Pros of Superset

• More mature and widely adopted project with a larger community and ecosystem
• Offers a broader range of visualization types and chart options
• Provides more advanced features for data exploration and dashboard creation

Cons of Superset

• Steeper learning curve and more complex setup process
• Requires more system resources and may be overkill for simpler use cases
• Less focused on collaborative query editing and sharing compared to Querybook

Code Comparison

Superset (Python):

from superset import db
from superset.models.slice import Slice

slice = Slice(
    slice_name="My Chart",
    datasource_type="table",
    datasource_id=1,
    viz_type="bar",
    params="{}"
)
db.session.add(slice)
db.session.commit()

Querybook (TypeScript):

import { QueryExecutionAPI } from 'lib/api/QueryExecutionAPI';

const executeQuery = async (queryId: number) => {
  const result = await QueryExecutionAPI.executeQuery(queryId);
  return result.data;
};

The code snippets demonstrate different aspects of each project. Superset's example shows creating a chart using its data model, while Querybook's example focuses on executing a query through its API.

Pros of Metabase

• More mature and widely adopted project with a larger community
• Offers a user-friendly interface for non-technical users to create visualizations
• Supports a wider range of databases and data sources out-of-the-box

Cons of Metabase

• Less focused on collaborative query editing and version control
• May be more resource-intensive for large-scale deployments
• Limited customization options for advanced users compared to Querybook

Code Comparison

Metabase (Clojure):

(defn run-query
  [query]
  (let [database (db/select-one Database :id (:database query))
        driver   (driver/database-id->driver (:id database))]
    (driver/execute-query driver query)))

Querybook (Python):

def run_query(query, engine):
    with engine.connect() as connection:
        result = connection.execute(query)
        return result.fetchall()

Both projects handle query execution, but Metabase's implementation is more abstracted and supports multiple database drivers, while Querybook's approach is more straightforward and relies on SQLAlchemy for database connections.

Pros of Redash

• More mature project with a larger community and wider adoption
• Supports a broader range of data sources out-of-the-box
• Offers a more polished and user-friendly interface for non-technical users

Cons of Redash

• Less focus on collaboration features compared to Querybook
• May require more setup and configuration for complex environments
• Limited built-in version control for queries

Code Comparison

Redash query execution:

query_runner = get_query_runner(data_source.type, data_source.options)
data, error = query_runner.run_query(query, user)

Querybook query execution:

engine = get_query_engine(data_source)
result = engine.execute_query(query, user)

Both projects use similar approaches for query execution, with slight differences in naming conventions and method signatures. Redash's implementation appears more straightforward, while Querybook's may offer more flexibility in terms of engine customization.

Pros of Zeppelin

• More mature project with a larger community and ecosystem
• Supports a wider range of programming languages and data processing frameworks
• Offers built-in visualization tools for data exploration

Cons of Zeppelin

• Heavier resource requirements and more complex setup process
• Steeper learning curve for new users
• Less focus on collaborative features compared to Querybook

Code Comparison

Zeppelin notebook cell (Python):

%python
import pandas as pd
df = pd.read_csv('data.csv')
df.head()

Querybook query cell (SQL):

SELECT *
FROM my_table
LIMIT 5;

While both tools support various languages, Zeppelin uses a % syntax to specify the interpreter, whereas Querybook is primarily focused on SQL queries. Zeppelin's notebooks can include multiple languages and visualization blocks, while Querybook is more streamlined for SQL-based data exploration and collaboration.

Zeppelin offers a more comprehensive data science platform with support for multiple languages and built-in visualizations. Querybook, on the other hand, provides a more focused and user-friendly experience for SQL-based data exploration and collaboration within organizations.

Pros of Falcon

• Built with React and TypeScript, offering a modern and type-safe frontend development experience
• Focuses on interactive data visualization and dashboarding capabilities
• Provides a more customizable and extensible architecture for building data applications

Cons of Falcon

• Less emphasis on collaborative features and team-oriented workflows
• May require more setup and configuration compared to Querybook's out-of-the-box solution
• Smaller community and fewer resources available for support and troubleshooting

Code Comparison

Querybook (Python):

from querybook.models import DataDoc

data_doc = DataDoc.create(
    title="My Data Document",
    owner_uid=user.id,
    environment_id=environment.id
)

Falcon (TypeScript):

import { Dashboard } from '@plotly/falcon';

const dashboard = new Dashboard({
  title: 'My Dashboard',
  layout: 'grid',
  items: [
    { type: 'chart', query: 'SELECT * FROM users' }
  ]
});

Both repositories offer solutions for data exploration and analysis, but they cater to different use cases. Querybook is more focused on collaborative SQL editing and execution, while Falcon emphasizes interactive data visualization and dashboard creation. The choice between the two depends on specific project requirements and team preferences.