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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs dbt

Airflow vs dbt

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
dbt
dbt
Stacks518
Followers461
Votes16

Airflow vs dbt: What are the differences?

Introduction

This post compares Airflow and dbt and highlights the key differences between the two tools.

  1. Scalability: Airflow is a workflow orchestration tool that allows the scheduling and execution of complex workflows, making it highly scalable. On the other hand, dbt is a data transformation tool that focuses on building data transformations for analytics purposes. While dbt can handle large datasets, it is not designed for scaling to the same extent as Airflow.

  2. Flexibility: Airflow provides a flexible platform for building custom workflows using Python, allowing users to create complex pipelines with ease. Additionally, it supports different types of tasks and operators, making it highly versatile. In contrast, dbt is primarily focused on transforming data stored in a database and is less flexible when it comes to building custom workflows.

  3. Architecture: Airflow follows a distributed architecture that enables high availability and fault tolerance. It uses a central scheduler and executor model, allowing multiple workers to execute tasks concurrently. In contrast, dbt follows a more simplistic architecture, with transformations executed in a linear fashion.

  4. Monitoring and Alerting: Airflow provides built-in monitoring and alerting capabilities, allowing users to track the progress of their workflows and receive notifications when issues occur. These features enable better visibility and proactive management of workflows. On the other hand, dbt does not have native monitoring and alerting functionalities, requiring users to rely on external tools to achieve similar capabilities.

  5. Community and Ecosystem: Airflow has a large and active community, with a rich ecosystem of plugins and integrations that extend its functionality. This makes it easy to find support, share knowledge, and leverage existing solutions. While dbt also has a growing community, it may not offer the same breadth of resources and integrations as Airflow.

  6. Purpose: Airflow is primarily focused on orchestrating and scheduling workflows, allowing users to define dependencies and manage complex pipelines. It is widely used in data engineering and data warehousing scenarios. On the other hand, dbt focuses on transforming and modeling data specifically for analytics purposes, providing a cleaner way to manage data transformation pipelines for business intelligence.

In Summary, Airflow is a scalable and flexible workflow orchestration tool with a distributed architecture, monitoring capabilities, and a strong community, while dbt is a data transformation tool with a simpler architecture, primarily focused on analytics data transformations.

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Advice on Airflow, dbt

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Comments

Detailed Comparison

Airflow
Airflow
dbt
dbt

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Code compiler; Package management; Seed file loader; Data snapshots; Understand raw data sources; Tests; Documentation; CI/CD
Statistics
Stacks
1.7K
Stacks
518
Followers
2.8K
Followers
461
Votes
128
Votes
16
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Observability is not great when the DAGs exceed 250
  • 1
    Logical separation of DAGs is not straight forward
Pros
  • 5
    Easy for SQL programmers to learn
  • 3
    Reusable Macro
  • 2
    Modularity, portability, CI/CD, and documentation
  • 2
    CI/CD
  • 2
    Schedule Jobs
Cons
  • 1
    Only limited to SQL
  • 1
    People will have have only sql skill set at the end
  • 1
    Cant do complex iterations , list comprehensions etc .
  • 1
    Very bad for people from learning perspective
Integrations
No integrations available
Exasol
Exasol
Snowflake
Snowflake
Materialize
Materialize
Presto
Presto
Amazon Redshift
Amazon Redshift
Google BigQuery
Google BigQuery
PostgreSQL
PostgreSQL
Apache Spark
Apache Spark
Dremio
Dremio
Databricks
Databricks

What are some alternatives to Airflow, dbt?

dbForge Studio for MySQL

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Liquibase

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

dbForge SQL Complete

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

Knex.js

Knex.js

Knex.js is a "batteries included" SQL query builder for Postgres, MySQL, MariaDB, SQLite3, and Oracle designed to be flexible, portable, and fun to use. It features both traditional node style callbacks as well as a promise interface for cleaner async flow control, a stream interface, full featured query and schema builders, transaction support (with savepoints), connection pooling and standardized responses between different query clients and dialects.

Flyway

Flyway

It lets you regain control of your database migrations with pleasure and plain sql. Solves only one problem and solves it well. It migrates your database, so you don't have to worry about it anymore.

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