Menggabungkan window dan subquery

Sekarang setelah kita mengetahui cara memanfaatkan teknik yang lebih lanjut di lingkungan Databricks, mari kita praktikkan. Bayangkan skenario berikut:

Direktur Hubungan Vendor sedang menyusun prioritas pendanaan untuk berbagai vendor yang bekerja sama dengan perusahaan Anda dalam menangani klaim asuransi. Mereka ingin memahami vendor mana yang memproses klaim pemilik rumah terbanyak untuk pelanggan Anda, lalu memberikan pendanaan tambahan untuk vendor terbaik.

Dalam kasus ini, kita perlu menggunakan fungsi window dan subquery dalam kueri yang sama!

Latihan ini adalah bagian dari kursus

Pengantar Databricks SQL

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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 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: Pola rekayasa data umum Exercise 2: Append vs. CDC Exercise 3: Memperbarui data penjualan kopi Exercise 4: Optimasi data dengan SQL Exercise 5: Menggunakan INSERT Exercise 6: Menggunakan MERGE Exercise 7: Pola analisis data tingkat lanjut Exercise 8: Memahami window functions dan sub-query Exercise 9: Menganalisis penjualan kopi per toko Exercise 10: Menulis subkueri Exercise 11: Menggunakan fungsi window Exercise 12: Menggabungkan window dan subquery

Latihan Saat Ini

Exercise 13: Rekap kursus