GaurData

The Default Backend for Building Data Applications.

[GaurData @hellogaurdata]

Why Is Analytical Logic Scattered Across So Many Systems?

Most teams do not have one analytical system.

They have the same analytical logic copied across many systems.

That is where drift begins.

#dataengineering #analyticsengineering #semanticlayer #modernstack #ai

[GaurData @hellogaurdata]

Dashboards Are Great Consumers, Terrible Sources of Truth

Dashboards are excellent consumers of analytical data.

Problems start when they become the place where meaning is defined.

#dataengineering #analyticsengineering #semanticlayer #businessintelligence #ai

[GaurData @hellogaurdata]

@ionleu https://t.co/eJfg0J03dP

[GaurData @hellogaurdata]

The Hidden Cost of Duplicating Metrics Across Systems

Every duplicated metric creates another opportunity for inconsistency.

The problem is not the calculation.

It is having multiple versions of the truth.

#dataengineering #analyticsengineering #semanticlayer #modernstack #ai

[GaurData @hellogaurdata]

Opening gaur to our first users.

First 5 teams. 30 days. No cost.

Let's talk. https://t.co/fMYeD9qS6a

#dataengineering #semantilayer #modernstack #free #analyticsengineering #ai

[GaurData @hellogaurdata]

Should Business Metrics Live in Application Code?

If every service computes revenue differently, you do not have a metric. You have multiple opinions.

Business metrics need stable contracts, not scattered implementations.

#dataengineering #analyticsengineering #semanticlayer

[GaurData @hellogaurdata]

Embedded Analytics Quietly Creates Backend Complexity

Embedded analytics is rarely just frontend work.

Most of the complexity lives underneath:
permissions, APIs, and analytical runtime behavior.
#dataengineering #analyticsengineering #embeddedanalytics #semanticlayer #ai

[GaurData @hellogaurdata]

@volosatovde https://t.co/eJfg0J03dP

[GaurData @hellogaurdata]

Warehouses Solve Storage, Not Consumption

Most analytics stacks stop at storage.

But APIs, dashboards, workflows, and AI systems still need governed analytical consumption.

That infrastructure layer is still missing.

#dataengineering #analyticsengineering #datawarehouse #ai

[GaurData @hellogaurdata]

Now the same founder can ask:
— Which products drove growth?
— Which city is shrinking?
— Are repeat buyers up or down?

Each answer in seconds. Each one consistent with the next....

[GaurData @hellogaurdata]

Running a brand doing ₹50L a month. Shopify, Stripe, Meta Ads, a Google Sheet.

Asked the simplest question 'what was revenue last month?' and got four different answers depending on the tool.

This is what Gaur solves 👇

[GaurData @hellogaurdata]

Gaur is built for this exact gap.

Step 1: Connect data sources. Shopify, Stripe, Postgres, S3, CSV takes minutes.

Step 2: Define revenue once. As a contract. Net of refunds, net of fees, exactly the way the founder wants it.

Step 3: Ask in plain English

[GaurData @hellogaurdata]

@DanielSmidstrup The default backend for building data applications.
https://t.co/fMYeD9qS6a

[GaurData @hellogaurdata]

@ProductHunt https://t.co/eJfg0J03dP

[GaurData @hellogaurdata]

Gaur lets you define your business metrics once and query them in plain English.

No SQL. No data team. No warehouse.

Your dashboards, tools, and AI agents all get the same answer.

[GaurData @hellogaurdata]

@ionleu https://t.co/fMYeD9qS6a

[GaurData @hellogaurdata]

AI Increased Analytics Demand, Not Analytics Trust

AI increased the number of analytical consumers.

It did not solve trust, semantics, or runtime safety.

Those problems still require governed analytical systems.

#ai #llm #dataengineering #analyticsengineering #semanticlayer

[GaurData @hellogaurdata]

@oPilot_app https://t.co/eJfg0J03dP