union

Pros of dbt-core

• More mature and widely adopted project with a larger community
• Extensive documentation and learning resources available
• Robust ecosystem of integrations and plugins

Cons of dbt-core

• Steeper learning curve for beginners
• More complex setup and configuration process
• Limited support for real-time data processing

Code Comparison

dbt-core:

models:
  - name: my_model
    columns:
      - name: id
        tests:
          - unique
          - not_null

Union:

@model
def my_model():
    return """
    SELECT id
    FROM source_table
    WHERE condition = true
    """

Union takes a more Python-centric approach, allowing users to define models using Python functions, while dbt-core uses YAML files for configuration and SQL for model definitions. This difference in approach may appeal to different types of users based on their preferred programming languages and workflow.

Pros of Airflow

• Mature and widely adopted project with a large community and extensive documentation
• Supports a wide range of integrations and operators out-of-the-box
• Offers a rich web UI for monitoring and managing workflows

Cons of Airflow

• Can be complex to set up and configure, especially for smaller projects
• Requires significant resources to run, which may be overkill for simpler workflows
• Learning curve can be steep for new users due to its extensive feature set

Code Comparison

Airflow DAG definition:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def my_task():
    print("Hello, Airflow!")

dag = DAG('example_dag', start_date=datetime(2023, 1, 1), schedule_interval='@daily')
task = PythonOperator(task_id='my_task', python_callable=my_task, dag=dag)

Union workflow definition:

from union import Workflow

def my_task():
    print("Hello, Union!")

workflow = Workflow("example_workflow")
workflow.add_task(my_task)

The code comparison shows that Union offers a simpler, more concise syntax for defining workflows, which may be preferable for smaller projects or those new to workflow orchestration. Airflow's syntax is more verbose but provides greater flexibility and control over task execution and scheduling.

Pros of Prefect

• More mature and established project with a larger community and ecosystem
• Offers both open-source and cloud-hosted options for flexibility
• Extensive documentation and tutorials for easier onboarding

Cons of Prefect

• Can be more complex to set up and configure for simple workflows
• Steeper learning curve for beginners compared to Union
• Heavier resource usage for small-scale projects

Code Comparison

Prefect workflow example:

from prefect import task, Flow

@task
def add(x, y):
    return x + y

with Flow("Math") as flow:
    result = add(1, 2)

flow.run()

Union workflow example:

from union import task, run

@task
def add(x, y):
    return x + y

result = run(add(1, 2))

Union aims for simplicity and ease of use, while Prefect offers more advanced features and flexibility. The choice between the two depends on the specific requirements of your project and team's expertise.

Pros of Dagster

• More mature and established project with a larger community and ecosystem
• Comprehensive documentation and extensive tutorials
• Built-in support for data quality testing and observability

Cons of Dagster

• Steeper learning curve due to its extensive feature set
• Can be overkill for simpler data pipelines or smaller projects
• Requires more setup and configuration compared to Union

Code Comparison

Dagster:

@op
def hello_world():
    return "Hello, World!"

@job
def my_job():
    hello_world()

Union:

from union import task

@task()
def hello_world():
    return "Hello, World!"

Union offers a simpler syntax for defining tasks, while Dagster provides a more structured approach with separate decorators for operations and jobs. Dagster's code structure allows for more complex pipeline definitions, but Union's simplicity may be preferable for straightforward tasks.

Both frameworks aim to simplify data pipeline management, but Dagster offers a more comprehensive solution with advanced features like data lineage and asset materialization. Union, being newer, focuses on simplicity and ease of use, making it potentially more accessible for developers new to data orchestration.

Pros of Meltano

• More mature project with a larger community and contributor base
• Extensive documentation and tutorials for easier onboarding
• Supports a wider range of data sources and destinations out-of-the-box

Cons of Meltano

• Steeper learning curve due to more complex architecture
• Heavier resource requirements for deployment and operation
• Less flexible for custom integrations compared to Union's modular approach

Code Comparison

Meltano configuration (meltano.yml):

plugins:
  extractors:
    - name: tap-github
      variant: meltanolabs
  loaders:
    - name: target-snowflake
      variant: meltanolabs

Union configuration (union.yaml):

sources:
  - name: github
    type: github
destinations:
  - name: snowflake
    type: snowflake

While both frameworks use YAML for configuration, Union's approach is more concise and straightforward. Meltano's configuration offers more granular control but requires more detailed setup. Union's modular design allows for easier customization of sources and destinations, while Meltano provides a more structured plugin ecosystem.

Pros of Great Expectations

• More mature and established project with a larger community and extensive documentation
• Supports a wider range of data sources and integrations
• Offers a suite of pre-built expectations and a flexible framework for custom expectations

Cons of Great Expectations

• Steeper learning curve due to its extensive feature set
• Can be resource-intensive for large datasets
• Configuration and setup process can be complex for beginners

Code Comparison

Great Expectations:

import great_expectations as ge

df = ge.read_csv("data.csv")
expectation_suite = df.expect_column_values_to_be_between(
    "age", min_value=0, max_value=120
)
validation_result = df.validate(expectation_suite=expectation_suite)

Union:

from union import expect

df = pd.read_csv("data.csv")
expect(df.age).to_be_between(0, 120)

Union offers a more concise and intuitive syntax for defining expectations, while Great Expectations provides a more comprehensive approach with additional configuration options.