Astrato Blog | Insights on Modern BI, AI, and Data Innovation

Introducing Astrato’s Nash AI

Your team’s agentic BI assistant – it builds the trusted data model first, then the dashboards on top, all in plain language. Nash AI is in public preview now.

Sankey Diagram Use Cases: When to Use One in Modern BI

A practical guide to Sankey charts in BI — use cases that justify one, when a bar chart wins instead, and why live-query Sankeys matter for operational workflows.

Snowflake Cortex LLM Generated Insights + Tables into Slack & Teams

Did you know you can add Snowflake GenAI Insight Summaries powered by Snowflake (and table/view reporting) to Slack & Teams, directly using Snowflake?

11 Best Practices to Optimize Snowflake

Snowflake’s powerful platform is architecturally different from almost every traditional database system and cloud data warehouse. Snowflake has completely separate compute and storage, and both tiers of the platform are near-instantly elastic.

8 Best Omni Analytics Competitors and Alternatives for 2026

Looking for Omni Analytics competitors in 2026? Compare the 8 best Omni Analytics alternatives including Astrato, Looker, Sigma, and Hex for modern BI.

9 Best ThoughtSpot Competitors and Alternatives for 2026

Looking for ThoughtSpot competitors in 2026? Compare the 9 best ThoughtSpot alternatives including Astrato, Power BI, Tableau, Sigma, Hex, and more.

Warehouse-Agnostic Semantic Layer: Model Once, Run on Any Warehouse

Model your metrics once and run them on Snowflake, BigQuery, or Databricks. Why a warehouse-agnostic semantic layer ends platform lock-in.

How to Define Your Core Metrics in a Semantic Layer: ARR, Churn & Active Customers

How to define ARR, churn, and active customers in a semantic layer — a reusable template so every metric means one thing everywhere.

Embedded BI for Databricks: AI/BI vs. Third-Party in 2026

Databricks AI/BI now embeds dashboards externally. So when do you still need a third-party platform? A decision guide for data platform teams.

Embedded Analytics for BigQuery: Best BI Platforms 2026

Compare embedded analytics platforms for BigQuery — Astrato, Sigma, Looker, Looker Studio, Power BI, Metabase. Built for product teams in Slack.

Guided Self-Service Analytics: A Practical Guide

Self-service BI keeps breaking. Guided self-service fixes it: the data team designs the path, business users move freely within it. Here's how.

Self-Service BI on Snowflake: Guide for Non-Technical Teams

Every BI vendor claims self-service in 2026. A four-level spectrum, five evaluation questions, and an honest comparison of the platforms that compete on Snowflake.

Warehouse-Agnostic Semantic Layer: Model Once, Run on Any Warehouse

Model your metrics once and run them on Snowflake, BigQuery, or Databricks. Why a warehouse-agnostic semantic layer ends platform lock-in.

How to Define Your Core Metrics in a Semantic Layer: ARR, Churn & Active Customers

How to define ARR, churn, and active customers in a semantic layer — a reusable template so every metric means one thing everywhere.

Types of Semantic Layers: Warehouse-Native vs Universal vs BI-Tool

Warehouse-native vs universal vs BI-tool semantic layers: how each works, what it is best for, and how to choose the right one for your stack.

How to Build a Semantic Layer (Warehouse-Native, Without a Multi-Year Project)

A practical guide to building a semantic layer: connect your warehouse, model joins, define metrics, secure and version it. No multi-year project.

Semantic Layer for Data Apps: Why Writing Data Back Demands Governed Definitions

Dashboards read data; data apps write it back. See why safe write-back needs a governed semantic layer — with Ceres Pharma proof.

How We Put 108 Million Rows of Logs on Screen Without an Extract

Query 108 million rows of machine telemetry live on ClickHouse — no extract, no sampling, no pre-aggregation. Filter, compare, and spot anomalies on raw time-series data.

Why I Was Tired of Watching Good Forecasts Lose to Gut Feel

Let planners run their own demand forecasts: enter a category, measure and horizon, and a Prophet model on Snowpark returns the forecast — in the warehouse, no notebook, no data scientist.

The Day I Realized Our Bug Dashboard Couldn't Fix a Single Bug

Stop bouncing bugs between a spreadsheet, Slack, and a ticketing tool. Build one live app to create, triage, and resolve issues in place — inline forms, writeback, governed.

How We Made the P&L Readable to People Who Aren't Accountants

Turn a P&L statement only accountants can read into a self-service dashboard anyone can navigate — governed line-item definitions, live warehouse data, drill from summary to transaction.

How We Gave RevOps a Churn Model They Can Run Without Data Science

Let the people who fight churn model it themselves: change a customer’s MRR, rescore their risk with a Snowpark model in the warehouse, and act — no data-science queue, no code.

We Replaced the Month-End Budget Spreadsheet With a Live App

Turn the month-end budget-vs-actuals scramble into a live app: enter the budget, see variance update against live warehouse data, with writeback and governance built in.