Matteo Ferrando - fal Blog

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Matteo Ferrando

dbt-fal v1.3.12 – Finding the cause of a subtle bug

dbt-fal v1.3.12 – Finding the cause of a subtle bug

We accidentally slowed down dbt seed for everyone using the dbt-fal adapter and now its fast again.

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v0.7 — Playing nice with dbt Python models

dbt recently introduced native Python models support! At fal, we believe this should be the way to run your Python models, so we have been working on dbt-fal, an adapter that enables you to run dbt Python models with any datawarehouse (including Redshift, Postgres). dbt-fal can be used to run

v0.5 — Adapter-specific dependencies, hooks with arguments, and --full-refresh support

v0.5 — Adapter-specific dependencies, hooks with arguments, and --full-refresh support

Make the fal package lighter by installing only the dependencies needed for your adapter.

v0.4 — moving forward

v0.4 — moving forward

The focus of this release is to make our experimental features generally available, simplifying fal flow run.

Introducing fal Python Models

Introducing fal Python Models

fal enables you to run Python with your dbt project. Today we are enabling Python-only models anywhere in your dbt project: Python Data Models.

Parallelizing Python Workflows in fal

Parallelizing Python Workflows in fal

Today's release introduces parallelization of Python scripts in your fal runs. During a run, fal creates a directed acyclic graph (DAG) of links between models and scripts. The number of threads represents the maximum number of tasks fal handles simultaneously. When fal is running, it will read the configuration for

How to run sentiment analysis on your dbt models from Python

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.