Composable DataOps Platform vs. Paxata

Composable Analytics is a well suited application when you need to gain insight on many different sources of information. It is less appropriate if you are trying to use it as an application only instead of a tool to create insight into your data.

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

• Flexible Dataflow Application
• Easy to source data and then compose to create actionable items for supporting decision
• Allowed to do different analysis
• Extremely powerful tool to digest information to produce insight from the data

• 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

• I can not really comment because I do not have a complete fluency with the product as of now.
• Recreating the wheel on some uses but i guess that may be user error.

• 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

I am unable to answer this question at this time

I would compare them to custom system like Morgan Stanley has that I use. A more appropriate company is Novus or LightKeeper. This product is potentially much cheaper but Novus and Lightkeeper already have built in Analytics screens that you could potential build yourself with Composable.

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

• 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