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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
Oracle Cloud Infrastructure (OCI) Data Flow is a comprehensive managed service for Apache Spark, enabling users to execute processing tasks on enormous data sets without the burden of deploying or managing infrastructure. This capability accelerates the delivery of applications, allowing developers to concentrate on building their apps rather than dealing with infrastructure concerns. OCI Data Flow autonomously manages the provisioning of infrastructure, network configurations, and dismantling after Spark jobs finish. It also oversees storage and security, significantly reducing the effort needed to create and maintain Spark applications for large-scale data analysis. Furthermore, with OCI Data Flow, there are no clusters that require installation, patching, or upgrading, which translates to both time savings and reduced operational expenses for various projects. Each Spark job is executed using private dedicated resources, which removes the necessity for prior capacity planning. Consequently, organizations benefit from a pay-as-you-go model, only incurring costs for the infrastructure resources utilized during the execution of Spark jobs. This innovative approach not only streamlines the process but also enhances scalability and flexibility for data-driven applications.
API Access
Has API
No
API Access
Has API
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$0.0085 per GB per hour
Free Trial
No
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
No
Mac
No
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
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Deequ
Website
github.com/awslabs/deequ
Vendor Details
Company Name
Oracle
Founded
1977
Country
United States
Website
www.oracle.com/big-data/data-flow/
Product Features
Product Features
Big Data
Collaboration
No
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
No
No-Code Sandbox
No
Predictive Analytics
No
Templates
No
Data Science
Access Control
No
Advanced Modeling
No
Audit Logs
No
Data Discovery
No
Data Ingestion
No
Data Preparation
No
Data Visualization
No
Model Deployment
No
Reports
No