Buat sebuah visualisasi

Sekarang setelah kita menulis kueri awal, mari perluas analisis dengan visualisasi. Bayangkan seorang rekan bertanya:

Untuk memastikan premi yang lebih adil di seluruh pelanggan, bisakah Anda mengidentifikasi pencilan terendah dan tertinggi untuk rata-rata premi di setiap negara bagian?

Kueri sangat baik untuk membuat himpunan data berbentuk tabel, tetapi menemukan wawasan dari tabel sering kali sulit. Untuk melengkapi kueri kita, mari buat visualisasi yang memudahkan Anda mengeksplorasi hasilnya.

Latihan ini adalah bagian dari kursus

Pengantar Databricks SQL

Latihan interaktif praktis

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Mulai berolahraga

Latihan ini adalah bagian dari kursus

Pengantar Databricks SQL

SkillTag.level.intermediateSkillTag.label

4.7+

Mulai Kursus Gratis

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 dalam Data Intelligence Platform Exercise 2: Manfaat lakehouse untuk SQL Exercise 3: Mengeksplorasi Databricks SQL Exercise 4: Mengeksplorasi beberapa data Exercise 5: Partner Connect Exercise 6: Aset utama Databricks SQL Exercise 7: Mengklasifikasikan aset Databricks SQL Exercise 8: Buat sebuah kueri Exercise 9: Buat sebuah visualisasi

Latihan Saat Ini

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: 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