Data engineers are often seen as the builders behind the scenes, but their role is evolving rapidly. They now influence not just data flow, but also how organizations harness the power of AI and real-time insights. Understanding this shift helps tech leaders better align their teams and investments for the future. The Evolution from Data
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All posts tagged in Data Science
Handling large datasets in Python can be slow and resource-intensive with traditional tools like Pandas. Recently, Polars has gained attention for its speed and efficiency. This article compares how both libraries handle real-world data problems, highlighting the performance advantages of Polars. Why Switch from Pandas to Polars Pandas has been the go-to library for data
Creating effective investment portfolios can be complex, but using Python libraries like skfolio makes it easier to test, compare, and tune different strategies. This tutorial walks through how to load stock data, convert prices into returns, and split the data into training and testing sets. It highlights how to build baseline portfolios and evaluate their
Vector databases have become essential tools for managing high-dimensional data in AI applications. As of 2026, they are central to powering retrieval systems, semantic search, and AI workflows that rely on large embeddings. Choosing the right database can significantly impact performance and costs, making it important to understand the options available today. Leading Managed and
SAP has announced a major move in the AI world by acquiring Prior Labs, a startup based in Freiburg that specializes in tabular foundation models. The deal signals a significant push by the German enterprise giant to develop a leading AI research lab in Europe. Over the next four years, SAP plans to invest more
Working with large datasets can be tricky, especially when they are multi-gigabyte files. Instead of downloading and storing the entire dataset, this approach streams the data directly, allowing for real-time analysis of individual samples. This method saves time and resources while providing immediate insights into the dataset’s structure and content. Setting Up the Environment for
AI systems are now often built around continuous streams of data. Unlike traditional datasets that are static and collected once, financial markets provide a constant flow of new information. This is especially true for cryptocurrencies, where prices and activity levels change rapidly and unpredictably. For AI models, this ongoing data creates both challenges and opportunities
Many people think AI failures in large companies come from bad models or overhyped tech. But a leading data platform company, Boomi, says the real problem is the data behind those AI systems. They call it “data activation,” and they believe it’s the missing piece needed for AI to truly deliver value. The Real Issue:
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Spade, a New York-based company, has raised $40 million in a Series B funding round. The company plans to grow its data and AI platform for modern financial services. The funding was led by Oak HC/FT, with support from Andreessen Horowitz, Flourish, Gradient, NAventures, the corporate venture arm of the National Bank of Canada, and
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Insurance companies know that AI could transform their operations, but many struggle with outdated data systems and manual processes. A recent report highlights the biggest hurdles that prevent the industry from fully harnessing AI’s potential. Despite high optimism, actual AI adoption remains limited, mainly because of complex data issues and legacy systems. Data Fragmentation and
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