Logs are one of the three key “pillars” of observability, and cloud environments are no exception. You can’t know what’s happening in your cloud without analyzing cloud service logs, which allow you to audit and monitor workflows within your cloud. That said, cloud logging is a unique beast in certain respects.

Data integration plays a foundational role in the progression to data and analytics mastery.

From creating a single source of truth to reducing the amount of human errors and time spent building reports, here is why dashboards are important.

Qlik enhances analytics exploration in Snowflake by launching Direct Query, a new capability that allows Qlik Sense applications and dashboards to query Snowflake directly using SQL pushdown.

Marketing, sales, and growth leaders are screaming “customer is king”. There is a palpable FOMO around customer-centricity: But what we are missing is a concrete guide that showcases how to build better customer experiences. In this step-by-step guide, we will dive deep and showcase how to set up a customer experience strategy from the ground up to delight your customers.

GoPay, the fully integrated payment solution, serves over 18.000 merchants. As a payment gateway, GoPay processed over 100 million transactions, connecting merchants, retail customers, and financial institutions in a seamless payment experience across mobile, desktop, and web. Their work is challenging - on one hand, the data they work with needs to be thoroughly validated to comply with financial regulations.

In this tutorial, we’re going to build an interactive customer Churn Insights Dashboard using the open-source Python framework, Streamlit, and the Continual predictions generated in Part 1: Snowflake and Continual Quickstart Guide. In Part 1, we connected Continual to Snowflake and used a simple dataset of customer information, activity, and churn status to build and operationalize a machine learning model in Continual to predict the likelihood of a customer churning.