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 Run Airbyte and Fivetran from your dbt project using fal You can now use fal to easily specify and trigger sync jobs on pipelines from both Fivetran and Airbyte.
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 How to reverse ETL a dbt model into Google Firestore Google Firestore [https://cloud.google.com/firestore/] is a cloud-hosted NoSQL database that can be directly accessed by mobile and web applications through native SDKs. It has a rich set of use cases [https://cloud.google.com/architecture/building-scalable-apps-with-cloud-firestore#when_to_use_firestore] , including keeping track of inventories, user sessions
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
