Learning Einstein Analytics: Unlock critical insights with Salesforce Einstein Analytics

A dataset is a collection of related data that is stored in a denormalized, yet highly compressed form. You can create the dataset from different resources such as Excel, Salesforce, or other databases. In other words, you can say that it is a data resource, specially formatted to create analytics and reports on it. In Einstein Analytics, all the fields of dataset come under three categories such as date, dimension, and measure.

A measure is a quantitative value, for example, amount, price, profit, and loss. Measure can be used to make mathematical calculations such as sum, average, maximum, minimum, and so on.

A dimension is a qualitative value, for example, city, region, and status. Dimensions can be used to create grouping and filtering. As it is a qualitative value, you cannot do math in this field.

A date can be represented as a day, month, year, and, optionally, time. We can use the date field to group, filter, and perform math.

Dataset builder is a point and click UI feature provided by Salesforce to create datasets. You can create a single dataset for a Salesforce object. Data can be created based on one or more related Salesforce objects. 

A lens is a particular view of a  dataset's data. Just like reports in Salesforce, the lens provides insights into data. This helps you analyze and visualize your data. 

 A visualization is a pictorial representation of dashboards, application, and lenses. Commonly, it can be a line chart, bar chart, stack chart, tables, pivots, or compare tables. Every visualization has a query associated with it.

A dashboard is a collection of charts, metrics, and tables. We can have one or more lenses in one dashboard.

A designer is a user interface where a user can create dashboards.

Dashboard JSON is the JSON file for your dashboard. This file includes the information related to your widgets, their location, settings, static steps, and how they are connected. 

An explorer is an interface where you explore datasets and lenses. It is the easiest way to access your business data and get data insights. Using the explorer, users can add measures, grouping, filters, and so on. In the UI, users can switch between Chart Mode, Table Mode, and SAQL Mode.

An application is a curated set of one or more dashboards and lenses. For example, If you have created four dashboards for the sales team and two dashboards for the service team then you can create two separate applications (like the folder) one for the sales team and another for the service team and move the dashboards and lenses to the respective applications.

A transformation refers to the manipulation of data. You can add transformations to a dataflow to extract data from Salesforce objects or datasets, transform datasets that contain Salesforce or external data, and register datasets.

For example, you can use transformations to join data from two related datasets and then register the resulting dataset to make it available for queries.  

Salesforce Analytics Query Language (SAQL) is used to access data from a dataset. It is a query language for Analytics platform. Just like all other query languages, SAQL retrieves data from the dataset. Lenses and dashboards also use SAQL behind the scenes. It gathers the meaningful data for visualizations. We can use SAQL to handle complex views such as working with multiple datasets to get a single view.

A predicate is a filter condition that defines row-level access for each record from the dataset. Define a predicate for each dataset on which you want to restrict access to records. In other words, row-level security is enforced by the predicate.

A metadata file is a JSON file that describes the structure of an external data file.

You can use a dataflow to create one or more datasets based on data from Salesforce objects or existing datasets. A dataflow is a set of instructions that specify what data to extract from Salesforce objects or datasets, how to transform the datasets, and which datasets to make available for querying.

A dataflow job processes the logic in a dataflow. 

For example, after creating a dataset, it will create a process that will kick-start the dataset creation process. We can monitor dataflow jobs from the data manager.