Artificial intelligence (AI) and big data technologies are driving major changes in how organizations think about business intelligence and analytics. Rather than be limited to querying and reporting on just what is in traditional BI systems or data warehouses, many business users and analysts want to tap a fuller range of data in systems running Apache Hadoop, Apache Spark, or on cloud data platforms and storage. At the same time, AI practices and technologies (in particular machine learning and natural language processing) are changing how users explore, analyze, and interact with data and the types of insights they can generate.
As companies shift their Big Data to the cloud and hybrid environments, the need for Big Data analytics and a corresponding long-term analytics strategy has become increasingly critical. Here’s your opportunity to listen to experienced Big Data practitioners articulate their best practices in building successful, long term analytics architectures.
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As companies invested in their digital transformations, one side effect was the proliferation of data environments and varying database instances. With so many systems to navigate, many organizations have fallen prey to operational disruptions and now struggle to achieve the high level of performance and scalability that their transformations promised.