Aproveitando o Auto Loader

Até agora, mostramos como ingerir arquivos individuais, mas como fazer isso de forma mais eficiente e inteligente? É aqui que o Auto Loader entra em cena, pois ele permite processar novos arquivos de forma incremental assim que eles chegam em um local especificado.

Qual das afirmações a seguir descreve melhor como o Auto Loader funciona ao ingerir novos dados?

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Introduction to Databricks SQL

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Introduction to 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: Ingerindo dados Exercise 2: Ingestão de dados via GUI Exercise 3: Hidratando o lakehouse Exercise 4: Fazer upload de dados manualmente Exercise 5: Usando COPY INTO Exercise 6: Aproveitando o Auto Loader

Exercicio Atual

Exercise 7: Transformando dados Exercise 8: SQL e a arquitetura de medalhões Exercise 9: Criando uma camada de dados de cafés Exercise 10: Limpando tabelas brutas Exercise 11: Criando a camada Silver Exercise 12: Criando uma tabela de sinistros com prêmio alto

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