Append vs. CDC

Bij het inladen van data kijk je niet alleen naar de kenmerken van de binnenkomende data zelf. Je kijkt ook naar hoe deze data zich verhoudt tot bestaande gegevenssets waar het onderdeel van is. Zoals je in de video zag, kun je data-inname grofweg in twee categorieën verdelen: data toevoegen (append) en Change Data Capture (CDC).

Deze oefening maakt deel uit van de cursus

Introductie tot Databricks SQL

Cursus bekijken

Praktische interactieve oefening

Zet theorie om in actie met een van onze interactieve oefeningen.

Begin met trainen

Deze oefening maakt deel uit van de cursus

Introductie tot Databricks SQL

SkillTag.level.intermediateSkillTag.label

4.7+

Begin de cursus 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: Veelvoorkomende data-engineeringpatronen Exercise 2: Append vs. CDC

Huidige oefening

Exercise 3: Gegevens over koffieverkopen bijwerken Exercise 4: Data optimaliseren met SQL Exercise 5: INSERT gebruiken Exercise 6: MERGE gebruiken Exercise 7: Geavanceerde patronen voor data-analyse Exercise 8: Windowfuncties en subquery's begrijpen Exercise 9: Koffieverkoop per winkel analyseren Exercise 10: Een subquery schrijven Exercise 11: Een windowfunctie gebruiken Exercise 12: Windows en subquery's samen gebruiken Exercise 13: Cursusafronding