Like peanut butter and jelly, ETL and data modeling are a winning combo. Data modeling can't exist without ETL, and ETL can't exist with data modeling. Not if you want to model data properly. Combining the two defines the rules for data transformations and preps data for big data analytics. In the age of big data, businesses can learn more than ever about their customers, identify new product opportunities, and so on.

Data integration is essential for any competitive business. The ability to sync all your data from disparate sources powers better insights, analysis, and ultimately faster business decision-making. Change data capture (CDC) is one element of data integration that focuses on keeping data accurate with near real-time updates as soon as data within a data source changes.

Are you ready to start exploring the world of API development with Django? In this tutorial, we will begin an exploration of how to leverage Django together with the Django REST framework, a robust and versatile framework for creating efficient and scalable APIs. This guide is designed to provide you with a clear and structured approach to Django REST API, ensuring that both beginners and experienced developers can navigate and utilize this powerful tool with ease.

Microsoft SQL is a popular relational database management system organizations use to manage and store data. As data volumes grow, extracting, transforming, and loading data has become essential for many businesses.

Built with BigQuery, Live Ramp Safe Haven enables cross-channel marketing analytics to help customers execute media campaigns.

GenAI depends on data maturity, in which an organization demonstrates mastery over both integrating data – moving and transforming it – and governing its use.

As software testers, we are not just cogs in a machine, executing tests, writing reports, and meeting deadlines. We are professionals who can make a difference in the world. We can find purpose in our testing and use it as a guiding light through the challenges we face. Kristel Kruustük, the founder of Testlio, is living proof that finding a bigger sense of meaning and purpose can transform not only our work but also our lives.

The misconception that product led growth implies a business neglects sales couldn’t be further from the truth. In reality, product led growth (PLG) involves integrating sales later in the customer journey, placing the purchasing power back in the hands of your users. This self-service approach is highly effective, especially when paired with insightful user data to streamline and target growth. For AI-centric companies, this product-led strategy serves as a fundamental pillar for success.

Recently we introduced one breaking change in how user information is exported from Countly, and we wanted to explain why such change was made and what to do to keep your plugins supported. But before we dive into why's, let's first reiterate the "what" part.

Commercializing an AI-based product requires turning technology into a marketable product, navigating challenges from development to market entry. Appealing to enterprise buyers is crucial for sustainable, continuous growth, as their interest not only validates your product’s value but also lays the foundation for long-term success and scalability. More specifically, the larger the businesses of your potential customers are, the more of a monopoly your product likely has in the market.