Systems | Development | Analytics | API | Testing

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.

Our new feature helps you implement Type 2 slowly changing dimensions for your historical database analytics with no coding needed.

The terms embedded analytics and business intelligence (BI) are often used interchangeably. Both help you find key insights from complex datasets. But are they really the same thing? Is there any major difference between embedded analytics software and a business intelligence platform? In this post, you will find all the details.

Every large enterprise organization is attempting to accelerate their digital transformation strategies to engage with their customers in a more personalized, relevant, and dynamic way. The ability to perform analytics on data as it is created and collected (a.k.a. real-time data streams) and generate immediate insights for faster decision making provides a competitive edge for organizations.

Google recently announced that the current Google Analytics 3 (Universal Analytics) will come to an end in July 2023 (now extended until October 2023) and they’ve encouraged all current users to start using the new GA4. Google Analytics 4 is the new version of the current GA reporting portal that current users have used to analyse the performance of their sites.

How are people discovering your Ecommerce business? What factors make a new buyer most likely to become a recurring customer? Why did a best-selling product on your Ecommerce website suddenly plunge in popularity? The answers to these questions and more can be found when you transform Ecommerce data to extract the insights hidden within. Of course, to do so, you’ll need a powerful, robust, and production-ready ETL and data integration platform like Integrate.io.

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.