Salesforce Einstein Analytics
& CRM Analytics
Course in Pune

Dataflows are the data pipeline engine in CRM Analytics — they define how data is extracted from Salesforce, transformed, augmented, and loaded into Datasets that Analytics can query. This module covers the Dataflow editor interface, the node types that make up a Dataflow (sfdcDigest for extracting Salesforce data, augment for joining datasets, computeExpression for calculated fields, filter for row filtering, edgemart for referencing existing datasets, and flatten for handling hierarchical data like the role hierarchy), scheduling Dataflow runs, and monitoring Dataflow execution and troubleshooting failures. We build a multi-node Dataflow that combines Opportunity, Account, and User data into a single enriched Sales dataset — exactly the kind of dataset that a sales performance dashboard needs. Understanding the Dataflow node types and how to combine them for complex data preparation is one of the most practical skills in the programme.

Recipes are CRM Analytics's visual, no-code data preparation tool — and for most data preparation tasks, they are significantly easier to use than writing raw Dataflow JSON. This module covers the Recipe builder interface, input nodes (Salesforce objects, CSV uploads, existing datasets), transformation nodes (Add Column with formulas, Bucket for grouping, Filter, Aggregate, Join for combining multiple datasets, and Flatten), output node configuration, and running and scheduling Recipes. We work through multiple Recipe scenarios — enriching a Case dataset with Account and User information, creating a pre-aggregated monthly performance dataset that reduces query time on dashboards, and joining Salesforce data with an external CSV of benchmark targets. The module also covers when to use a Recipe vs a Dataflow — understanding this choice is a question that comes up regularly in CRM Analytics consultant interviews.

A Lens is the analytical exploration layer in CRM Analytics — it is where you slice, dice, filter, and visualise data from a Dataset before assembling insights into a Dashboard. This module covers creating Lenses from Datasets, the different chart types available (bar, line, scatter, compare table, treemap, funnel, map, and more), grouping, filtering, and sorting in the visual interface, and SAQL — the Salesforce Analytics Query Language. SAQL is CRM Analytics's query language (similar in concept to SQL but with its own syntax), and it is what you write when the visual lens editor is not sufficient for your analytical requirement. We cover SAQL syntax — foreach, groupby, filter, foreach with order and limit — and practice writing SAQL queries for complex analytical requirements that cannot be expressed in the visual interface. Students who have SQL experience will find SAQL conceptually familiar; those who are new to query languages will work through a structured progression from simple to complex queries.

Dashboards are the product that end users and business leaders actually see — and building dashboards that are visually clear, intuitively interactive, and genuinely useful requires specific skills that go well beyond just dragging charts into a canvas. This module covers the Dashboard Designer interface in depth — adding and configuring charts from Lenses, working with Steps (the query objects that power each chart), building global filters that dynamically update all charts on the dashboard, faceting (so selecting a value in one chart automatically filters all related charts), creating date range selectors, building compare table visualisations for multi-dimensional data, using number widgets for KPI highlights, and designing the dashboard layout for different screen sizes. We build two complete business dashboards: a Sales Performance Dashboard for a VP of Sales and a Service Operations Dashboard for a contact centre manager — both with the filters, interactions, and KPI visibility that real business users need.

In most enterprise CRM Analytics implementations, not every user should see all the data in every dataset. A regional sales manager should see pipeline data for their region only. An account executive should see their own opportunities and their team's, but not everyone else's. Security Predicates are CRM Analytics's mechanism for enforcing this kind of row-level security — they are filter conditions that are automatically applied to every query against a Dataset based on the logged-in user's attributes. This module covers Security Predicate syntax, the User object attributes that can be referenced, applying Security Predicates to Datasets, testing security configurations with different user profiles, and the common patterns for role-hierarchy-based security and team-based security. Security Predicates are consistently one of the most tested areas in the Analytics Consultant exam, and they come up in virtually every enterprise CRM Analytics implementation project.

Einstein Discovery is the part of CRM Analytics that genuinely separates it from traditional BI tools. It uses machine learning to find patterns in your data, identify the key drivers of a metric (for example, what factors most influence opportunity close rates), generate automated stories that explain findings in plain language, and deploy AI predictions as fields directly on Salesforce records — so a sales rep can see a predicted close probability on every opportunity without leaving Salesforce. This module covers creating an Einstein Discovery Story from a Dataset, reading and interpreting the Story outputs (Key Predictors, Outcome Distribution, What-If analysis), deploying a Story's predictions to Salesforce records as a Prediction field, and understanding the governance and data quality requirements for meaningful Einstein Discovery predictions. We build an Opportunity Win Prediction story that surfaces close probability on the Opportunity record — a real, immediately deployable AI capability.

Analytics Apps are packaged collections of Datasets, Dataflows, and Dashboards that can be deployed as a unit — either from AppExchange (Salesforce's pre-built analytics apps for Sales, Service, B2B Marketing, and more) or as custom apps for specific business requirements. This module covers deploying and configuring pre-built Analytics Apps, building custom Analytics Apps, embedding CRM Analytics dashboards directly in Salesforce Lightning pages (so business users see analytics without going to Analytics Studio), and embedding dashboards in Experience Cloud customer portals. The exam preparation section covers all Analytics Consultant certification exam domains — Data Layer, Security, Dashboard Design, Einstein Discovery, Sharing and Governance — with domain-specific revision, two full timed mock exams, and exam strategy coaching. Most Aapvex students are ready to attempt the exam within 3–5 weeks of completing the programme.