Visualizzare i risultati delle query

Le query sono ottime per restituire informazioni a livello di riga e molto granulari per rispondere a una domanda specifica, ma spesso non sono sufficienti per un data analyst. In questo esercizio useremo i risultati della query precedente e genereremo visualizzazioni per rispondere meglio alle domande sul nostro servizio clienti.

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Introduzione a Databricks SQL

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Introduzione a Databricks SQL

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Build a strong data warehousing foundation with the Databricks Data Intelligence Platform! You will learn how Databricks and the lakehouse architecture set your organization up for a modern SQL and BI stack. You will also explore the key components of the Databricks SQL product, ranging from data storage techniques to compute optimizations.

Exercise 1: SQL in the Data Intelligence Platform Exercise 2: Benefits of the lakehouse for SQL Exercise 3: Exploring Databricks SQL Exercise 4: Exploring some data Exercise 5: Partner Connect Exercise 6: Databricks SQL key assets Exercise 7: Classifying Databricks SQL assets Exercise 8: Create a query Exercise 9: Build a visualization

Build a strong data foundation for the lakehouse architecture! You will learn how to ingest, transform, and model your data using the capabilities in Databricks SQL. You will explore GUI-based and programmatic approaches to building out the medallion architecture, and creating a data ecosystem ready for any analytics workload.

Exercise 1: Ingesting Data Exercise 2: GUI-based data ingest Exercise 3: Hydrating the lakehouse Exercise 4: Upload data manually Exercise 5: Using COPY INTO Exercise 6: Leveraging Auto Loader Exercise 7: Transforming data Exercise 8: SQL and the medallion architecture Exercise 9: Creating a coffee data layer Exercise 10: Cleaning up raw tables Exercise 11: Creating the silver layer Exercise 12: Creating a table of large premium claims

Create powerful data insights using Databricks SQL! You will learn how to write queries, create visualizations, and power dashboards using in-platform Databricks capabilities. You will practice leveraging SQL code, filters, and parameters to create a robust analytical application for your end users.

Exercise 1: Eseguire query nella Data Intelligence Platform Exercise 2: Asset analitici in Databricks SQL Exercise 3: Eseguire query sul nostro insieme di dati del caffè Exercise 4: Analizzare i sinistri assicurativi Exercise 5: Arricchire le query con funzioni Exercise 6: Visualizzare i risultati delle query

Esercizio in corso

Exercise 7: Creare un'applicazione di analytics Exercise 8: Il ruolo di Partner Connect Exercise 9: Un dashboard sui dati del caffè Exercise 10: Aggiungere filtri ai dashboard Exercise 11: Uso dei parametri nelle query Exercise 12: Crea un dashboard per i dirigenti

In the final chapter, you will learn some more advanced techniques that leverage the key differentiators of the Databricks platform. You will learn how to handle high-velocity and fast-changing data using window functions, and will be able to merge datasets as they come in.

Exercise 1: Common data engineering patterns Exercise 2: Append vs. CDC Exercise 3: Updating coffee sales data Exercise 4: Optimizing data with SQL Exercise 5: Using INSERT Exercise 6: Using MERGE Exercise 7: Advanced data analysis patterns Exercise 8: Understanding window functions and sub-queries Exercise 9: Analyzing coffee sales by store Exercise 10: Writing a sub-query Exercise 11: Using a window function Exercise 12: Using windows and sub-queries together Exercise 13: Course recap