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Summary
Key takeaways
- The article ranks eight Python IDEs and editors for 2026 using a scoring model across debugging, AI integration, data science support, large-codebase performance, beginner onboarding, and cost.
- VS Code with GitHub Copilot is presented as the best overall choice for most Python developers because it performs well across all major categories without serious weaknesses.
- PyCharm Pro is positioned as the best option for professional Python back-end work, especially for Django and FastAPI projects where deep debugging and framework-aware tooling matter.
- Cursor is described as the strongest AI-native environment because it is built around AI workflows and multi-file edits, not just plugin-based assistance.
- JupyterLab remains the leading notebook-first environment for data science, research, and model prototyping, but it is not recommended as a full replacement for a production IDE.
- Spyder is treated as a solid option for scientific Python and analysis without notebooks, though its lack of AI assistant support is a major weakness in 2026.
- Vim or Neovim with LSP is recognized as a serious professional setup for terminal-focused developers, but only for people already invested in that workflow.
- Thonny is presented as the best first IDE for complete beginners because it is easy to install, simple to understand, and designed for learning.
- IDLE is framed as suitable only for the very first steps, after which users should move to a more capable tool quickly.
- A central idea in the article is that choosing a Python IDE in 2026 is no longer just about debugging or extensions. AI workflow compatibility is now one of the main decision factors.
When this applies
This applies when a developer, team lead, CTO, or hiring manager is choosing a Python development environment and wants to match the tool to a real workflow rather than just picking the most famous editor. It is especially useful when the decision depends on AI assistant support, Django or FastAPI development, notebook-based data science, mixed-language projects, onboarding beginners, or handling large codebases. It also applies when a team wants to standardize on one main environment while still understanding where specialized tools fit better.
When this does not apply
This does not apply as directly when the organization has already standardized on a specific IDE and the decision is no longer open. It is also less useful when the main need is a deep technical benchmark of performance, plugin internals, enterprise licensing, or hardware-level resource usage. If the choice is based mostly on personal preference and long-established habits, the article may be informative, but it will not fully resolve that decision because it is organized around workflow fit rather than personal taste.
Checklist
- Define the main Python workflow you need to support.
- Decide whether AI assistant integration is essential or optional.
- Check whether the team mainly builds Django or FastAPI back ends.
- If yes, compare PyCharm Pro and VS Code more closely than the other tools.
- Decide whether the work is notebook-first, production-code-first, or mixed.
- If notebook work is central, evaluate JupyterLab or VS Code with notebook support.
- Check whether the team works across Python, SQL, YAML, Docker, and Markdown in the same projects.
- If yes, prioritize tools that handle mixed stacks well, especially VS Code.
- Review whether AI-agent workflows and multi-file editing would create real value.
- If yes, compare Cursor’s benefits against its higher cost and steeper expectations.
- Consider how quickly new team members need to be onboarded.
- Check performance expectations for larger repositories and legacy codebases.
- Separate beginner learning needs from professional development needs.
- Compare total tooling cost, including paid AI subscriptions and professional IDE tiers.
- Choose the environment based on real workflow fit, not popularity alone.
Common pitfalls
- Choosing an IDE based only on habit and ignoring how AI integration now affects productivity.
- Treating JupyterLab as a full replacement for a production-oriented Python IDE.
- Picking Cursor only because it is AI-native, without checking whether the team actually needs agent-style workflows.
- Defaulting to PyCharm for every Python team, even when the work is strongly polyglot and may fit VS Code better.
- Ignoring the total cost difference between free tools plus AI add-ons and more expensive AI-first environments.
- Recommending Vim or Neovim to beginners or mixed-experience teams that need fast onboarding.
- Choosing Spyder for long-term work without considering its lack of meaningful AI support.
- Keeping beginners in IDLE too long instead of moving them to a more capable environment.
- Mixing up notebook experimentation, data engineering, back-end development, and large-scale software engineering as if they need the same tool.
- Looking for a universal best IDE instead of selecting the best one for a specific Python use case.
Methodology: Each IDE is scored 1–5 across six dimensions by the Uvik Software engineering team based on current product documentation, official pricing, and hands-on evaluation. Scoring criteria are defined below the decision matrix. All pricing reflects April 2026 figures.
The Python IDE you choose in 2026 is no longer just a function of debugger quality or Git integration. It is also a function of where AI coding assistants work best—and the answer is not the same across tools or workflows.
This ranking is built on real-world Python workflows: back-end development with FastAPI and Django, data engineering with Airflow, dbt, and Spark, AI/ML development with PyTorch and LangChain, and beginner onboarding. It combines classic IDE quality scores with an AI-era compatibility layer that most existing comparisons still lack.
Quick Decision Matrix: 8 Python IDEs Scored
| IDE / Editor | Python Debug | AI Depth | Data Sci | Large Code | Beginner | Cost | Total (/30) |
| VS Code + Copilot | 4 | 4 | 4 | 4 | 4 | 4 | 24 |
| PyCharm Pro | 5 | 3 | 4 | 5 | 3 | 3 | 23 |
| Cursor | 3 | 5 | 2 | 4 | 2 | 3 | 19 |
| JupyterLab | 2 | 2 | 5 | 2 | 3 | 5 | 19 |
| Spyder | 3 | 1 | 4 | 2 | 4 | 5 | 19 |
| Vim/Neovim + LSP | 3 | 3 | 1 | 5 | 1 | 5 | 18 |
| Thonny | 3 | 1 | 1 | 1 | 5 | 5 | 16 |
| IDLE | 2 | 1 | 1 | 1 | 4 | 5 | 14 |
Scoring methodology: Python debugging reflects the quality of the native debugger, including breakpoints, variable inspection, and framework-aware stepping. AI integration depth measures how natively and deeply AI assistance is built into the workflow, not merely whether a plugin exists. Notebook/data science reflects out-of-the-box support for notebooks, variable explorers, and data visualization. Performance on large codebases measures indexing speed, memory usage, and responsiveness on repositories above 50k lines. Beginner onboarding reflects zero-config setup, clarity of error messages, and educational affordances. Cost is scored relative to developer context: 5 = free and open-source; 1 = significant paid subscription required.
Why VS Code + Copilot leads: It is the most-used Python IDE in the community and integrates natively with the most-adopted AI coding assistant. The combination scores no lower than 4 in any dimension relevant to a working Python developer.
Why PyCharm scores highest on debugging: Its debugger is Python-native with no extension configuration, supports Django server processes and pytest integration, and outperforms debugpy in complex framework-aware scenarios.
Why Cursor scores 5 for AI but lower elsewhere: Cursor rebuilt the editor around AI rather than adding it as a plugin. Its Composer mode performs multi-file edits from a single natural-language prompt. However, notebook support is limited relative to VS Code or JupyterLab, and it is not suited for beginners.
If You Only Want the Short Answer
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General-purpose Python: VS Code + GitHub Copilot
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Professional back-end Python (FastAPI, Django): PyCharm Pro
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Notebook-first data science: JupyterLab
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AI-agent-driven development: Cursor
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Scientific Python/data analysis: Spyder
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Terminal-native, performance-first: Vim/Neovim + LSP
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Learning Python from scratch: Thonny
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Absolute zero-config start: IDLE (then move to VS Code within a week)
Individual IDE Deep Dives
VS Code + GitHub Copilot
Best for: General-purpose Python, back-end development, multi-language codebases, and teams.
The Microsoft Python extension provides IntelliSense via Pylance, integrated debugging via debugpy, environment management, and first-class Jupyter notebook support inside the editor. The extension marketplace offers deep support for FastAPI, Django, pytest, Docker, and Git with minimal configuration.
AI assistant compatibility: GitHub Copilot is the native AI layer—inline completions, multi-file edits via Copilot Coding Agent, and in-editor chat. Copilot Pro is $10/month. Windsurf, Tabnine, and Codeium also offer VS Code plugins. The AI integration is plugin-based rather than architecture-native (unlike Cursor), which means AI features depend on extension quality.
Data engineering: Handles mixed-file data engineering projects well—Python DAGs, YAML configs, SQL models, and Markdown all have strong extension support. Jupyter integration makes iterative work practical. Remote Development extensions support SSH-based Spark cluster work.
Downsides: Requires more manual configuration than PyCharm for Django or FastAPI debugging. Performance on very large monorepos can lag depending on extension load. AI features are additive rather than native to the architecture.
Uvik Software engineer verdict: The right default for most Python teams in 2026. It covers back-end, data engineering, and AI/ML workflows without forcing specialization, and its AI assistant costs are manageable at the team tier. For organizations looking to hire Python developers, VS Code is the stack they’ll ramp up fastest on.
PyCharm Professional
Best for: Professional Python development, large back-end codebases, Django and FastAPI projects, Python-focused teams.
PyCharm is a purpose-built Python IDE with the most comprehensive Python debugger in this comparison—supporting remote debugging, Django template debugging, pytest integration, and database-aware query inspection without plugin configuration. As of the 2025.1 release, PyCharm is one unified product: core features, including Jupyter Notebook support, are free, and Pro adds advanced features at approximately $249/year. The built-in database browser, HTTP client, and scientific mode make it self-contained.
AI assistant compatibility: JetBrains AI Assistant is bundled with PyCharm Pro (2025.1+), providing code completion, chat, and MCP support. GitHub Copilot and Tabnine have JetBrains plugins. Windsurf offers a JetBrains plugin for completions and chat, though its full agentic Cascade features require the native Windsurf Editor.
Data engineering: Strong database tools and SQL editor for Airflow/dbt workflows involving mixed Python + SQL. Remote interpreter support is useful for Spark-adjacent work. For heavy notebook-first workflows, JupyterLab or VS Code is more efficient.
Downsides: Higher cost at the Pro tier. Heavier on memory than VS Code or Neovim. AI features, while improving, are behind Cursor and VS Code + Copilot in agentic workflow depth. Not ideal for polyglot teams.
Uvik Software engineer verdict: The right choice for Python-specialized teams working on complex Django or FastAPI back ends where deep refactoring, framework-aware debugging, and type checking matter more than AI agent workflows.
JupyterLab
Best for: Data science exploration, ML model iteration, academic research, notebook-first workflows.
JupyterLab is the open-source, next-generation notebook interface and the standard platform for data scientists working with pandas, NumPy, Matplotlib, scikit-learn, and PyTorch. Its cell-based execution model makes iterative data exploration natural and persistent. JupyterLab extends the classic Notebook interface with a full file browser, terminal, text editor, and extension ecosystem.
AI assistant compatibility: GitHub Copilot has no official JupyterLab extension. Community projects like Notebook Intelligence (NBI) and Jupyter-Copilot bring Copilot-powered completions and chat to JupyterLab, but these are not maintained by GitHub. Windsurf offers an official codeium-jupyter extension with inline completions. Tabnine provides browser-based Jupyter support. The Jupyter AI extension is a separate official Jupyter project supporting multiple LLM providers (OpenAI, Anthropic, and others) but does not integrate directly with a Copilot subscription. For the most reliable AI-assisted notebook experience, VS Code’s Jupyter integration with Copilot remains the stronger path.
Data engineering: Strong for Pandas, NumPy, and exploratory PySpark work. Less useful for orchestration code (Airflow DAGs, dbt models), which belongs in a code editor. JupyterLab is the right tool for the exploration phase, not the pipeline development phase.
Downsides: No traditional debugger by default. AI assistant support is fragmented and less mature than in VS Code or PyCharm. Not suited for writing production Python modules. Poor performance on non-notebook files.
Uvik Software engineer verdict: Non-negotiable for data scientists doing exploration and model prototyping. It is not an IDE replacement—it is a specialized tool. Teams doing both exploration and production code should pair JupyterLab with VS Code or PyCharm. For data science consulting engagements requiring interactive notebooks, JupyterLab is the starting point.
Cursor
Best for: AI-driven development, complex multi-file Python projects, developers who want agent-mode coding workflows.
Cursor is a VS Code fork that rebuilt the editor architecture around AI. Its Composer mode allows developers to describe a task in natural language and have Cursor identify and modify every relevant file simultaneously. Background Agents (Pro+ and above) run multi-step tasks—implementing features, running tests, refactoring across the codebase—while the developer works on something else. Cursor Pro costs $20/month, Pro+ $60/month, and Ultra $200/month. Teams pricing is $40/user/month.
AI assistant compatibility: Cursor is its own AI stack—accessing GPT-4o, Claude, and Gemini models directly with Supermaven-based autocomplete. It is not a Copilot wrapper. You cannot install Copilot inside Cursor.
Data engineering: Codebase indexing and retrieval-augmented context generation make it strong for large, complex projects. For data engineering involving mixed Python + SQL + YAML, agent mode accelerates development. Notebook support is limited compared to JupyterLab or VS Code.
Downsides: More expensive than VS Code + Copilot for individuals and significantly more for teams ($40/user vs $19/user for Copilot Business). Not suitable for beginners. Heavy agent use can drain the monthly credit pool quickly.
Uvik Software engineer verdict: The right choice for experienced Python engineers on complex, multi-file projects where AI agent workflows deliver measurable velocity gains. Not the right IDE for beginner onboarding or notebook-heavy data science.
Spyder
Best for: Scientific computing, data analysis, Python-centric data science without notebooks.
Spyder is open-source, free, and designed for scientists, engineers, and data analysts working in Python. Its Variable Explorer, Plots pane, and built-in Profiler make it the closest thing to MATLAB’s workflow in Python. It integrates directly with Anaconda and the scientific Python stack (NumPy, pandas, Matplotlib, SciPy). The IPython console supports multi-line interactive execution.
AI assistant compatibility: Spyder has no native AI assistant integration and no well-supported AI plugin ecosystem. This is its most significant weakness relative to VS Code or PyCharm in 2026.
Downsides: No AI assistant support. Not suited for software engineering work, web frameworks, or multi-language codebases. The extension ecosystem is thinner than VS Code or PyCharm. Performance degrades on larger projects.
Uvik Software engineer verdict: A strong tool for data analysts and researchers doing Python-native scientific computing. In 2026, the absence of AI integration is a material disadvantage. Unless AI assistance is not a priority, VS Code is the better long-term choice.
Vim/Neovim + LSP
Best for: Terminal-native development, performance-first environments, developers who have invested in a Vim workflow.
Neovim has native LSP support, with mature Python LSP servers: Pyright (Microsoft, also powering VS Code’s Pylance) and Ruff (extremely fast linting and formatting, replacing Flake8, isort, and Black). With Mason for server management and nvim-cmp for completion, Neovim becomes a capable Python IDE with minimal memory overhead.
AI assistant compatibility: GitHub Copilot’s Neovim integration is mature. Tabnine also supports Neovim. Windsurf provides both a Vim plugin (windsurf.vim) and a native Neovim plugin (windsurf.nvim) for inline completions. None of these plugins provides the full agentic workflows available in Cursor or VS Code with Copilot Agent mode.
Downsides: Steep learning curve and high configuration overhead. Not suitable for beginners or teams needing fast onboarding. No native GUI or notebook support. AI assistant coverage is more limited than VS Code or PyCharm.
Uvik Software engineer verdict: A legitimate professional Python environment for developers who have already made the investment. Recommending it to a team or newcomer is not practical in 2026.
Thonny
Best for: Complete Python beginners, educators, MicroPython / embedded development.
Thonny comes with Python built in—a single installer gets a beginner writing code with no environment configuration. Its debugger is designed to visualize expression evaluation step-by-step, showing how Python executes subexpressions. The Variable Explorer is always visible. Package installation has a GUI. It also supports MicroPython on ESP32, ESP8266, and Raspberry Pi boards.
AI assistant compatibility: None. This is appropriate for its use case—introducing AI assistants before a learner understands basic control flow would be counterproductive.
Downsides: Not suitable for professional Python work. No AI integration, no plugin ecosystem, limited to beginner use cases.
Uvik Software engineer verdict:The right first tool for Python beginners. Every learner using it should plan to graduate to VS Code within a few weeks. Thonny is not a long-term environment, and that is by design.
IDLE
Best for: Absolute first steps in Python, classroom environments without installation constraints.
IDLE is bundled with every Python installation—no separate setup required. It provides an interactive shell, a basic editor with syntax highlighting and auto-completion, and a minimal debugger. For someone running their first Python script in five minutes, IDLE is sufficient.
AI assistant compatibility: None. IDLE has no extension support.
Downsides: Limited features, slow on larger projects, basic debugger, minimal customization. No AI integration, no plugin ecosystem.
Uvik Software engineer verdict: IDLE is a baseline, not a tool. It is useful exactly once: the first day someone installs Python and wants to see it run. Move to Thonny or VS Code on day two.
AI Coding Assistant Compatibility Matrix
| IDE / Editor | GitHub Copilot | Cursor (Native) | Windsurf / Codeium | Tabnine | Best Fit |
| VS Code | ✅ Native, deep | N/A | ✅ Plugin | ✅ Full | Broadest AI choice |
| PyCharm Pro | ✅ Plugin | — | ✅ Plugin (lite) | ✅ Full | AI + deep debug |
| JupyterLab | ⚠️ Community ext. | — | ✅ Plugin | ✅ Browser | Notebook AI assist |
| Cursor | — | ✅ Native | — | — | Deepest AI integration |
| Spyder | ❌ None | — | ❌ None | ❌ None | Not AI-focused |
| Vim/Neovim | ✅ Plugin | — | ✅ Plugin | ✅ Plugin | Terminal AI coding |
| Thonny | ❌ None | — | ❌ None | ❌ None | No AI support |
| IDLE | ❌ None | — | ❌ None | ❌ None | No AI support |
“Native” = built into the IDE core.
“Plugin” = installable extension with full or near-full features.
“Partial” = works with reduced functionality.
“—” = not available.
GitHub Copilot in VS Code is the lowest-friction, most affordable entry into AI-assisted Python development. Cursor offers the deepest AI integration but requires a full IDE switch. Windsurf provides the broadest IDE coverage of any AI assistant—with plugins for VS Code, JetBrains, Vim, Neovim, and JupyterLab—though its full agentic Cascade features require the standalone Windsurf Editor. Tabnine is the only tool with a credible on-premise deployment for enterprises with strict data residency requirements.
What Changed in 2026
Three structural shifts made AI integration a primary IDE selection criterion this year. First, Cursor crossed one million users and entered Fortune 500 adoption, establishing that a standalone AI-native IDE—not just a plugin—has product-market fit. Second, Windsurf was acquired by Cognition AI for approximately $250 million in late 2025, combining Cascade’s agentic IDE capabilities with autonomous development ambitions; Windsurf then overhauled its pricing in March 2026, moving to a quota-based model at $20/month Pro. Third, PyCharm unified its product in 2025.1, bundling JetBrains AI Assistant and closing the AI feature gap with VS Code. The practical effect: AI-first developers increasingly treat IDE selection as part of the AI toolchain decision, not separate from it.
Which IDE for Which Use Case
Building FastAPI or Django Backends
Primary: PyCharm Pro. Alternative: VS Code + GitHub Copilot.
PyCharm Pro provides framework-aware autocompletion for Django ORM, URL routing, and template contexts that VS Code’s Python extension does not match without significant manual configuration. Its debugger attaches directly to a running Django or FastAPI development server. VS Code + Copilot is a strong alternative for teams that also write TypeScript, SQL, or other languages in the same codebase.
When not to choose PyCharm: If the team works across multiple languages or if the project requires significant notebook-based exploration, VS Code’s breadth is preferable.
Python Data Engineering (Airflow, dbt, Spark)
Primary: VS Code + GitHub Copilot. Alternative: PyCharm Pro.
Data engineering projects involve Python DAG files, SQL models, YAML configurations, Dockerfiles, and sometimes Scala—a polyglot mix that VS Code’s extension ecosystem handles better than a single-language IDE. Copilot’s inline suggestions cover Airflow DAG boilerplate, dbt Jinja SQL, and PySpark transformation patterns. PyCharm Pro is the alternative for teams whose data engineering work is primarily Python-heavy rather than SQL or YAML-heavy.
When not to choose either: If the team’s workflow is notebook-based EDA, JupyterLab should be the entry point before writing production pipeline code.
AI/ML Development (LangChain, LlamaIndex, PyTorch)
Primary: VS Code + GitHub Copilot. Alternative: Cursor.
AI/ML work in 2026 involves both notebook-based experimentation and code-based production work, including inference endpoints, agent architectures, and retrieval pipelines. VS Code handles both via its Jupyter integration and Python extension. Copilot has good coverage of PyTorch, LangChain, and LlamaIndex patterns. Cursor is the alternative for developers building complex multi-file agent architectures where Composer’s multi-file awareness accelerates development.
When not to choose either: For pure notebook-first ML model training, JupyterLab paired with VS Code for code delivery is a practical split.
Learning Python from Scratch
Primary: Thonny. Alternative: VS Code.
Thonny’s single-installer setup (Python included), step-through expression evaluation, and beginner-friendly error messages make it the most appropriate first environment. VS Code is the right second step once a learner has completed their first project—transitioning within the first few weeks ensures skills transfer to professional environments.
When not to choose Thonny: As soon as the learner starts real projects, Thonny’s lack of extensibility will quickly become limiting.
General-Purpose Python for Teams
Primary: VS Code + GitHub Copilot. Alternative: PyCharm Pro.
VS Code + Copilot is the practical team default: the most-used Python IDE, the most compatible with adjacent tooling (Docker, Git, cloud CLIs, database tools), and the most cost-effective AI assistant at $10/user/month. Onboarding new engineers to VS Code is faster than to PyCharm or Cursor.
When not to choose VS Code: If the team is purely Python-focused and values deep framework-aware tooling over breadth, PyCharm Pro’s per-seat cost is justified.
Large Legacy Codebases
Primary: Cursor. Alternative: PyCharm Pro.
Cursor’s codebase indexing and retrieval-augmented context generation make it effective for navigating and modifying large, unfamiliar Python codebases. Composer mode can execute codebase-wide refactoring from a natural-language description. PyCharm Pro is the alternative for teams preferring a more controlled, manual approach—its refactoring tools and code navigation are the strongest in the traditional IDE category.
When not to choose Cursor: If the team is cost-sensitive ($40/user/month for Teams vs. $19/user/month for Copilot Business), or if the codebase involves heavy notebook work.
Notebook-First Experimentation
Primary: JupyterLab. Alternative: VS Code (with Jupyter extension).
JupyterLab is a purpose-built notebook environment. Its cell-based execution, built-in terminal, file browser, and extension ecosystem make it the most complete pure-notebook IDE. VS Code with its Jupyter extension is a strong alternative for teams who want notebooks and production code in one environment.
When not to choose JupyterLab: If the project requires writing production-quality Python modules, running tests, or managing a multi-file codebase, JupyterLab alone is insufficient.
Frequently Asked Questions
What is the best IDE for Python in 2026?
VS Code + GitHub Copilot is the best all-round Python IDE for most developers in 2026. It integrates natively with the most-adopted AI coding assistant, handles the full range of Python workflows from back-end to data science, and costs $10/month for Copilot Pro. For pure Python back-end work on complex codebases, PyCharm Pro is a compelling alternative. For AI-agent-driven development, Cursor is the most capable tool in its category.
Is PyCharm better than VS Code for Python?
It depends on the workflow. PyCharm Pro has the stronger Python-native debugger and deeper framework integration for Django and FastAPI. VS Code is more versatile across languages, has stronger AI assistant support at the team level, and costs less. For a Python-only team working on complex back-end projects, PyCharm is the more specialized tool. For a polyglot team or one prioritizing AI-assisted workflows, VS Code wins on breadth.
What is the best Python IDE for data science?
For notebook-first data science, JupyterLab is the standard. For data science teams that also write production Python code, VS Code + GitHub Copilot is the most practical combined environment. Spyder is a strong choice for scientific computing workflows that do not require notebooks, offering a Variable Explorer and Plots pane integrated out of the box.
What is the best Python IDE for beginners?
Thonny is the best first Python IDE for complete beginners: it includes Python, has no environment setup, and features a step-through expression debugger designed for learning. Once comfortable with basic Python, beginners should transition to VS Code, which is the industry standard and the environment most likely to match a professional team’s tooling.
Which Python IDE works best with GitHub Copilot?
VS Code provides the deepest GitHub Copilot integration, including inline completions, multi-file Copilot Agent edits, and chat. PyCharm has a functional Copilot plugin with broad support. Neovim has a mature Copilot plugin. JupyterLab has community extensions for Copilot (such as Notebook Intelligence) but no official GitHub-maintained integration.
Is Cursor better than VS Code for Python?
Cursor is better for Python developers who want native AI agent workflows—Composer mode for multi-file edits and Background Agents for autonomous task execution. VS Code is better for most other use cases: broader plugin ecosystem, lower cost, better notebook support, and more mature Copilot integration. Cursor is a separate IDE built on VS Code’s codebase, not a VS Code extension.
What IDE should I use for FastAPI or Django?
PyCharm Pro is the strongest option for FastAPI and Django development due to its framework-aware autocompletion and integrated debugging. VS Code + GitHub Copilot is a close second and the better choice for teams working across multiple languages.
What IDE is best for Airflow, dbt, and Spark workflows?
VS Code + GitHub Copilot is the recommended IDE for Airflow, dbt, and Spark workflows. Data engineering projects involve a mix of Python, SQL, YAML, and Jinja that VS Code’s extension ecosystem handles well. PyCharm Pro is a good alternative for Python-heavy data engineering work.
