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The integration of graph databases with graph analytics and Machine Learning (ML) is an ongoing trend in the graph database market. By leveraging graph analytics, organizations can uncover intricate patterns, identify anomalies, and extract valuable insights from complex relationships within their data. The integration of ML further enhances these capabilities, enabling predictive modeling and automated decision-making based on graph-based patterns. This trend empowers businesses to make data-driven decisions, enhance customer experiences, detect fraud, and optimize operations, making graph databases a crucial component for advanced analytics & AI-driven applications.
Leading providers offer DBaaS options, making it increasingly accessible to a wide range of businesses seeking the benefits of graph databases without the management overhead. Database-as-a-service startups, such as EdgeDB, Aiven, Compose, and Tesora Corp., are fostering the market growth.