Image by Author | Midjourney & Canva

Today’s analysts operate differently. Spreadsheets and static reports are no longer the only tools involved. Many analysts deal with databases, develop scripts, query APIs, and even create internal tools. This implies that they require a whole stack, not simply a browser and Excel.

The reality is, you don’t need to be a developer to use development tools. Having the appropriate tools can help you work more efficiently and share your work more easily. They can help you work more effectively with technical teams, automate tedious tasks, and even go deeper into data exploration.

In this article, we’ll go over seven tools that every contemporary analyst needs to be familiar with. You can increase your control, expedite your process, and think beyond dashboards with these solutions.

What modern analysts actually do | Image by Author

Let’s get into it.

1. JupyterLab

If you’ve worked with Python or R, you’ve probably come across Jupyter Notebooks. But JupyterLab takes it further. Think of it as your analyst’s workbench, where you can run code, write notes, test ideas, and visualize everything in one place.

What makes it powerful isn’t just the ability to write and run code. It’s the flexibility. You can open multiple notebooks, terminal windows, scripts, and visualizations side by side. It’s great for jumping between data cleaning, plotting, and documenting all without breaking flow.

Whether you’re experimenting with a new dataset, writing quick Python scripts to analyze trends, or cleaning up CSVs, JupyterLab gives you a smooth loop: write, run, see, repeat.

You can also plug in extensions to preview datasets, render plots with tools like Plotly, or even connect to remote kernels if you’re working on bigger machines.

How analysts use JupyterLab | Image by Author

2. VS Code

Visual Studio Code is more than a code editor. It’s the ideal compromise between a feature-rich IDE like PyCharm and something more straightforward like Notepad. Analysts adore it for this reason.

In a single window, you can compose Markdown notes, access CSV files, execute Python, R, or SQL programs, and even test REST APIs. It doesn’t try to be too smart, and it doesn’t get in your way. It just works.

What makes VS Code extra useful for analysts is its extensions. You can install ones like:

• Python for linting and Jupyter support
• SQLTools for writing and running SQL across different databases
• REST Client to test APIs right inside the editor
• GitLens to work with Git in a visual way

There’s even a Jupyter extension that lets you run notebooks inside VS Code if you prefer everything in one place.

If you’ve ever found yourself switching between five apps to finish one task, VS Code might be your fix.

VS Code extensions that make analysts’ lives easier | Image by Author

Once you’re comfortable with the basics, you can slowly expand your knowledge into your full analyst environment.

3. Postman

Analysts today don’t just pull data from spreadsheets or databases. More often, they tap into APIs, pulling real-time product data, financial information, user metrics, or even AI model results. That’s where Postman comes in.

With Postman’s user-friendly UI, you can perform API queries, view answers, and troubleshoot rapidly. To verify that an endpoint functions, there’s no need to create a front end or write curl instructions.

Suppose you wish to retrieve information from a pricing API. After entering the URL, adding a header or token, and clicking “Send,” the JSON response appears in a convenient tab. You can then examine, filter, or copy it directly into your notebook or script.

You may also store requests in collections to avoid starting over every time. This is invaluable when working in a group. Everyone can see the anticipated replies, the keys required, and how the APIs are utilized.

Additionally, if you’re testing unfinished APIs, you can still develop and test around Postman’s mock answers.

How analysts use postman | Image by Author

Once you get the hang of it, Postman becomes your go-to bridge between raw data and clean analysis.

4. GitHub

You don’t have to be a software engineer to use GitHub. And no, you don’t need to memorize 20 terminal commands either. GitHub is essentially a clever way to track changes, back up your work, and collaborate without overwriting each other’s files.

Let’s say you are working on a notebook that evaluates the performance of products. You modify and ruin a chart, but you can’t recall what you did. If you have the notebook in a GitHub repository, you can view a complete history of modifications, revert to an earlier version, or even start a new branch to experiment without affecting the original.

You can also:

• Share your work with teammates or the public
• Leave comments on each other’s commits or pull requests
• Use issues and discussions to track questions or requests

And if you’re into AI-assisted workflows, GitHub Copilot can suggest full code snippets as you type. It works inside notebooks, scripts, and even documentation.

How analysts use GitHub | Image by Author

Forget naming files things like final_v5_this_one_real_final.ipynb. GitHub helps you stay organized without the chaos.

5. Metabase

Metabase is perfect for analysts who want to share insights quickly without setting up a full BI pipeline. You don’t need to write complex queries (though you can), and you don’t need to train your team to use it. Just connect your database, and you’re good to go.

Let’s say your team wants to know: “What’s our daily active users by country over the last 30 days?”

With Metabase, you can do that in two clicks. Filters, grouping, and date pickers make it easy to visualize the data. No SQL? No problem. But if you know SQL, there’s a full-featured editor with variables, stored queries, and even visual joins.

What distinguishes Metabase is how easy it is to:

• Build automatically refreshing dashboards
• Create alerts (e.g., when income drops below a threshold)
• Let non-analysts dig into the data without breaking things

Metabase in action | Image by Author

It’s not a replacement for deep-dive analysis, but it’s a powerful way to turn raw data into team-friendly insights. If you want your work to be seen and used, Metabase helps you bridge that gap.

6. DBeaver

When you work across multiple databases, such as Postgres, MySQL, Snowflake, or SQLite, switching between tools can quickly become messy. DBeaver fixes that.

It gives you a single, clean UI to connect, write queries, browse tables, and visualize results. Whether you’re exploring a massive user events table or just trying to understand someone else’s schema, DBeaver makes it easier to navigate.

You can:

• Run and save SQL queries
• View ER diagrams of your database
• Export query results to CSV or Excel
• Connect to cloud DBs and local instances in the same place

And it doesn’t get bloated. The tabs stay tidy, and the autocomplete is smart. You don’t need to run a thousand joins in your head; DBeaver helps you follow the logic as you build.

Analyst’s SQL workspace in DBeaver | Image by Author

7. Notion or Obsidian

Data work isn’t just about charts and queries. A lot of it is thinking, tracking ideas, writing assumptions, and mapping messy paths to clean insights. That’s where tools like Notion and Obsidian come in.

If you like working in a structured, visual way, Notion is your friend. You can create:

• Research logs
• Documentation for recurring queries
• Dashboards of your work
• Even mini wikis for your team

If you prefer something local, fast, and more markdown-based, Obsidian does the job. It’s offline-first and perfect for building a web of notes with links between ideas.
Both tools help you:

• Keep track of past projects
• Capture half-baked ideas before they’re gone
• Share findings or templates with others

How analysts use Notion vs Obsidian | Image by Author

It’s easy to forget what worked or why a certain choice was made six months ago. These tools help you build your own internal knowledge base and stay sharp over time.

Wrap up

Being an analyst today means wearing more hats: you’re part data detective, part engineer, and part storyteller. The right tools don’t just make your work faster, they shape how you think, what you notice, and how confidently you can act on the data.

You don’t need to learn all seven tools overnight. Start with what solves your most annoying pain point, maybe that’s managing messy SQL, or versioning your notebooks, or making faster charts for your team. Then move up from there.

Each tool has a community behind it, lots of guides online, and real examples you can learn from. Try them out and build something better than a dashboard.