With the continued evolution of big data analytics applications, there is an emerging paradigm for real-time decision making. This ability to exploit analytical models to influence behaviors within predefined time windows—by simultaneously processing massive data volumes yet getting immediate analytical results—allows businesses to leverage new opportunities to compete more effectively. The conventional approach to data warehousing with its need for staging, synchronization, and sequences of data extraction and transformation, however, elongates the process and diminishes the ability to derive fast analytical results. New architectures and strategies will be required to obtain these new results.
In this webinar, we will explore the concept of hybrid transactional/analytical processing or HTAP, an alternative architecture that allows you to simultaneously balance transactional and analytical processing. By leveraging real-time stream processing with in-memory processing, HTAP provides a platform for reporting and analytics without having to separate the data into a data warehouse.
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