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What is Great Expectations?

Great Expectations is designed as an open standard that promotes improved data quality through collaboration. This tool aids data teams in overcoming challenges in their pipelines by facilitating efficient data testing, thorough documentation, and detailed profiling. For the best experience, it is recommended to implement it within a virtual environment. Those who are not well-versed in pip, virtual environments, notebooks, or git will find the Supporting resources helpful for their learning. Many leading companies have adopted Great Expectations to enhance their operations. We invite you to explore some of our case studies that showcase how different organizations have successfully incorporated Great Expectations into their data frameworks. Moreover, Great Expectations Cloud offers a fully managed Software as a Service (SaaS) solution, and we are actively inviting new private alpha members to join this exciting initiative. These alpha members not only gain early access to new features but also have the chance to offer feedback that will influence the product's future direction. This collaborative effort ensures that the platform evolves in a way that truly meets the needs and expectations of its users while maintaining a strong focus on continuous improvement.

What is Data Quality on Demand?

Data plays a vital role in multiple sectors of a business, such as sales, marketing, and finance. To fully leverage this data, it's important to maintain its integrity, protect it, and manage it effectively throughout its entire lifecycle. At Uniserv, we believe that data quality is a core principle of our identity and the services we offer. Our customized solutions convert your customer master data into a crucial asset for your business. The Data Quality Service Hub ensures that your customer data remains of the highest quality across all locations within your organization, including those overseas. We offer services that align your address information with international standards, utilizing the best reference data available. Furthermore, we validate email addresses, phone numbers, and banking information with meticulous attention to detail. If your data has duplicate entries, we can quickly pinpoint them according to your defined business requirements. Often, the identified duplicates can be merged automatically using predefined rules or categorized for manual assessment, which promotes an efficient data management workflow that boosts operational productivity. This thorough strategy for ensuring data quality not only aids in compliance but also builds trust and credibility in your customer relationships, ultimately leading to stronger business outcomes. An unwavering commitment to data quality fosters a culture of accountability within the organization, encouraging all departments to prioritize accurate data handling.

Media

Media

Integrations Supported

Acryl Data
Amazon Redshift
Amazon S3
Apache Airflow
Apache Spark
Astro
Dagster+
DataHub
Databricks Data Intelligence Platform
Flyte
Flyte
Jupyter Notebook
Meltano
MySQL
PostgreSQL
Prefect
SAP Store
SQL Server
Secoda
ZenML

Integrations Supported

Acryl Data
Amazon Redshift
Amazon S3
Apache Airflow
Apache Spark
Astro
Dagster+
DataHub
Databricks Data Intelligence Platform
Flyte
Flyte
Jupyter Notebook
Meltano
MySQL
PostgreSQL
Prefect
SAP Store
SQL Server
Secoda
ZenML

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Great Expectations

Company Website

greatexpectations.io

Company Facts

Organization Name

Uniserv

Date Founded

1969

Company Location

Germany

Company Website

www.uniserv.com/en/business-cases/customer-data-management/data-quality/

Categories and Features

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Categories and Features

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

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