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Going from dbt models to pandas DataFrames
How-to

Going from dbt models to pandas DataFrames

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
Mar 18, 2022 2 min read
Populate dbt models with CSV data
How-to

Populate dbt models with CSV data

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.
Mar 4, 2022 3 min read
What’s new in fal 0.2.0

What’s new in fal 0.2.0

We’re constantly working on improving fal for our users by looking into issues and feature requests. You can create a Github issue [https://github.com/fal-ai/fal/issues], if you also would like something in fal to be improved. Today we are releasing a new version of fal with
Feb 17, 2022 3 min read
The Unbundling of Airflow
Featured

The Unbundling of Airflow

Star us on GitHub In each subdomain of software, we have seen the same story over and over again. Products start small, in time, add adjacent verticals and functionality to their offerings and end up becoming a platform. Once these platforms become big enough, people start to figure out how
Feb 15, 2022 4 min read
Image of fal and dbt logos on a cloud
How-to

How to run Python with dbt Cloud using Github Actions and dbt Cloud API

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:
Jan 26, 2022 6 min read
How to run sentiment analysis on your dbt models from Python
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.
Dec 17, 2021 4 min read
How to detect data anomalies in your dbt models
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
Dec 13, 2021 10 min read
How to run forecasts on dbt models

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
Dec 7, 2021 3 min read
How to integrate dbt with Slack
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
Dec 1, 2021 3 min read
Introducing fal: do more with dbt

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
Nov 29, 2021 1 min read
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