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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
MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.
API Access
Has API
API Access
Has API
Integrations
Apache Spark
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Hadoop
Java
Kubernetes
MapReduce
Integrations
Apache Spark
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Hadoop
Java
Kubernetes
MapReduce
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Deequ
Website
github.com/awslabs/deequ
Vendor Details
Company Name
Apache Software Foundation
Founded
1995
Country
United States
Website
spark.apache.org/mllib/
Product Features
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization