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The Einstein Analytics Cloud is a Salesforce product, much like the Sales & Service Cloud, you can buy this product and start using it out of the box. However, Wave is the platform that the Analytics Cloud is built on. This means that in the future, Partner App companies will be able to build their own BI applications on the Wave platform. This relationship might seem familiar, and that’s because it’s exactly the same as Salesforce.com and Force.com.

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You might be thinking to yourself, “Why would I need Analytics Cloud? Reports & Dashboards provide graphical representations just fine?”.Depending on the type of business you work for, this statement may very well hold true, not all companies are going to have a need for the Analytics Cloud. However, for the ones that do, it will be down to limitations of Salesforce Reports & Dashboards.
R&D are fantastic for viewing quick real time operational data to get a snapshot of what is happening inside your CRM. But to try and determine trends from extremely large sets of data can become problematic. One of the main attractions of the Analytics Cloud and other BI tools is the speed they can process large amounts of data. R&D in Salesforce can process a moderate amount of data, but it definitely is not suited to processing millions of rows.
Speed and processing power becomes even more apparent when you realise that Analytics Cloud can process data from external systems as well. It can grab data from ETL, CSV upload (Which can also be done on iOS devices) and of course data from your Salesforce CRM.
A couple of other points include is the fact that R&D can only historically report on data over 90 days, and the graphs and visual representations of data is far superior in the Analytics Cloud.
Analytics Solution Elements:

Dashboard:

Similar to the dashboard in a standard Salesforce instance, the Wave dashboard is a combination of reports (known as “Lenses”)with possible filters,links,images,etc.

Lenses:

These are saved explorations created from Datasets. It consists of

Measure:
  • What to measure
  • Is numeric values like count, sum, average etc…

Dimensions
  • How to measure
  • Value in dataset by which we can group content. columns like Opportunity won date, stage, lead source and so on.

Filters
  • Which data to include (Condition)

Dataset:

The name says it all for this one — a dataset is simply a set of data. For example, it could be a list of opportunities or a list of users. You also might have an augmented dataset, which basically combines two datasets into one (e.g. opportunity line items and opportunity information).
The data used in these datasets can enter Analytics Cloud through any of three channels:
  • A built-in standard Salesforce connector, which makes for a really nice GUI.
  • A manually uploaded CSV file.
  • Custom integrations through a middleware layer such as Informatical.

Dataflow:

The easiest way to think about dataflow is to view it as the bridge between the part of Analytics Cloud that receives data and the part that packages that data into datasets. Dataflow handles aspects of dataset augmentations as well as any translations that are needed before the final datasets can be created or updated.

Apps:

We can say , this is fancy word for folders analogous to “Reports and Dashboards” in Salesforce. Apps can contain Datasets, lenses and dashboards. By default, there are two types of Apps.
  • Private App.
  • Shared App.

As name suggests, anything in “private app” would be visible to logged in user and anything in “Shared App” would be visible to users having access to that App.

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