How-to How to run sentiment analysis on your dbt models from Python 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.
How-to How to detect data anomalies in your dbt models 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
How to run forecasts on dbt models Transforming and validating data is very easy with dbt. We can build complex models that span multiple dependencies. We can also create macros that can work across a dbt project and make data pipelines easy to maintain. Sometimes we want to do more with our dbt models, like for example
How-to How to integrate dbt with Slack 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
Introducing fal: do more with dbt At Features & Labels [https://fal.ai], dbt [https://www.getdbt.com/] is one of our favorite tools to use while managing our data pipelines. It is a category defining tool (see Analytics Engineering [https://www.getdbt.com/what-is-analytics-engineering/]) that enables us to build data transformations without having to write boilerplate
