Create, Schedule & Share Paginated Reports and Data Exports

⏰Deliver Business-Critical Insights | Provide users with essential insights precisely when they need it📃Generate, schedule, and distribute reports and data exports seamlessly.✨Leverage AI from Astrato Insights, simplify complex data into actionable summaries.

This People Analytics and Feedback demo dashboard is enhanced with essential capabilities, including writeback functionality and AI-powered sentiment analysis using Snowflake Cortex LLM & ML from Snowpark.The sentiment sheet displays drivers and keyword segmentation to provide deeper insights into employee feedback. The integration of sentiment analytics allows for a nuanced understanding of employee performance and morale.With the ability to easily update data in real-time, this dashboard is a powerful tool for tracking employee engagement, retention efforts, and growth signals, enabling proactive decision-making based on actionable insights.

Having a hard time coming up with a creative name for your signature color? Astrato's GenAI Co-pilot functionality powered by Snowflake and Mistral AI got you covered!

Smash Factor is ball speed divided by club speed. The higher the smash factor the better the energy transfer. A golfer would hope to achieve a smash factor near 1.50 on driver shots.Calculate your smash factor in this interactive data app

Ask complex questions, get simple answers quickly, without sharing data ⚡Business users ask really challenging questions of their data, LLMs can retrieve answers to those questions superfast. Bring the AI to the data, build powerful Data Apps with AI, using Astrato & Snowflake.

Select a customer from the table and update their MRR (monthly recurring revenue) to model their risk of churn. No-code modeling, without using data scientists time. Seamlessly build action into analytics and integrate with other tools using the power of Snowflake and Snowpark.How to:

1. Select a customer ID using the button in the first column of the table
2. Update the subscription value (be sure that it differed from the original subscription value)
3. Click on the calculate risk button

Load data right into Snowflake using this Astrato Data App demo. Reading directly from APIs into Snowflake, presented and initiated, right in Astrato. This Astrato Data App brings you data on demand, froom the Zoopla API, searching the UK property market in real time, storing the data in Snowflake and visualising it instantly in Astrato.Built for you, by Snowflake Data Superhero & Astrato Senior Product Manager, Piers Batchelor.See the full blog, with code: https://medium.com/snowflake/python-data-ingestion-with-snowpark-in-5-steps-d5dbd305ad69

This tool isn't just a dashboard; it's a strategic asset for proactive churn management, making it indispensable for any subscription-based business focused on sustainable growth.Maximize your revenue retention with our cutting-edge Churn Subscription Modeling Data App. Designed for meticulous subscription management, this dynamic tool offers real-time insights into Monthly Recurring Revenue (MRR), customer counts, and Average Revenue Per User (ARPU), all while tracking changes month over month.Visualize MRR distribution across churn risk buckets and gauge churn risk by account manager with our intuitive graphs. The app highlights crucial metrics like Risk Adjusted MRR and Preserved MRR, ensuring you can quickly pivot strategies to bolster customer retention.

This "Snowpark for Python" powered data app runs budget allocations against a linear regression model to predict future ROI (Return On Investment) of variable advertising spend budgets across multiple channels including search, video, social media, and email using Snowpark for Python and scikit-learn.

Astrato makes visualizing financial data easy and accessible to business users, offering meaningful insights from complex financial statements. Intuitive dashboards, insightful visualizations, and cloud-native analytics not only enhance the quality of the BI but the capacity for collaboration in an organization.

This app uses an input form and Snowpark to deliver advanced forecasting. Simply enter the product category and measure that you want to forecast, along with the time frame you are looking for.Behind the scenes, a Snowpark UDTF with the forecasting procedure 'Prophet' (by Facebook) calculates an advanced forecast. Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

The Seattle Housing Market Dashboard displays data for homes sold in Seattle, Washington, USA between August and December 2022. The dashboard showcases Astrato’s Input form functionality, and enables the user to predict a price of their desired home based on their requirements, using a sophisticated filtering option.The Price Prediction Sheet uses a statistical model to predict a home's price based on its attributes, such as number of bedrooms, bathrooms, Square Feet (Sqft) and Zip code or Region. The user enters their requirements into the input form, and predicts the price based on its attributes. This feature was accomplished using Python.The dashboard will also display similar homes (the same number of bedrooms, with ±1 bathroom, ±500 sqft, and in the same zip code or region.) This feature was created with SQL. The Supplemental Data Sheet allows the user to explore different regions and get a sense of how house prices and availability have changed over time.