We discuss a straightforward way to integrate your dbt project with SageMaker by using fal. We will train a SageMaker model, store model data in a dbt model, use a SageMaker model to make some test predictions and store prediction results in another dbt model.

One of our favorite features of dbt is the ability to build dependencies between data models using the ref function. With ref, you can build pipelines all in SQL, simply by referring to other tables in your select statements. But what if you want to refer to these models in

A common source of raw data in ELT pipelines are CSV files. These text files hold data in multiple lines with headers and need to be parsed and loaded into data warehouses, ideally in an automated process. One way to load CSV data is by using the dbt seed command.

dbt [https://www.getdbt.com/] is our favorite tool to build data pipelines. It allows us to skip boilerplate data engineering code, focus only on SQL and helps us build with software engineering practices like reusability, auto generating docs and tests. We recently introduced our open source project fal [https:

Sentiment analysis is the practice of determining whether a text / statement is positive, negative or neutral typically using natural language processing (NLP). Applying sentiment analysis on data such as reviews, tickets, feedback and survey responses can help you understand how your organization is doing in the eyes of your customers.

For time series data, anomalies are almost always present in some way or another. Be it a sudden change in rainfall for a town due to climate change or, in our example here, a sudden upwards trend in daily Covid-19 cases. Any system or business that runs on time series

Using Slack bots can be a great time saver. They can enhance whatever workflow they are a part of, be it project management [https://slack.com/apps/A074YH40Z-trello?tab=more_info], version control [https://slack.github.com/] or simple content sharing [https://slack.com/apps/A0F827J2C-giphy]. As we bring our