Today's data environments are highly diverse—residing on many platforms and requiring a variety of approaches to ensure data resiliency and availability. Delivering technology alone will not be enough in 2023. To help make the process of identifying useful products and services easier, each year, DBTA presents a list of Trend-Setting Products. These products, platforms, and services range from long-established offerings that are evolving to meet the needs of their loyal constituents, to breakthrough technologies that may only be in the early stages of adoption.
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AI is advancing at a faster clip this year with the debut of several technologies boosting its usage. Generative Pre-trained Transformer (GPT), for example, is the large-scale natural language technology that uses deep learning to produce human-like text. AI expert and Erudit's chief science officer and co-founder, Ricardo Michel Reyes, discussed the growing use of AI in programming and whether or not its advancement is something that programmers should fear when it comes to their job security.
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Observability by definition is a measure of how well internal states of a system can be inferred from knowledge of its external outputs. In other words, a system's behavior is determined from its outputs—information obtained by certain measures. Though observability as a term has been used for many years, the modern understanding originates in software observability, which is the ability to collect data about program execution, internal states of modules, and communication between components.
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While the emerging constellation of next-generation data architectures—fabric, mesh, and cloud—is extremely appealing, it's still full of unknowns. These approaches present opportunities for greater data democratization, but also increased complexity. Understanding the distinctions between data fabric and mesh are also important before moving to this architecture.
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