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Airbnb Reviews (✨❄️Snowflake Cortex)

Unlock insights with our dynamic Airbnb dashboard! Whether you're a property owner, a traveler, or a market analyst, our dashboard offers a comprehensive view of Airbnb listings and reviews in Boston.

  • Interactive Map: Navigate through Boston’s neighborhoods to see the distribution of listings.
  • Detailed Listings: Access key details such as price, availability, and amenities.
  • Review Analysis: Understand guest experiences with sentiment analysis and ratings breakdown.
  • Market Trends: Track trends over time to make informed decisions.
  • Self-Service BI: Explore data without adding tickets to a never-ending backlog. Bookmark your analytics to revisit it and share with others.

Drill-down into themes and topics with our word cloud. https://help.astrato.io/en/articles/8811397-word-cloud

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Change the org, date range or click any chart — data updates on the fly.
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Chart Configurator

Easily configure and explore interactive charts to visualize your data with drag-and-drop simplicity.

❄️🦸2026 Snowflake Data Superhero Map

Dive into this interactive map showcasing the global community of Snowflake Data Superheroes! Using Cortex, we enriched location data with Longitude & Latitude, bringing the Superheroes' worldwide presence to life. Built in Astrato, the visualization leverages our native heatmap and point map layers to deliver a seamless, dynamic experience. See the power of live data connections and intuitive storytelling in action! Created by our very own Snowflake Data Superhero, Piers Batchelor.

108M Rows of Logs & IOT data | ClickHouse

A fast, simple way to explore machine-generated log data at scale. This workbook uses the Brown University MgBench dataset, a public benchmark built to test analytics performance on high-volume operational logs. You’ll find millions of rows of raw machine metrics – CPU, memory, disk, network, and load stats – captured across multiple machines and groups. It’s ideal for showing how Astrato handles live-query workloads, fast filtering, and smooth interaction on dense time-series data. What’s inside: Machine groups and names CPU usage (idle, system, user, wio, nice) Memory and disk metrics Load averages (1/5/15 minute) Network bytes in/out Time-series views built directly from the raw MergeTree tables This workbook helps users explore: Trends over time Resource spikes Group comparisons Anomalies in machine performance Great for demos that highlight real operational telemetry, live queries, and how Astrato handles large datasets without extracts. Sources: https://clickhouse.com/docs/getting-started/example-datasets/brown-benchmark | https://clickpy-playground.clickhouse.com/?query_id=GEOICKHUJBUXMYROUXJK7C

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