Meder Kamalov

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.

You can now use fal to easily specify and trigger sync jobs on pipelines from both Fivetran and Airbyte.

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.

dbt 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 which let us run Python

Google 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, including keeping track of inventories, user sessions and state mutations. Firestore is fast and enable low-latency querying of large quantities of data.

Using Slack bots can be a great time saver. They can enhance whatever workflow they are a part of, be it project management, version control or simple content sharing. As we bring our dbt projects to production, Slack bots can help us stay on top of dbt runs and models.