Dashboard Creation Tools

Apache Superset might be the most underrated dashboarding tool out there: 1. Open source and free. 2. Supports nearly every data source you’ll ever need. 3. Gives control for customizations. 4. The richest selection of graphics and charts. 5. Semantic layer support for SQL. 6. Active community support. 7. Virtual datasets and views. Superset was built by data people for experienced data users who are comfortable with technology. It might not be suitable for everyone on your team. It’s designed for speed and efficiency without unnecessary complexity. There’s no “Read this tutorial to learn how to change measures to dimensions” or “Contact customer support to add a new user” or run SELECT* and see what’s there. If someone can build in Superset, they can handle anything. Not because Superset is difficult (it isn’t!), but because using it trains you to achieve the desired output with minimal steps. Analysts proficient in Superset tend to use only the necessary number of columns for reports, optimize data structures for efficiency, aim for the most optimal time for query execution, etc. Superset may not be a BI tool in the traditional sense, but it stands out as one of the most capable, lightweight, and thoughtfully designed dashboard builders. My recent overview of Superset and what to expect from modern BI tools - https://lnkd.in/g5Wwwvq6

GoodData is redefining the way we think about data visualization with an innovative concept called Napkin Analytics—a way to turn your drawn ideas into interactive dashboards. I found this fascinating. Here’s why: We often start with ideas in meetings, brainstorming sessions, or even casual discussions—charts drawn by hand, layouts mocked up on whiteboards, or notes scribbled on paper. But translating those into actual BI tools? That’s where many great ideas get stuck. Napkin Analytics aims to solve that. Here’s how it works: -- You sketch out a chart—literally, on paper or a whiteboard -- Using AI models like GPT-4o and computer vision, the image is processed to identify chart types, titles, axes, and metrics -- Then, semantic mapping kicks in—linking your sketch with actual datasets in your BI system -- The result? A fully interactive and dynamic dashboard, generated from a drawing This has big implications: - Faster idea-to-insight cycles - Less reliance on technical skills for initial prototypes - Better collaboration between business users and data teams This isn’t just a cool feature—it’s a bridge between human creativity and data-driven action. You can check out the full blog here → https://lnkd.in/d92Nfgrz What do you think—is this the future of how we’ll build dashboards?

Your data is a dumpster fire. But you still want to be “data-driven.” Cool. Let’s fix that. Without spending $10K on tools you’ll never use. → Step 1: ETL Move the data. Don’t overthink it. Fivetran — Free for 500K rows. Unless you're Google, you're fine. Python script — If you’re fancy (or broke). The goal? Get your data into one place — no duct tape, no “just export to Excel.” → Step 2: Data Storage BigQuery. That’s it. Dirt cheap. Built to scale. Doesn’t cry when you throw SQL at it. Storage cost? Basically zero. Query cost? Only if you go full goblin mode. → Step 3: Dashboards That Don’t Suck Looker Studio or Tableau. Your choice. Because insights can be shown in any BI tool if done by a pro data analyst. Make it visual. Make it clear. Make it clickable. That’s how you get decisions made. → Real-Time Data? Segment. Tracks product usage, events, user clicks, all that juicy stuff. Just don’t pipe it straight into BigQuery unless you want a surprise invoice that makes your CFO cry. That’s the starter pack. No BS. No “enterprise-grade solutions.” Just tools that work, stack clean, and don’t bankrupt you. If you’re not doing this — you’re not data-driven. You’re just drowning in CSVs with a dream.

No Power BI? No Problem. Everyone seems to be building dashboards in Power BI these days. But what if you don’t have access? Maybe your company hasn’t provided a license. Maybe your laptop can’t handle it. Or maybe you’re just not sure where to begin. Here’s what most people don’t realize: You can still build solid analytics skills using free, accessible tools, and those same skills will carry over when you do start using Power BI. Tools like Tableau Public, Looker Studio, Google Sheets, and Excel Online can teach you how to clean data, build dashboards, apply formulas, and tell compelling stories with data. You don’t need expensive software to start. You just need the right mindset and resources. I’ve pulled together some of the best tutorials and practice tools to help you get started: ↳ Access the Tableau Free desktop version here: https://lnkd.in/dxXxzR_m ↳ Learn how to install it here: https://lnkd.in/dkYHrQfC ↳ Introduction to Tableau: https://lnkd.in/deXZiDjG ↳ Connecting to Data Sources: https://lnkd.in/dq8ibppR Core Skills ↳ Calculated Fields: https://lnkd.in/dEdhYjYC ↳ Filters & Parameters: https://lnkd.in/dJPaGJ_i ↳ Tableau Zen Master Tips & Tricks: https://lnkd.in/dXqY3yPs ↳ Top 10 Tableau Dashboard Design Tips: https://lnkd.in/dZcewx7i Advanced Techniques ↳ Create a Stunning Advanced Dashboard in Tableau: ↳ LOD Expressions: https://lnkd.in/dSfjmuWg ↳ Tableau Prep: https://lnkd.in/dkYHrQfC Real-World Applications ↳ Tableau Public Portfolio: https://lnkd.in/dxXxzR_m ↳ Case Studies: https://lnkd.in/d_jRSttk Additional Resources ↳ Practice Datasets: https://lnkd.in/dEwcEiVq ↳ Cheat Sheets: https://shorturl.at/3SHnK ↳ Communities: https://lnkd.in/dqTZySvW Know someone who needs this? Share it with them. ♻ If you’re serious about leveling up your data career, join my WhatsApp channel for direct insights & updates, or subscribe to my YouTube channel for in-depth tutorials. ↳ My WhatsApp channel: https://lnkd.in/dawGfYjq ↳ My YouTube channel: https://lnkd.in/deiQF4DW

If you want to work like a senior Power BI developer, Start with their toolbox. Because seniors don’t just know DAX. They understand how every supporting tool multiplies their output. Think of your workflow in layers, not just visuals, but the backbone that makes a report reliable, fast, and scalable. Here’s the real breakdown: --- 1️⃣ Query & Formula Layer This is where most dashboards slow down. - 𝗗𝗔𝗫 𝗦𝘁𝘂𝗱𝗶𝗼: lets you see what your formulas are doing under the hood. You can test queries, analyze performance, and fix issues before they become nightmares. - 𝗗𝗔𝗫 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: turns your slow measures into efficient ones. Think of it as a “performance consultant” for your DAX. It shows you what to rewrite and 𝘄𝗵𝘆. --- 2️⃣ 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿 Your model structure determines 80% of your report’s performance. - 𝗧𝗮𝗯𝘂𝗹𝗮𝗿 𝗘𝗱𝗶𝘁𝗼𝗿: helps you build a clean, reusable, scalable semantic model. You get best practices, scripting, role management, and fast edits that Power BI alone can’t match. --- 3️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿 Professional BI teams don’t deploy blindly. - 𝗔𝗟𝗠 𝗧𝗼𝗼𝗹𝗸𝗶𝘁: lets you compare PBIX metadata, handle version control, and deploy changes without breaking production. It’s the closest thing to “safe deployment” in the Power BI ecosystem. --- 4️⃣ 𝗗𝗲𝘀𝗶𝗴𝗻 𝗟𝗮𝘆𝗲𝗿 A good dashboard isn’t built in Power BI; it’s designed before it’s built. - 𝗘𝘅𝗰𝗮𝗹𝗶𝗱𝗿𝗮𝘄 / 𝗖𝗮𝗻𝘃𝗮 / 𝗙𝗶𝗴𝗺𝗮: help you map UI/UX, layout, colors, and flow before writing a single measure. This saves hours of rebuilding and gives stakeholders clarity early. --- Once these tools become part of your workflow, you stop “managing Power BI”… and start 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 dashboards like a senior developer. --- Tools are leverage. Leverage accelerates career growth. ✅ Image Source - sqlbi<dot>com #powerbi #dataanalyst #data #day29

I built a full-stack data dashboard app in real-time at the Streamlit Creator's Monthly Meetup using Cortex Code and the Streamlit Agent Skill. The dashboard has KPI metrics, a scatter plot of fluorescent protein spectral data, chromophore distribution chart, and a filterable protein explorer table. The workflow was straightforward. I described what I wanted in natural language, Cortex Code agentically generated the Streamlit app connected to Snowflake, and I iterated on the design conversationally until it looked right. What stood out to me was that I spent my time on what the dashboard should show, not how to wire it all together. Layout, caching, Altair charts, Snowflake connectivity, uploading the data to Snowflake ... were all handled for me. If you're curious, you can try this out yourself ... 🕹️ Cortex Code https://lnkd.in/gAKCmJdp 🎈 Streamlit Agent Skills https://lnkd.in/gZjQ-SRX