Systems | Development | Analytics | API | Testing

Building Cost-Effective, Real-Time Pipelines with Snowflake Dynamic Tables

Join Sales Engineer Gabriel Mullen as he demonstrates how Snowflake’s Dynamic Tables streamline real-time data pipelines. Discover a simple, declarative approach to ingest data incrementally, maintain cost efficiency, and keep insights fresh. This demo will walk through setting target lag, leveraging incremental refreshes, and automating orchestration, allowing you to power analytics and BI dashboards with minimal overhead and maximum performance.

Cost Aware Data Engineering: Designing Snowflake ETL Pipelines for Maximum Efficiency

Are your Snowflake ETL pipelines silently draining your budget? With 80% of data management experts struggling to accurately forecast cloud costs (Forrester), the efficiency of your ETL processes is more crucial than ever. Join us for this session in our Weekly Walkthrough drop-in series, "Controlling Cloud Costs," where we'll explore how to optimize your Snowflake ETL pipelines for cost and performance.

#shorts - Maximize Customer Engagement with #Qlik #data #analytics #automation

With a powerful Qlik Customer Profile Dashboard and Qlik App Automation, you can instantly identify key segments, and immediately target the right customers with personalized offers—all in just a few clicks! This means better outreach, higher sales, and improved customer retention without the guesswork and multiple tools. Ready to level up your customer strategy? Let’s dive in! Follow, engage, repost for more insights! hashtag#Qlik.

Data Quality Monitoring: Enabling Reliable, High-Integrity Data

In this demo, we’ll show you how to create a custom Data Metric Function (DMF), associate it with your tables for continuous data quality monitoring, and query the results from a centralized table. Watch to learn how built-in monitoring helps you track critical data objects, identify quality issues, and take quick action to ensure reliable, high-integrity data across your organization.

What is Real Estate Data Analytics? - A Complete Beginner's Guide

Real estate data analytics refers to specific software solutions that help real estate and property management businesses gather operational data from various sources into one user interface, analyze key metrics to gain insights into their markets, such as property trends, demographics, and economic indicators, and make data-led decisions regarding buying, selling, renting, or managing properties.