How to Connect Multiple Data Sources in Snowflake Without Slowing Down

Glean and Snowflake Intelligence are teaming up to help teams get clear answers and useful insights by combining governed data from Snowflake with company knowledge from different sources indexed in Glean. Learn more: https://lnkd.in/e8Hviyj9

Transform Your Analytics Performance with Snowflake Materialized Views If your dashboards take too long to load or your queries keep repeating — you’re losing time and compute power. Here’s how Snowflake Materialized Views, change the game: Key Benefits: ⚡ Instant Insights – Dashboards and recurring reports load in a flash with precomputed query results. 🔄 Always Up-to-Date – Data auto-refreshes, so your insights stay accurate in real time. 💰 Cost Efficiency – Reduced compute usage means lower costs and higher ROI. 📈 Performance Boost – Smarter query execution = faster decision-making. 🔧 Seamless Implementation – CloudLabs ensures smooth setup, optimization, and ongoing management. 🌐 Accelerate your analytics with Snowflake, empowered by CloudLabs. . . . #SnowflakePartner #CloudLabs #DataPerformance #DataAnalytics #BusinessIntelligence #Snowflake #DataOptimization #CloudComputing #FasterInsights #DataEngineering #DataEfficiency

Supercharge your Snowflake queries with Materialized Views! ⚡ Mastering Materialized Views in Snowflake can revolutionize your data insights process, helping you retrieve information faster and reduce compute time. These views allow you to precompute and store complex query results, enabling subsequent queries to access pre-aggregated data instantly instead of recalculating from raw tables each time. This optimization significantly reduces latency and costs, especially for heavy analytics workloads with repetitive patterns. By strategically designing your materialized views based on common joins, filters, or aggregations, you can ensure rapid dashboard updates and reports with minimal developer effort. For developers focused on optimizing performance and expediting time-to-insight, embracing materialized views is among the most intelligent strategies to implement in 2025.

Ever wonder why Snowflake is so fast at analytical queries? 🤔 It all comes down to how data is stored: columnar vs row-based storage 🗃️➕🧮 it’s one of the main reasons Snowflake delivers blazing-fast insights. Traditional row-based databases store data row by row: meaning all the fields of one record are stored together. Great for transactional workloads where you want to pull complete records quickly, but can be inefficient when you only need a few columns of a huge table. Columnar storage used by Snowflake takes a different approach: Data is stored by columns, grouping similar data types together 🏷️ Reads only the columns required for your query, minimizing I/O and speeding things up ⚡ Leverages advanced compression techniques, shrinking storage and costs 💾💰 Excels at heavy analytical workloads: aggregates, filtering, and complex joins 🔍✂️ Scales seamlessly and keeps queries lightning-fast even on massive datasets 🚀 So next time you dive into big data analytics, remember how columnar storage smartly streamlines the heavy lifting, turning vast datasets into clear insights with speed and efficiency #Snowflake #ColumnarStorage #DataWarehouse #Analytics #DataEngineering #CloudData #BigData #SQL

What if implementing SCD Type 2 in Snowflake could actually be simple? We all used to spend way too much time dealing with streams and tasks until we got Dynamic table. In my latest YouTube video, I walk you through a step-by-step approach to SCD Type 2 using only Dynamic Tables. No streams, no tasks just clear, straightforward logic you can use right away. Here’s what’s inside: 1- Real-world examples 2- Easy-to-follow explanations for every step 3- Practical tips you can apply to your data projects today If you’re ready to make your data engineering workflows smoother and easier to understand, give it a watch and tell me what you think! Video link: https://lnkd.in/dQktSGcT Learn Dynamic tables in details here : https://lnkd.in/dYRQ-uBA Let’s make advanced data engineering a little less intimidating together. #dataengineering #snowflake #dynamictables #cloudlearningyard

🚀 Transform your raw data into actionable intelligence! Our new tutorial dives into Building Data Marts with dbt and Snowflake, the essential practice for modern Analytics Engineering. Learn how to: ✅ Shape raw data into valuable insights. ✅ Connect business logic to clean, trusted data. ✅ Leverage dbt's incremental models and Snowflake's power for efficiency. ✅ Build modular, high-performance data pipelines. A well-built data mart is where true analytics begins. Master this critical skill to empower your BI teams! Watch the full tutorial now: https://lnkd.in/eFBVqkJ6 #DataMarts #dbt #Snowflake #AnalyticsEngineering #DataModeling #DataWarehousing #DataEngineering #CloudData #DataMastery

This is a core concept for anyone serious about Analytics Engineering and turning raw data into trusted, accessible insights. We cover how to leverage dbt's powerful features with Snowflake to create efficient, modular data marts. From understanding business logic to implementing incremental models, this tutorial is packed with practical steps to elevate your data transformation skills. Check it out and let me know how you're building your data marts! Full guide here: https://lnkd.in/eJAiVRRH #dbt #Snowflake #DataMarts #AnalyticsEngineering #DataModeling #DataEngineering #SQLTransformations

💡 Day 6 | Snowflake Wasn’t the Hero. The Client’s Data Problem Was 🎯 One of my favorite sales calls ever started with a simple truth: “Snowflake isn’t the story, your data chaos is.” This client was drowning in reports from five different tools. Every department had a “version of truth.” They didn’t need another product demo. They needed someone to listen and connect the dots. So instead of pitching Snowflake first, I said: “Let’s talk about what’s slowing your business down.” Once we mapped their challenges, data silos, slow queries, messy pipelines, the solution naturally pointed to Snowflake. That day, I learned something big 👇 When you make the client’s pain the hero of the story, the product automatically becomes the savior. #Sales #Snowflake #StorySelling #DataEngineering #B2bSales

Anyone who has used Snowflake Intelligence will know how easy it is to use, but it turns out you can also build incredibly useful applications easily as well. This article describes how to build a Snowflake Intelligence application which helps to optimise performance and spend on your Snowflake account based on your usage patterns. Many thanks to Umesh Patel for publishing the original article. https://lnkd.in/emTfpMgy

🚀 Anomaly Detection on Snowflake – Finding the Unexpected in Your Data Even well-prepared sales data can contain hidden anomalies. With Snowflake’s native Anomaly Detection Model (ADM), you can identify outliers directly within your data platform – fast, scalable, and without moving data. Here’s what the workflow looks like in action: 📊 Training data directly from Snowflake’s sample datasets 🧠 Model creation and training with just a few SQL commands 💾 Result handling and why it can’t simply be stored as a view 🔍 Analysis to detect irregular sales patterns An efficient approach for data teams bringing AI-powered anomaly detection natively into Snowflake. #Snowflake #AnomalyDetection #MachineLearning #DataAnalytics #DataEngineering #INFOMOTION #SnowflakeSquad Ali Sayar Dawid Veltzé Daniel Eiduzzis Tobias Orzegowski Bernd Paulini Bart Wrobel Lukasz Chlipala Dr. Tina Klaus INFOMOTION GmbH