Journey of Data¶
Mammoth helps create a feedback-driven, data flow Pipeline so you can see what your data really shows. Here is a snapshot of how that might look.
Add New Data¶
Mammoth allows you to fetch or add new data from different sources.
Local Files: Upload files from your system.
Databases: Connect to a supported databases .
API: From a cloud API like Salesforce or Google Analytics;
Webhooks: Push data to Mammoth using webhooks .
You can also pull any existing public Datasets as a comma separated values (CSV) file or a zipped CSV hyperlink.
When opening a Dataset, Mammoth automatically creates the first View and then you can create an Explore card to begin looking for quick insights, anomalies or patterns. For more information on why Explore cards are a powerful tool, see here.
Creating Multiple Views¶
Views can provide new perspectives on your data and let you transform and analyse it using simple or complex Pipelines. For instance:
You can create multiple Views on the same Dataset;
A View can blend data from many Datasets and Views;
Multiple Views can add their end data to the same Dataset.
Setting up multiple Views of a Dataset is an incredibly useful asset. For instance:
If you have raw data in many columns, each showing different statistics, the columns may become difficult to deal with. To simplify, create multiple Views on the same Dataset and hide the irrelevant columns so the Pipeline is ready for analysis;
Or, if only a few columns need to be exposed for downstream analysis, you can hide the rest and save the View as a new Dataset;
Or, you can merge data from another View based on a common key;
Or, if your data comes from multiple sources and is structured differently, you can create Pipelines on each Dataset through its Views to standardise output and then append into a target Dataset.
After a Task is complete, save your data to another Dataset or publish it for further analysis to an external system like PowerBI, Elasticsearch/Kibana or Google Data Studio.