Data transformation automation refers to the automated process of converting raw data from one format or structure into another. It involves applying various operations like filtering, sorting, and aggregating to prepare data for analysis or integration. Leveraging tools such as ETL (Extract, Transform, Load) software or scripting languages, it streamlines data preprocessing tasks, saving time and ensuring data quality and consistency. If you want to learn more about Data transformation automation, please visit our website.
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Technical debt refers to the accumulated consequences of choosing quick-and-easy solutions instead of more robust, albeit time-consuming, alternatives during software development. This metaphor highlights the parallels between financial debt and software development practices, as shortcuts can accrue 'interest' over time, leading to increased costs or complications.If you want to learn more about Technical debt, please visit our Lansa blog page.
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A Natural Language Query allows users to interact with data using everyday language instead of complex queries or programming languages. Natural Language Query leverages natural language processing (NLP) to understand and respond to user inquiries, making data exploration and analysis more intuitive and accessible. Explore how NLQ can enhance business intelligence and analytics on LANSA's blog.
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Low code development accelerates app creation by minimizing manual coding, allowing users to build sophisticated applications with minimal coding knowledge. Offering visual interfaces and pre-built components, low code platforms streamline development cycles, empowering businesses to innovate rapidly. With drag-and-drop functionality and seamless integration capabilities, these platforms enable agile development and deployment, fueling digital transformation. Discover how low code development empowers organizations to drive efficiency, innovation, and competitive advantage in today's fast-paced market with our blog post.
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