This blog post provides a step-by-step guide to integrating Dbt (Data Build Tool) and Great Expectations for enhancing data quality with E-commerce analytics as an example. It covers the entire process from setting up a project, transforming raw data into analytics-ready tables, to implementing data quality tests using a sample e-commerce dataset in Snowflake.
Data quality is crucial for making good data-driven decisions. As data volumes grow exponentially, automating data testing and documentation is key to maintaining high quality data in analytics databases. Dbt (Data Build Tool) and Great Expectations are two popular open-source frameworks that help tackle different aspects of managing analytics engineering pipelines.
In this post, we will walk through an end-to-end example of integrating Dbt and Great Expectations using an e-commerce dataset. We will load sample e-commerce order data into a Snowflake data warehouse. Dbt will help us transform the raw data into analytics-ready tables and views. Great Expectations will then help create data quality test suites validate the analytics dbt models.
Before we begin, let's outline the key items we need:
Sample E-Commerce Data Model
Our raw sample e-commerce data has information on customer orders, products, payments and order reviews stored in Snowflake stages.
Here is a snippet of what the raw orders data looks like:
And the products data:
We want to transform this raw data into an analytics dataset with clean, derived metrics like order revenue, product margins etc.
Here is what our target analytics data model looks like:
This end-to-end pipeline is depicted below:
We first setup a dbt project to build the transformations and generate the analytics dataset.
Initialize a dbt project ecomm_dbt and configure the connection to the Snowflake database ECOMM_ANALYTICS
Define the source and target schemas in the schema.yml file:
Build the dbt SQL models to transform raw data:
Run tests on dbt project:
Next, we setup Great Expectations to define data quality expectations and validate the analytics dataset.
Create a datasource pointing to the ECOMM_ANALYTICS database
Ingest metadata from dbt catalog:
Auto-generate expectations for dim_customers
Add custom expectations for metrics:
Execute validation for customer 360 expectation suite:
Here are some ways we can build on this ETL monitoring framework:
In this post, we walked through a detailed example of integrating dbt and Great Expectations for an e-commerce pipeline using code snippets. Defining rigorous data quality tests is key to identifying issues early and maintaining high quality datasets.
Hopefully this gives you a blueprint to implementing automated data testing and documentation for your own data transformation pipelines!
In this blog, we explore the intricate challenges faced during mobile app testing and pragmatic strategies to surmount them. We delve into each aspect that complicates mobile app testing from device and OS diversity to security concerns and user experience optimization.
Discover how to use pprof, Go's profiling tool, to tackle blocking issues in Go applications. This guide outlines steps to pinpoint and remedy performance bottlenecks stemming from goroutine synchronization and shared memory access.
Learn how to use Nginx to host both backend services and Single Page Applications (SPAs) on a single server. This guide covers the setup of Nginx configuration files, utilizing the sites-available and sites-enabled directories for better organization, and managing server configurations for different domains.