1. Creating an analytics application
Hello, and welcome back! In this lesson, we will be going over how to use different parts of Databricks SQL to power your analytical applications.
2. What is an analytics application?
You might be asking yourself, what exactly is an analytics application? While they can take many different forms, at their core, analytical applications are the interface between different user groups and the data they want to glean insights from. This often will take the form of a dashboard, but can be a web application, data stream, or even a chat bot!
3. Filters and parameters
Interactivity is a key component of an analytics application, as this allows the end users to modify your work and derive their own insights, without you having to create them yourself.
In Databricks SQL, there are two main capabilities that pass on interactivity to your end users. First, we have filters, which should be very familiar. Filters allow your users to modify the query results by effectively creating a WHERE clause that they can modify. Filters do not require that your end users know how to write SQL, and give them a variety of form factors, such as text, numerical, or drop-down filter options.
Parameters, on the other hand, are a more flexible and powerful alternative to filters. Parameters allow the user to inject any arbitrary text into the SQL query. While these are often used as an alternative to filters or WHERE clauses, they can also be used for more advanced use cases, such as changing the function used, or dynamically letting the user select fields in the query.
4. Dashboards in Databricks SQL
Most users are comfortable with the idea of a dashboard. In Databricks SQL, a dashboard is a collection of visualizations from multiple queries or source tables, all viewable in a single location. These allow for a more dynamic and holistic approach to serving end users.
In dashboards, you can leverage existing queries, create new ones based on the data you have available, or even leverage some more advanced capabilities like AI and Large Language Models. These dashboards are collaborative and easily shareable through a URL.
5. Partner Connect
While Databricks SQL provides a fully developed dashboarding capability, many users already have a method of consuming data that they prefer. For these users, Databricks has its Partner Connect capability. Partner Connect allows users to connect their favorite partner technology directly to Databricks SQL Warehouses or clusters. You have seen Partner Connect already in this course, but is often more relevant here for analysts going through Databricks SQL. This means that end users will be able to leverage the scalability and power of Databricks compute, while also keeping their familiar and purpose-built tools.
6. Let's practice!
Now, let us review how to create these analytics applications using Databricks SQL.