Trifacta

Trifacta is an open and interactive cloud platform for data engineers and analysts to collaboratively profile, prepare, and pipeline data for analytics and machine learning.

You can find Trifacta in these categories:

Trifacta in Data quality tools

https://www.trifacta.com/

Trifacta is an open and interactive cloud platform for data engineers and analysts to collaboratively profile, prepare, and pipeline data for analytics and machine learning. It presents automated visual representations of data based upon its content in the most compelling visual profile. In addition, every profile is completely interactive, allowing the user to simply select certain elements of the profile to prompt transformation suggestions.

Commercial:	Commercial
Data cleansing:
Data Discovery & Search:
Data Profiling:
Data standarization:
Free edition:

Learn more on vendor website Browse Data quality tools

Trifacta in Data profiling tools

https://www.trifacta.com/

Trifacta is an open and interactive cloud platform for data engineers and analysts to collaboratively profile, prepare, and pipeline data for analytics and machine learning. For ease of data profiling, Trifacta automatically identifies dataset formats, schemas, specific attributes, and relationships across attributes and datasets, along with associated metadata for each dataset.

Access control:
Commercial:	Commercial
Desktop/Cloud:	Cloud
Excel workbooks:
Flat files:
Free edition:
Metadata identification:
NoSQL sources:
Runs on: (for desktop):	-
Sensitive data discovery:
SQL sources:
Statistics of data:	Avg,Max,Min,Stdev
Tagging data:

Learn more on vendor website Browse Data profiling tools

Trifacta in Data discovery tools

https://www.trifacta.com/data-discovery/

Trifacta offers a unique end-to-end data wrangling tool to conduct a data discovery process by taking raw data sources and transforming them into the appropriate format for analysis. With Trifacta, the user is able to see how the data will be used for different types of analysis. Trifacta has a six-step iterative data-wrangling process that leads to a more accurate analysis. The steps include discovering, structuring, cleansing, enriching, validating, and publishing. Moreover, it provides users with the best visualization for each specific type of data automatically.