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features
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support

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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

Managed Service for Apache Spark is a unified Google Cloud platform designed to run Apache Spark workloads with greater ease, performance, and scalability. It offers both serverless and fully managed cluster deployment options, allowing users to choose the best model for their needs. The platform eliminates the need for infrastructure management, enabling teams to focus on data processing and analytics. With Lightning Engine, it delivers up to 4.9x faster performance than open-source Spark, improving efficiency for large-scale workloads. It integrates AI-powered tools like Gemini to assist with code generation, debugging, and workflow optimization. The service supports open data formats such as Apache Iceberg and connects seamlessly with Google Cloud services like BigQuery and Knowledge Catalog. It is designed for a wide range of use cases, including ETL pipelines, machine learning, and lakehouse architectures. Built-in security features and IAM integration ensure strong data governance. Flexible pricing models allow users to pay based on job execution or cluster uptime. Overall, it helps organizations modernize their data infrastructure and accelerate analytics workflows.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Apache Spark Yes 
Ascend No 
Collibra No 
Gemini Enterprise Agent Platform Notebooks No 
Google Cloud Bigtable No 
Google Cloud GPUs No 
Google Cloud Knowledge Catalog No 
Google Cloud Managed Service for Apache Airflow No 
Google Cloud Platform No 
Google Cloud Profiler No 
IBM watsonx.data integration No 
Immuta No 
New Relic No 
Orchestra No 
Pantomath No 
Privacera No 
Qubole No 
Syntasa No 
Ternary No 
Unravel No 

Integrations

Apache Spark Yes 
Ascend Yes 
Collibra Yes 
Gemini Enterprise Agent Platform Notebooks Yes 
Google Cloud Bigtable Yes 
Google Cloud GPUs Yes 
Google Cloud Knowledge Catalog Yes 
Google Cloud Managed Service for Apache Airflow Yes 
Google Cloud Platform Yes 
Google Cloud Profiler Yes 
IBM watsonx.data integration Yes 
Immuta Yes 
New Relic Yes 
Orchestra Yes 
Pantomath Yes 
Privacera Yes 
Qubole Yes 
Syntasa Yes 
Ternary Yes 
Unravel 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 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

Google

Founded

1998

Country

United States

Website

cloud.google.com/products/managed-service-for-apache-spark

Product Features

Product Features

Big Data

Collaboration Yes 
Data Blends Yes 
Data Cleansing No 
Data Mining Yes 
Data Visualization Yes 
Data Warehousing Yes 
High Volume Processing Yes 
No-Code Sandbox No 
Predictive Analytics Yes 
Templates No 

Data Analysis

Data Discovery Yes 
Data Visualization Yes 
High Volume Processing Yes 
Predictive Analytics Yes 
Regression Analysis Yes 
Sentiment Analysis Yes 
Statistical Modeling Yes 
Text Analytics No 

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