Introduction

Aggregations allow you to derive new datasets from existing database tables by slicing, summarizing, combining, filtering, and grouping data. These database-level transformations are defined using SQL SELECT queries, which contain your desired data transformation logic. Moose automatically creates a materialized view in your database using the provided query, which is continuously updated as new data is added to your source table(s).

Why Use Aggregations

Aggregations are ideal for:

Pre-computing results for frequently used queries.

Performing database-level transformations that should be consistently triggered as new data is ingested.

Ensuring high-performance querying and analysis by keeping transformed data readily available.

Creating reusable data subsets that can be utilized by other parts of your application, including other Aggregations and Consumption APIs.

Aggregation Lifecycle

Define: Write a SQL SELECT query that defines your desired data transformation.

Save: Save your query in the /aggregations folder of your project.

Execute: Moose executes your query to create a materialized view in your database.

Update: The materialized view is continuously updated as new data is added to your source table(s).

Creating and Using Moose Streaming Functions Creating Aggregations