Select a .pbix file and it runs entirely in your browser. The file is never sent to a server, all information is kept locally.

Features:

- Lists tables, columns and relationships

- Export tables to CSV/XLSX

- Shows all DAX measures with their expressions

- No install and no account

Feedback and suggestions are very welcome!

I'm struggling at work, and I need advice. I'm an analyst, but my question is not super specific to analytics, so I understand if I need to move my post.

The main problem is that I've had multiple coworkers send me long, Claude-generated documents about our data models and tasks, and I am having a hard time understanding them.

I work at a decently large company, and I've been shuffled around on a few different projects. This means I have to learn all about the underlying data models, metrics definitions, etc. each time I move. I am a big "learn by doing" person. In my previous jobs, I'd be assigned a couple of ad hoc requests, and that would help me build my understanding of the data and how it's used.

At this job, someone (peer or, the main culprit , my own manager) will send me a zip folder with a mix of md, sql, py, all generated by Claude, and say "this should have what you need." I'm expected feed everything into Claude, validate the output, and then have a report or updated data model ready to go in less than a week. Most of the instructions I receive were written by a robot, for a robot. Standard, shared documentation is missing in a lot of places, and the context lives in someone's head and then gets put into a prompt for Claude.

I'm having a major comprehension problem paired with a speed problem. I'm expected to move quickly and defend all my numbers, but I feel I can only do one or the other. My boss insists I must be prompting Claude incorrectly if I am getting confused or slowed down.

Does anyone have advice about how I can use Claude (as it is required by my company) but not totally melt my brain? I feel so lost right now.

Hi,

I have been doing data analysis for quite some time now, and notice a common theme of issue I have had when working across departments in my company, as the dataset may vary the knowledge required to understand is always different, hence getting the time to sit down and study them, is near impossible.

In some cases, values in columns don't make sense, objectives aren't properly defined, or important information is scattered across questionnaires, documents, emails and conversations with the people who collected the data.

And of course, the people who have that knowledge aren't always available when you need clarification.

I want to understand how other people deal with this, particularly those working across multiple projects or departments in a company or organisation.

I have been trying to tackle this issue by using a plugin (currently still in the work, but getting good result so far), to assist me . With it, I am able to simultaneously connect the spreadsheet I'm analysing to a shared project space containing uploaded contextual documents or files that contains information and knowledge related to the spreadsheet, so I can find information without having to search through everything manually again. So when going through the dataset, I can click on a variable and it searches the documents and find information on it that are presented in those saved documents.

But nonetheless, I'm interested in how you approach these problems, especially those working in different industries or with larger datasets.

Would love to hear about your experiences.

I build sales reports for e-commerce brands and this keeps coming up. A customer orders in October and gets refunded in November. Shopify puts the refund in November, and accounting usually wants it there too. But if you're judging how October's campaign or product did, it makes more sense to pull the refund back to October, otherwise October looks better than it was and November looks worse.

How do you handle it? One view, two views (by refund date and by order date), or something else?

Hii everyone,

I was a digital marketer executive in an agency, after i quit my job i started learning tech stack for data analysis i am finished with learning and currently on project i started my project last week its a cart abandonment analysis from GA4 dataset on big query whole plan is to analyze using creating a funnel than using python perform statistical tests ( hypothesis testing and ab testing ) and present my findings through power bi.

ideation part was done now i actually had to do project so i asked claude to create a guide for me to follow, a project guide and another solution guide to it.

But the guide's language was tuff it gave me ques and was done with i didn't knew what to do or how to do to it fast forward to today i have improvised and created 4th version of that guide with improved language better guidance but there is always something missing a vague question important information being left out for my sake i created an excel with imp fields out of 108 columns.

Today after writing a 50 line query to create a session table i was confused like why am i doing this i couldn't ans why i need this table it doesn't look like the funnel i thought it would be, mind you i have created funnels before in sheets for campaign analysis from top of the funnel metrics to the bottom of funnel from cpm cpc ad spend to order initiated order completed and all the steps in between so i asked chatgpt about the funnel part of my project ( which i thought i was working toward bcz of all the steps in between ) how is it related and it couldn't ans probably or give me reason which makes sense i wanted an industry level project and it told me that we will prove that shoppers are dropping and at what stage like wtf we already know shoppers are dropping and told me to analyze by device and demographics which still falls under EDA what realistic problem finding is that and what realistic solution can i provide ?

I am facing too much time reading that guide and going back and forth with GPT/claude to understand things and actual process because of the confusion caused by guide and even the solution fields don't match, can anyone help me with execution how do i execute and create portfolio projects i tried looking up DA projects of people on GitHub but didn't find anything useful i don't know how to even represent a project.

I need a framework on how to create a project i am not copying from anywhere just need guidance, looking for advice on how should i approach project creation even ready to pay ( although i don't have much money tbh but still )

I am seconding guessing that i should just create project with ai ideation would be mine and i can just understand the code and project ( practise SQL and pandas daily and DAX few times a week ) that way i will have coding syntax in mind and create projects more and faster which will get me interviews real learning is on the job anyways i have forecasting, data cleaning and time series projects in mind as well i am short on savings money as well

Howdy people!!

I have been a data analyst for about half a year now and has experienced few issues when it comes to handling and processing data for reporting; some due to departments giving dataset that aren't clear, some due to report requirements or objectives not being definitive and some may be due to unavailability of higher ups who have key knowledge of the data, as of course they are busy people, and so the report you will write is highly dependent on the source data.

Aside this as I grow in my data analytics career what problem could one also face, or even better what problem or experience have you faced or is common in your own area of work or environment as a data analyst.

Would really appreciate a few seconds of your time to explore our dashboards and share your feedback through a short questionnaire. Your responses will help us understand how people interpret and trust AI-based fraud predictions.
Questionnaire: https://forms.gle/eCAHLwVgc3W795dk6

Your participation would mean a lot to our research. Thank you for your time and support!

Please fill it (crying emoji 1000 times)

I am mainly self taught after finding an interest in it from a college project so I'm curious how others have gone about becoming efficient in excel and how long it took to become comfortable with it

Hi everyone!
I’m looking for recommendations for comprehensive Excel and Power BI courses that cover everything from beginner to advanced levels.
I already have an intermediate level of Excel, but I’d like to take a structured course starting from the fundamentals and progressing to advanced topics. My goal is to obtain a certificate that reflects a complete learning path, rather than taking several short courses covering isolated topics.
I’m open to both free and paid options. What matters most to me is course quality, practical knowledge, and recognition in the job market.
I’d appreciate recommendations regarding:
Which platforms and courses offer the best beginner-to-advanced learning paths?
Is it worth taking a complete course even if I already have some intermediate knowledge?
Are there recognized certificates or certifications that employers actually value?
Should I learn Excel first and then Power BI, or study both simultaneously?
Which courses include practical exercises and real-world projects?
For context, I have a background in Chemistry and would like to apply these tools to laboratory work, quality control, research, data analysis, and process monitoring.
I’d especially appreciate recommendations from people who have completed these courses or use Excel and Power BI professionally.
Thanks!

Hi all,

I've started building a free website that explains web analytics concepts (like sessions, bounce rate vs. exits, engaged sessions, active users) through interactive visuals.

The goal is to make tricky measurement rules intuitive without having to dig through dry documentation.

You can check out what I have so far here: https://seeinganalytics.com

I'd love your honest feedback on:

• Are the visuals clear and accurate?

• Which confusing analytics concepts should I tackle next?

Thanks!

hey guys, i need an ai to help me throughout my data analyst course. it needs to help me with sql, excel, and data visualization, and also act as a mentor. would you recommend claude pro or chatgpt plus?

My office has 365 and uses Teams and Sharepoint as much as possible. I'm a new data analyst (no actual experience, I've been with them for 20 years in an unrelated role and a few months ago they decided to weaponize my ADHD by anointing me with their first data analyst title). Because we're already so integrated with Teams, I've been trying to build projects within it, but I've been plagued with occasional glitches.

The last time this happened, I was chasing my tail for hours trying to debug something that turned out to be a random issue with Teams hanging up. This time, I'm trying to daisy chain Power Apps, Power Automate, and Sharepoint lists to ghetto rig a free kinda-sorta-database. It's nothing important, we have a legit database for incident logging, I'm just building a tool for front-line personnel to help with assignment rotation and informal analysis of one specific incident type.

Once again, I was just chasing my tail thinking something was wrong with my half-built Power Automate flow that was randomly alternating between working and not working, until I noticed my Outlook and Copilot app were acting up too, and now both are completely down after rebooting everything. I see that Microsoft 365 has a global partial service interruption now and am wondering how much faith I should put into 365 actually working on a daily basis. Is this a common issue? Would it be better to do most things outside of 365 whenever possible? Or is the interconnectivity worth it for the occasional glitch?

Sharing a project that might be useful if anyone here is into sports/motorsport data or just wants a real-world dataset to practice on.

F1Archive is an archive of 8 seasons of Formula 1 session data, lap times, full telemetry (speed, throttle, brake, gear, RPM, DRS), tyres, weather, race control, team radio, stored in Postgres with a documented REST API and bulk export in CSV, JSONL, Parquet, or SQLite. There's a web viewer for exploring it visually, but it's genuinely usable as a raw dataset source for anyone who wants to do their own analysis without having to scrape and clean the data themselves.

One thing I tried to do properly: data completeness is documented, not hidden. If a session is missing telemetry or has partial data, there's a page explaining exactly what's missing and why, which matters a lot if you're doing anything you want to trust the results of.

Fully open source, happy to talk through the schema if anyone wants to use it for a project.

Have a look at f1archive.org

I made a free PC application. Maybe someone still need to handle large files like this.

GB-scale files open instantly; filter, sort, aggregate, derive, and run SQL, with one-click data-quality diagnostics, split large files and export to Excel — everything runs locally, no data uploaded.

Currently, only a Windows version is available, as I don't have a Mac at home. I used to use a Mac when I was employed, but I'm currently unemployed.

You can download from : Lightning CSV — GB-scale CSV desktop tool

I just started prepping to become a data analyst , i have a degree in Computer Application and i am familiar with python , pandas , numpy , matplotlib and basic ml concpets.
Currently i am studying SQL and i have got decent grip on HAVING , DISTINCT , WHERE , LEFT AND INNER JOINS but i am not really sure how much SQL i actually need to study my for my first job , i have solved 18 questions on SQL50 but i dont understand how am i supposed to apply these concepts in an actual project.
If anyone has any guidance for me tackle this SQL situation and general advise of how i should be prepping to become a Data Analyst in Q1 2027 will be much appreciated, ty again.

Hi All

As a data engineer i spent a lot of annoying time understanding the table Lineage in a prehistoric legacy system.

Based on my hardships and understanding I've built this Lineage tool

https://sahasilak.github.io/GraveTrail/

I would appreciate it if you can spend some time trying it out by sharing feedback like

Good, bad, need improvements , something like this already exists

for the past 4 years or so, our reporting ran almost entirely on Power BI.
it did what it was supposed to do while our data team was small and our business stakeholders were happy receiving scheduled PDF exports / opening dashboard links inside Microsoft Teams.

and yet over the last 18 months, our engineering and analytics setup moved to Linux and Mac, our warehouse matured on Snowflake, and three specific friction points made Power BI painful to live with:

1. The desktop-client bottleneck. Half our analytics engineers and product managers work on macOS or Linux. Power BI Desktop remains Windows-only. Having engineers spin up Windows VMs or cloud desktop instances simply to tweak a report or verify a measure felt absurd for a modern stack.

2. Logic trapped in binary .pbix files. DAX measures, table relationships, and row-level security filters lived inside individual report files or workspace models. We could not run standard git pull request reviews on metric changes, track lineage across versions, or prevent two analysts from writing slightly different versions of the same retention calculation.

3. license sprawl and the Fabric push which was very annoying. What started as $10/user/month Pro licenses steadily escalated with Premium-per-user upgrades, external viewer licensing constraints, and heavy pressure to commit to Microsoft Fabric capacity (F64+) just to access basic direct lake and copilot features.

then we did spend the past month evaluating five open-source and code-first tools to replace Power BI. We wanted an architecture where metric logic lives in version-controlled code, dashboards run cross-platform in the browser, and numbers can be queried both by BI tools and by downstream applications or AI agents.

Here is the comparison breakdown across the options we tested, i have used a bit of claude to structure this output and evaluation, but only a bit. Hope that's more readable now.

and what we learned testing each option?

1. Apache Superset: Great for rich charting, heavy on operational maintenance If your primary goal is finding an open-source tool that matches Power BI's extensive chart variety, Superset is the closest visual equivalent. It handles complex dashboards, geospatial visualizations, and slice-and-dice exploration without per-seat licensing.

• Where it worked: Beautiful out-of-the-box dashboards, zero Windows dependencies, and strong community support.

• Where it pinched: The permission system is intricate and requires careful role configuration. While Superset has virtual datasets, business logic still ends up scattered across ad-hoc SQL snippets in different charts rather than sitting in a centralized semantic layer.

1. Metabase: Unmatched for rapid self-serve, but lighter on modeling governance Metabase remains the fastest tool for getting non-technical team members exploring data. The graphical query builder allows sales and product managers to ask questions without writing SQL.

• Where it worked: Onboarding takes less than an afternoon. Anyone comfortable with Excel filters can build their own cards immediately.

• Where it pinched: Metric logic is stored in Metabase's application database rather than in code. Version control via YAML requires higher-tier licensing, and managing row-level security across multi-tenant departments required duplicating questions once our role hierarchies became complex.

1. Lightdash: Ideal if your team is already 100% committed to dbt Lightdash takes a dbt-first stance: your dbt project is your single source of truth, and Lightdash auto-generates your BI layer from your dbt schema YAML files.

• Where it worked: All metric definitions live in Git pull requests alongside transformation models. Perfect versioning and developer workflow.

• Where it pinched: If an analysis doesn't fit your pre-compiled dbt DAG, you cannot easily explore it on the fly. Furthermore, you cannot point other non-Lightdash tools or customer-facing apps at its metric engine without going through their specific query layers.

1. Evidence.dev: Excellent for data engineers, foreign to drag-and-drop business users Evidence replaces traditional BI dashboards with code-generated analytical markdown documents. You write SQL queries in fenced blocks and render charts with declarative tags.

• Where it worked: Entirely code-driven, fast static rendering, and versioned cleanly in Git. Our analytics engineers loved the developer experience.

• Where it pinched: It is fundamentally a publishing tool for analysts, not an ad-hoc exploration tool for business stakeholders. If a VP wants to drag a dimension into a pivot table on Friday afternoon, Evidence won't serve that workflow.

1. Cube (Cube Core): Decoupling the metric layer from the visualization We tested Cube Core as an open-source semantic layer sitting between Snowflake and our visualization tools. Instead of defining metrics inside each dashboard tool, we defined cubes, dimensions, joins, and measures in YAML files stored in our git repository.

• Where it worked: Universal consumption: We can connect Superset or Metabase to Cube's SQL API (which speaks the Postgres protocol). Both tools query the exact same governed metric definitions, so nobody argues over differing numbers. Performance and caching: Cube's pre-aggregation engine stores partitioned rollups, serving repetitive dashboard filter changes in sub-second response times without triggering live warehouse compute on every query. Multi-surface readiness: Because Cube provides REST, GraphQL, and remote MCP endpoints on top of the same model, our engineers can build embedded customer portal widgets and point LLM agents at governed measures without re-implementing business logic.

• Where it pinched: Upfront data modeling investment: Unlike Power BI where you can drag raw tables and click "Auto-detect relationships," Cube requires explicit modeling of join paths and primary keys in code. If your underlying schema has messy foreign keys, you must resolve them before metrics compile. Dashboard building requires picking a front-end: While Cube provides built-in workbooks and web exploration, teams wanting traditional heavy dashboards typically pair it with an open-source visualizer like Superset or custom React components.

how we decided to structure our stack?

Rather than trading one monolithic vendor (Microsoft) for another single closed tool, we settled on a two-tier decoupled architecture:

1. Semantic & Governance Tier: We use Cube Core as our open-source metric layer. All calculations (such as gross retention, ARR, and active seats), join topologies, and role-based access rules are written in code, reviewed in pull requests, and versioned in Git.

2. Consumption & Visualization Tier: For internal exploratory dashboards and operations teams, we point open-source Superset at Cube's SQL API. For our engineering team's customer-facing portal and agentic workflows, we query Cube's data APIs directly.

would also like to ask those data teams that migrated away from Power BI, did you replace it with an all-in-one platform (like Superset / Metabase), or did you separate the semantic layer from the visualization tool? how did your business analysts transition from writing DAX formulas to code-first Git/YAML modeling?

I wanted to see if X users were amplifying eachother to generate fake virality on X. I got a dataset of top posts posted about politics in India and used Gephi lite to create a network map of the tweet authors and the authors they are replying to. I'm not sure how to interpret these graphs however. Any help?

Hello everyone,

I'm in school and learning sql and python, but I want to work on a project that pulls data into a dashboard that I can put into github and to start a portfolio that way.

There is so much bits and pieces of advice, but I haven't seen a standardized way I guess?

Also, any suggestions for projects would be helpful as well.

Thank you,

I kept getting a useful report out of an AI conversation, then watching the next one rebuild the calculations or charts differently. So I built Trellum, a free and open source Python framework for reports as code.

The screenshot is a Cart Funnel report in the Trellum Dark theme, on synthetic data. A live demo is here: https://trellum.dev/demo/

With Trellum, your coding agent writes a Python report you keep and rerun. Same code, inputs, configuration and dependencies, and you have a repeatable process. New data can change the results without changing how they're calculated.

Two things that help:

• Filters and cross-filters run in the browser, so clicking a bar doesn't rerun the warehouse query.

• You can point at a chart in a local preview and send the change to your still-running agent. It edits the source and rebuilds the preview.

I built it privately for about a year and a half before the public repo went up in October. It's been used in production at a big gaming company that heavily relies on analytics for about a year, and my team there moved completely off Tableau onto it.

Site: https://trellum.dev Source (AGPL-3.0-only): https://github.com/trellumhq/trellum

To try it, open a project folder in your coding agent and paste this:

Set up Trellum in this project. Build my first interactive report with the included sample data, run the relevant checks, and open a live browser review. Keep listening for my feedback so we can refine it together.

Trellum: https://trellum.dev
Setup guide: https://github.com/trellumhq/trellum

Always enjoyed playing around with personal data analysis, and this has been a labour of love for some years now that I dip in and out of, so giving a bit of an update to progress, since the original.

For background, this is built upon the available data export functions from WhatsApp and iMessage (local SQLite database). There are no calls to LLMs or AI involved, and no cloud/servers, it runs offline with everything done locally on-device using standard data analysis techniques.

I've gotten the majority of dashboard design inspiration from the many many hours of playing football manager!

Key components to the analysis:

• Custom built weighted points scoring mechanism for messages which factors in message length, content (laughs, apologies, questions, images, videos etc), speed of response, whether it started a new conversation as well as a series of other factors in order to produce a "contribution balance" assessment. 

• Each conversation is rated based on the total score, giving a quantitative view of how balanced, rich, or responsive it was. 

• I use a heuristic tagging system to detect key language traits - like questions, apologies, laughter etc - using lightweight rules instead of heavier NLP models - tried these early on and the results weren't that much better than plain heuristics.

• I’ve avoided sentiment analysis so far, as standard on-device NLP models didn’t perform well. Also tried experimenting with small on-device LLMs for richer insight - but they are also unreliable and too slow frankly, so am now working on some custom models that can leverage the on-device machine learning model frameworks.

For those that want to try, it's available publicly as a free tool called Mimoto on the app store, supporting data from both WhatsApp (on iOS) and iMessage (on macOS).

Appreciate the community's feedback, and happy to answer any questions, or listen to ideas anyone has about the project. Cheers!

Hi I have been a data analyst for about 5 years and so far it feels like all I do in my job is less actual data analysis and more administrative work or work that other people in my department don't want to do. Occasionally I will work on Dashboards but that's few and far in between compared to administrative work. I'm thinking about pivoting into Cybersecurity or something more technical because this work is just not mentally stimulating or rewarding. Has anyone else dealt with this?

I am looking to create a heat map of activity, and may pay for a basic piece of work if someone can help me.