
Yes
Roles / Permissions
Yes
Spend and Cost Reporting
Yes