Pros of Jupyter Notebook

• More widely adopted and supported by the data science community
• Extensive ecosystem of extensions and integrations
• Easier setup and installation process for beginners

Cons of Jupyter Notebook

• Limited support for multiple programming languages within a single notebook
• Less robust collaboration features compared to Zeppelin
• Lack of built-in data visualization tools

Code Comparison

Jupyter Notebook:

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.show()

Zeppelin:

%python
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
z.show(plt)

Both Jupyter Notebook and Zeppelin offer interactive environments for data analysis and visualization. Jupyter Notebook is more popular among data scientists and has a larger ecosystem, while Zeppelin provides better support for multiple languages and built-in visualization tools. The code examples demonstrate how to create a simple plot in both environments, with minor differences in syntax and display methods.

Pros of nteract

• More modern, React-based UI with a cleaner, more intuitive interface
• Better support for interactive widgets and rich media outputs
• Stronger focus on desktop and local development environments

Cons of nteract

• Less extensive language support compared to Zeppelin
• Fewer built-in data visualization options
• Smaller community and ecosystem of extensions/plugins

Code Comparison

nteract (JavaScript/React):

import { ContentRef } from "@nteract/types";
import { actions } from "@nteract/core";

export const loadNotebook = (contentRef: ContentRef) =>
  actions.fetchContent({ contentRef, filepath: "/path/to/notebook.ipynb" });

Zeppelin (Java):

NotebookAuthorization notebookAuthorization = NotebookAuthorization.getInstance();
AuthenticationInfo authInfo = new AuthenticationInfo(userName);
Notebook notebook = notebookRepo.get(noteId, authInfo);
notebook.runAll(authInfo, true);

Both projects aim to provide interactive notebook experiences, but nteract focuses more on modern web technologies and local development, while Zeppelin offers broader language support and is often used in big data environments. nteract's code tends to be more frontend-oriented, while Zeppelin has a stronger backend focus.

Pros of Redash

• More focused on data visualization and dashboarding
• Easier setup and configuration for non-technical users
• Better support for SQL databases and cloud data warehouses

Cons of Redash

• Less support for big data processing and distributed computing
• Fewer built-in interpreters for various programming languages
• More limited in terms of advanced data analysis capabilities

Code Comparison

Redash (Python):

from redash import create_app
from redash.cli import manager

app = create_app()
manager.app = app

Zeppelin (Java):

import org.apache.zeppelin.interpreter.Interpreter;
import org.apache.zeppelin.interpreter.InterpreterContext;

public class CustomInterpreter extends Interpreter {
    @Override
    public InterpreterResult interpret(String st, InterpreterContext context) {
        // Custom interpretation logic
    }
}

Summary

Redash is more suitable for users who primarily work with SQL databases and need quick, easy-to-setup dashboards. Zeppelin offers a more comprehensive environment for data processing and analysis, especially for big data scenarios. Redash's codebase is primarily Python-based, while Zeppelin uses Java, reflecting their different focuses and capabilities.

Pros of Superset

• More focused on data visualization and exploration, with a wider range of chart types and interactive features
• Easier to set up and use for non-technical users, with a more intuitive user interface
• Better support for modern data sources and SQL databases

Cons of Superset

• Less flexible for custom code execution and data processing compared to Zeppelin's notebook-style interface
• More limited support for big data processing frameworks like Spark
• Steeper learning curve for advanced customizations and extensions

Code Comparison

Superset (Python):

from superset import db
from superset.models import Slice

slices = db.session.query(Slice).all()
for s in slices:
    print(f"Slice: {s.slice_name}, Chart Type: {s.viz_type}")

Zeppelin (Scala):

%spark
val df = spark.read.json("/path/to/data.json")
df.createOrReplaceTempView("mydata")
val result = spark.sql("SELECT * FROM mydata WHERE value > 100")
z.show(result)

The code snippets highlight the different approaches: Superset focuses on querying and visualizing existing data, while Zeppelin allows for more complex data processing and analysis within the notebook environment.

Pros of Metabase

• User-friendly interface with drag-and-drop query builder
• Easy setup and deployment, suitable for non-technical users
• Strong focus on data visualization and dashboarding capabilities

Cons of Metabase

• Limited support for complex data processing and ETL operations
• Less flexibility for custom code execution compared to Zeppelin
• Primarily SQL-focused, with limited support for other languages

Code Comparison

Metabase SQL query example:

SELECT product_name, SUM(sales_amount) AS total_sales
FROM sales
GROUP BY product_name
ORDER BY total_sales DESC
LIMIT 10

Zeppelin Spark SQL example:

%spark.sql
SELECT product_name, SUM(sales_amount) AS total_sales
FROM sales
GROUP BY product_name
ORDER BY total_sales DESC
LIMIT 10

Key Differences

• Zeppelin supports multiple interpreters (Spark, Python, R, etc.), while Metabase is primarily SQL-focused
• Metabase offers a more polished, user-friendly interface for data exploration and visualization
• Zeppelin provides more flexibility for data processing and custom code execution
• Metabase is better suited for business users, while Zeppelin caters more to data scientists and engineers

Both tools have their strengths, with Metabase excelling in ease of use and quick data visualization, while Zeppelin offers more advanced data processing capabilities and support for multiple programming languages.

Pros of Grafana

• More focused on data visualization and dashboarding
• Wider range of data source integrations out-of-the-box
• More active community and frequent updates

Cons of Grafana

• Less suitable for data exploration and analysis
• Limited support for running custom code or queries

Code Comparison

Grafana (dashboard JSON configuration):

{
  "panels": [
    {
      "type": "graph",
      "title": "CPU Usage",
      "datasource": "Prometheus",
      "targets": [
        { "expr": "node_cpu_usage" }
      ]
    }
  ]
}

Zeppelin (notebook paragraph):

%spark
val df = spark.read.json("data.json")
df.groupBy("category").count().show()

Grafana excels in creating polished dashboards with various visualizations, while Zeppelin offers a more flexible environment for data analysis and exploration. Grafana's configuration is typically JSON-based, focusing on defining visualizations and data sources. Zeppelin uses a notebook-style interface where users can write and execute code in multiple languages. The choice between the two depends on whether the primary need is for dashboarding (Grafana) or interactive data analysis (Zeppelin).