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In the context of DP-900 (Azure Data Fundamentals) and the related analytics concepts, the main types of data analytics to understand are:
- Descriptive analytics
- Purpose: Answers “What happened?”
- Focus: Summarizes historical data to provide insight into past performance or events.
- Typical outputs: Reports, dashboards, summary statistics, trend lines.
- Azure/DP-900 context:
- Using Azure SQL Database or Azure Synapse Analytics to query historical data.
- Using Power BI to build reports and dashboards that visualize past sales, usage, or operational metrics.
- Diagnostic analytics
- Purpose: Answers “Why did it happen?”
- Focus: Drills into data to find causes, correlations, and relationships behind the results seen in descriptive analytics.
- Typical outputs: Drill-through reports, correlation analyses, segmented views (by region, product, time period).
- Azure/DP-900 context:
- Using SQL queries in Azure SQL Database or Synapse to slice data by different dimensions.
- Using Power BI’s interactive visuals and filters to explore why a KPI changed (for example, by product category or geography).
- Predictive analytics
- Purpose: Answers “What is likely to happen?”
- Focus: Uses historical data and statistical or machine learning models to forecast future outcomes.
- Typical outputs: Forecasts, risk scores, demand predictions.
- Azure/DP-900 context:
- Large-scale analytical stores such as Azure Synapse Analytics or Azure Databricks are used as platforms for building and running predictive models.
- DP-900 expects recognition that these services support advanced analytics and machine learning over large datasets.
- Prescriptive analytics
- Purpose: Answers “What should be done?”
- Focus: Recommends actions based on predictive models and business rules to optimize outcomes.
- Typical outputs: Recommended decisions, optimized schedules, next-best-action suggestions.
- Azure/DP-900 context:
- While DP-900 is fundamentals-level and does not go deep into optimization algorithms, it introduces the idea that Azure’s analytics services (for example, Synapse and Databricks) can be used in solutions that not only predict outcomes but also drive recommended actions, which can then be surfaced via applications or Power BI.
In DP-900, these analytics types are framed within:
- Core data and analytics concepts (transactional vs. analytical workloads).
- Large-scale analytics services such as Azure Synapse Analytics and Azure Databricks.
- Data visualization and reporting with Power BI, which primarily supports descriptive and diagnostic analytics but can also present predictive and prescriptive results produced by Azure analytics platforms.
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