Eine Unterabfrage schreiben

Wenn wir unsere Skills in der Databricks-Umgebung ausbauen, wollen wir mit fortgeschritteneren Analysen unserer Datensätze beginnen. In dieser Übung übst du den Einsatz von Unterabfragen, um weitere Versicherungsdaten zu analysieren.

Stell dir folgendes Szenario vor:

Du bist Data Analyst im Compensation-Team eines nationalen Versicherungsunternehmens. Du wurdest gebeten zu ermitteln, wie viele große Schadensfälle (>$1000) jede Mitarbeiterin bzw. jeder Mitarbeiter hatte, da dies in die Jahresboni einfließt.

Diese Übung ist Teil des Kurses

<Kurs>Einführung in Databricks SQL</Kurs>

Interaktive praktische Übung

Verwandle Theorie mit einer unserer interaktiven Übungen in die Praxis

Diese Übung ist Teil des Kurses

<Kurs>Einführung in Databricks SQL</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.7+

Kurs kostenlos starten

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: Querying in the Data Intelligence Platform Exercise 2: Analytical assets in Databricks SQL Exercise 3: Querying our coffee dataset Exercise 4: Analyzing insurance claims Exercise 5: Supplementing queries with functions Exercise 6: Visualizing query results Exercise 7: Creating an analytics application Exercise 8: The role of Partner Connect Exercise 9: A coffee data dashboard Exercise 10: Adding filters to dashboards Exercise 11: Using parameters in queries Exercise 12: Create an executive dashboard

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: Häufige Muster im Data Engineering Exercise 2: Anhängen vs. CDC Exercise 3: Kaffee-Umsatzdaten aktualisieren Exercise 4: Daten mit SQL optimieren Exercise 5: INSERT verwenden Exercise 6: MERGE verwenden Exercise 7: Fortgeschrittene Muster zur Datenanalyse Exercise 8: Window-Funktionen und Subqueries verstehen Exercise 9: Analyse von Kaffeeverkäufen nach Filiale Exercise 10: Eine Unterabfrage schreiben

Aktuelle Übung

Exercise 11: Eine Window-Funktion verwenden Exercise 12: Window-Funktionen und Subabfragen zusammen verwenden Exercise 13: Kursrückblick