Differences between dbt Core and dbt Cloud

Ansam Yousry — Thu, 22 Jun 2023 19:19:15 +0000

dbt (Data Build Tool) is an open-source tool that enables data analysts and engineers to build data transformation pipelines. There are two main versions of dbt: dbt Core and dbt Cloud.

In this article, we'll discuss the differences between dbt Core and dbt Cloud. For more detailed information, check out this medium post.

dbt Core

dbt Core is the open-source version of dbt that can be installed and run locally. It is a command-line tool that allows you to write SQL code to transform your data. dbt Core has the following features:

Can be run on your local machine or on a server.
Allows you to write SQL code to transform your data.
Integrates with popular data warehouses such as Snowflake, BigQuery, and Redshift.
Supports version control using Git.
Provides testing functionality to ensure the quality and accuracy of your data.
Allows you to document your data pipelines using Markdown.

dbt Core is a powerful tool that provides a lot of functionality for transforming your data but requires some setup and configuration.

dbt Cloud

dbt Cloud is a cloud-based platform that provides a user-friendly interface for building and managing data transformation pipelines. It is a paid service that provides additional features on top of dbt Core. dbt Cloud has the following features:

Provides a web-based interface for building and managing data pipelines.
Automates the deployment and running of your dbt projects.
Provides a collaborative environment for teams to work on data pipelines together.
Includes a built-in scheduler for running data pipelines on a schedule.
Provides additional functionality for monitoring and alerting on data pipelines.

dbt Cloud is a powerful tool that provides a lot of functionality for building and managing data pipelines without requiring a lot of setup and configuration. It is especially useful for teams who want to collaborate on data pipelines and don't want to manage their own infrastructure.

Conclusion

dbt is a powerful tool for building and managing data transformation pipelines. dbt Core is the open-source version that can be installed locally, while dbt Cloud is a cloud-based platform that provides additional features on top of dbt Core. Which version you choose depends on your needs and preferences. If you're just getting started with dbt, dbt Core is a great place to start. If you're working on a team or need additional functionality, dbt Cloud may be a better fit.

Apache Airflow: A Beginner's Guide

Ansam Yousry — Thu, 22 Jun 2023 18:50:23 +0000

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It was developed by Airbnb to manage their data pipelines. The platform is used to create complex workflows composed of multiple tasks, and it allows for easy maintenance and monitoring of these workflows.

In this article, we'll cover the basics of Apache Airflow and how to install it using Docker. For more detailed instructions, check out this medium post.

What is Apache Airflow?

Apache Airflow is a platform that allows you to create, schedule, and monitor workflows. Workflows are created using Python code and are made up of multiple tasks that are executed independently. Each task can be run on a different machine, making it possible to scale workflows as needed.

Airflow has a web interface that allows you to monitor the status of your workflows, view logs, and see the dependencies between tasks. It also has a powerful scheduler that can handle complex dependencies and retry failed tasks.

Installing Apache Airflow with Docker

Installing Apache Airflow can be a complex process, but using Docker can make it much simpler. Docker is a platform that allows you to create, deploy, and run applications in containers. Containers are lightweight, portable, and self-contained, making them ideal for running complex applications like Apache Airflow.

To install Apache Airflow with Docker, follow these steps:

Install Docker on your machine. You can download Docker from the official website.
Create a directory for your Airflow installation. This directory will contain your Airflow configuration files and your Docker Compose file.
Create a Docker Compose file in your Airflow directory. This file will define the services that make up your Airflow installation. Here's an example:

`version: '2'

services:
postgres:
image: postgres:9.6
environment:
POSTGRES_USER: airflow
POSTGRES_PASSWORD: airflow
POSTGRES_DB: airflow
volumes:
- postgres_data:/var/lib/postgresql/data

webserver:
image: apache/airflow:2.1.2
depends_on:
- postgres
environment:
- LOAD_EX=n
- FERNET_KEY=your_fernet_key_here
- EXECUTOR=Local
- POSTGRES_USER=airflow
- POSTGRES_PASSWORD=airflow
- POSTGRES_DB=airflow
ports:
- "8080:8080"
volumes:
- ./dags:/opt/airflow/dags
- ./logs:/opt/airflow/logs
- ./plugins:/opt/airflow/plugins
command: webserver

volumes:
postgres_data:`

Start your Airflow installation by running docker-compose up in your Airflow directory. This will start the Postgres database and the Airflow webserver.
Access the Airflow web interface by navigating to http://localhost:8080 in your web browser. You should see the Airflow dashboard, which shows the status of your workflows.

Conclusion

Apache Airflow is a powerful platform for managing complex workflows. Using Docker to install Airflow can make the process much simpler and more manageable. With Airflow, you can create, schedule, and monitor your workflows with ease, allowing you to focus on your data instead of worrying about the infrastructure.

DEV Community: Ansam Yousry

Differences between dbt Core and dbt Cloud

dbt Core

dbt Cloud

Conclusion

Apache Airflow: A Beginner's Guide

What is Apache Airflow?

Installing Apache Airflow with Docker

Conclusion