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.

SQL in the Data Intelligence Platform

Benefits of the lakehouse for SQL

Exploring Databricks SQL

Exploring some data

Partner Connect

Databricks SQL key assets

Classifying Databricks SQL assets

Create a query

Build a visualization

Data Warehousing in Databricks

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.

Ingesting Data

Options

True

False

GUI-based data ingest

Hydrating the lakehouse

Upload data manually

Using COPY INTO

Leveraging Auto Loader

Transforming data

Medallion architecture

SQL and the medallion architecture

Creating a coffee data layer

Cleaning up raw tables

Creating the silver layer

Creating a table of large premium claims

Data engineering with Databricks SQL

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.

Querying in the Data Intelligence Platform

Analytical assets in Databricks SQL

Querying our coffee dataset

Analyzing insurance claims

Supplementing queries with functions

Visualizing query results

Creating an analytics application

The role of Partner Connect

A coffee data dashboard

Adding filters to dashboards

Using parameters in queries

Create an executive dashboard

Data analysis with Databricks SQL

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.

Common data engineering patterns

Append vs. CDC

Updating coffee sales data

Optimizing data  with SQL

Using INSERT

Using MERGE

Advanced data analysis patterns

Understanding window functions and sub-queries

Analyzing coffee sales by store

Writing a sub-query

Using a  window function

Using windows and sub-queries together

Course recap

Advanced patterns with Databricks SQL

Every organization has a need for a data warehousing capability to run critical business intelligence analyses. Over the years, there have been many different tools to satisfy these needs, but often were separate technology stacks that struggled to keep up with modern data needs and scale.

Databricks is a powerful Data Intelligence Platform that can satisfy many of an organization's analytical needs.  By leveraging Databricks SQL, an organization can utilize a hyper scalable and performant data warehouse solution directly in the lakehouse architecture.

In this course, you will be introduced to the key components and workflows within Databricks SQL.

First, you will learn how to use Databricks SQL to perform different data engineering tasks and moving your data through the medallion architecture to serve data teams. You will earn the common techniques many data engineers use, how to choose between different data storage techniques, and how to keep your data up to date.

Next, you will learn how to analyze your data in Databricks SQL like many data analysts do in the industry. You will learn the common functions and techniques using ANSI based SQL, and will then create in-platform visualizations and dashboards to better tell a story about your data.

Finally, 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 together as they come in.

Every organization has a need for a data warehousing capability to run critical business intelligence analyses. Over the years, there have been many different tools to satisfy these needs, but often were separate technology stacks that struggled to keep up with modern data needs and scale.

Databricks is a powerful Data Intelligence Platform that can satisfy many of an organization's analytical needs. By leveraging Databricks SQL, an organization can utilize a hyper scalable and performant data warehouse solution directly in the lakehouse architecture.

In this course, you will be introduced to the key components and workflows within Databricks SQL.

First, you will learn how to use Databricks SQL to perform different data engineering tasks and moving your data through the medallion architecture to serve data teams. You will earn the common techniques many data engineers use, how to choose between different data storage techniques, and how to keep your data up to date.

Next, you will learn how to analyze your data in Databricks SQL like many data analysts do in the industry. You will learn the common functions and techniques using ANSI based SQL, and will then create in-platform visualizations and dashboards to better tell a story about your data.

Finally, 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 together as they come in.

Intermediate SQL

Introduction to Databricks

Learn Databricks SQL for data engineering, analytics, and real-time data workflows in the lakehouse architecture.

Introduction to Databricks SQL

アソシエイトデータエンジニア Databricks内で

データアナリスト Databricksで

LakeFlow Connect can ingest data from databases, files, and SaaS applications

LakeFlow Connect consists of managed and pre-built connectors

When you upload data via a GUI-based tool in Databricks, you create a table in Unity Catalog

You can use the manual upload capability with databases, as well as files

Both of the specified data ingestion techniques require some programming to be done

LakeFlow Connect does not keep your data up to date, and must be reconfigured each time

GUI-based data ingest

Introduction to Databricks SQL

実践的なインタラクティブ演習