2025 was a year of meaningful change. We leaned into new ideas, new conversations, and new technologies—and even opened the doors to a new office. From conferences and roundtables to product launches and celebrations, one thing never shifted: our commitment to building with purpose. As the year comes to a close, we’re grateful for the

FOR IMMEDIATE RELEASE Aretove Inc Unveils Pollinetic, an Agentic AI Analytics Workspace for Modern Data-Driven Teams New York, NY – Dec 22, 2025 – Aretove today announced the launch of Pollinetic, an Agentic AI Analytics Workspace that uses specialized AI agents to turn natural language questions into reliable, end-to-end analysis. Pollinetic connects directly to existing

Artificial Intelligence (AI) could be considered as the star of any show, right now. However, behind all the shine lies the unrecognized work of data engineers, the architects of modern AI systems. From organizing massive datasets to improving data pipelines, data engineers play a very important role in turning unorganized information into actionable insights. So,

When it comes to Machine Learning (ML), data is everything. However, if the data is not well structured, then it can never give you a clear picture. In other words, data needs to be refined and shaped into something meaningful for an algorithm to make sense of it. That transformation is what feature engineering is

In the early days of enterprise data engineering, everything revolved around large, monolithic data platforms. These all-in-one systems managed ingestion, storage, transformation, and analytics within a single framework. While these systems offered convenience initially, they soon proved to be rigid, and difficult to scale as business requirements changed. Today, as organizations deal with diverse and

Speed is power, and in 2025, edge analytics is redefining how businesses harness that power. As enterprises generate massive data streams from IoT sensors, devices, and applications, relying solely on cloud-based Business Intelligence (BI) systems is no longer enough. Traditional models are increasingly strained by bandwidth limitations, rising costs, and scalability challenges. That’s where edge

Introduction: Every year, companies face significant losses from an often-overlooked issue: data debt. It doesn’t appear in financial statements, but it drives delays, inefficiency, and higher costs across the organization. Recent research highlights the scale of the problem: • $15 million per year: The average cost organizations incur due to poor data quality (Gartner). •

For much of the past decade, data science has been seen as one of the most attractive career paths. Organizations built analytics teams, universities added specialized programs, and countless professionals reskilled to step into this growing field. The phrase “data is the new oil” captured the imagination of businesses everywhere. But with the rise of

Continual Learning in ML Systems: Why It Matters for the Future of AI Machine learning systems are often trained once, deployed, and then left static. While this approach works in controlled environments, real-world data is never static. Customer behavior shifts, fraud tactics evolve, and industries face new regulations. In such cases, a model that isn’t