Cascading is a platform used to develop big data applications on Hadoop. It provides a computational engine, system integration framework, data processing, and programming capabilities. One of the key benefits of cascading is that it provides portability to development teams, so they can move existing applications without incurring the cost of rewriting. Cascading applications can run on different platforms such as MapReduce, Apache Thesis and Apache Flink and link between the platforms.
Data Observability is the ability to monitor, diagnose, and manage the quality of data throughout the data lifecycle. It is also the discipline to automatically find out the health of your data and solve problems as soon as possible.
Augmented analytics is an approach that automates and improves data analysis using advanced technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP).
Especially in machine learning and natural language processing (NLP) projects, data is often represented as numerical vectors. At this point, traditional databases may be insufficient to manage vector-based data. This is where Vector Database (Vector DB) comes into play.
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