Build and Deploy Machine Learning Models from Jupyter Notebooks with fal and dbt Introduction Machine Learning (ML) is increasingly important in data-driven decision making, so it's important to use modern tools and techniques to streamline the machine learning workflows. This is where dbt and fal can come in - together they make it easy to manage and deploy machine learning models in a
Populate dbt models with CSV data. Part 2: the power of dbt-fal In this blog post, we will explore how dbt-fal and dbt can be combined to create a streamlined and efficient process for loading CSV data into a data warehouse, even when dealing with large datasets.
fal and dbt-fal: a magic combination When should you use fal vs dbt-fal? If your Python script writes back to the data warehouse, you should use dbt-fal.
Notebooks as fal scripts and more Today we're presenting more new features that our users might like: Jupyter notebooks as fal scripts, execute_sql magic function, dbt source test results.
Introducing Post Hooks fal post hooks let you define Python scripts that don't need to write to the data warehouse.
Supporting Athena and DuckDB We’re constantly working on making it as easy as possible to integrate fal into our users’ projects. So far we have supported PostgreSQL, Google BigQuery, Snowflake and AWS Redshift. Today we’re adding two more: AWS Athena and DuckDB. Star us on GitHub [https://github.com/fal-ai/fal]Amazon
How-to Use fal to integrate SageMaker with dbt 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.
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.
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
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:
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
