Informatica Data Prep vs. Paxata

I am working in a medical device company and we require a large amount of data to analyze. Handling large amounts of data using this tool is very easy and the user interface is easy and simple to navigate. Earlier we used to use excel and text files to extract the data but now using this tool we have improved in transforming the data.

Incentivized

Paxata can be highly useful to someone who doesn't like/have any experience with writing codes to treat data before using it as input into BI dashboards. Paxata can accelerate data cleaning in environments where a large amount of unclean data is generated and business decisions on the go are required. It performs really well while dealing with natural language.

Incentivized

• Improve the business process outcomes
• Create internal/external operational efficiencies
• Product functionality and performance

Incentivized

• Visualize distributions in large data sets effectively which enable the user to quickly spot outliers and treat them appropriately
• Provides recommendation to merge datasets based on matching column values
• The cluster and edit feature in my opinion is its most powerful feature and reduces cardinality in column with text

Incentivized

• Dependency with other systems like Hadoop as it's data repository.
• Service slow connection sometimes.
• Configuration should be easy.

Incentivized

• Doesn't provide recommendation on how to impute values
• There is a lag quite often
• We can say whether a column has errors or quality issues in the first look

Incentivized

To improve the business process outcomes we have purchased this tool. By using this tool data preparation is easy and much [more] flexible and I would suggest [it as] a good option to try. As it is built with powerful AI and supports as large datasets as we can get without any reduction in performance.

Incentivized

Paxata is a much better tool when it comes to handling natural language but Talend provides recommendations on how to impute missing values and outliers. Paxata provides recommendations on dataset tie-ups and joins but Talend doesn't provide any such recommendations. In paxata you can visualize distribution of data in a column and filter them by dragging and selecting the section you'd like to retain

Incentivized

• Dependency on hadoop cluster as its repository

Incentivized

• It saves time to clean data
• It reduces the requirement of too many data engineer/stewards and hence adds positive impact on the return of the business

Incentivized