The formidable transformative capability of artificial intelligence is hindered by a significant real-world challenge: not only the complexity of analytical processes but also the extensive time required to transition from executing a keyword search to accessing the desired information.
Deborah Leff, a seasoned revenue agent at SQream, highlights the issue, stating, “While working with interfaces that include some inherent delays, one encounters highly intricate procedures where the waiting period can extend to days or even weeks before obtaining specific information.”
During the recent VB Spotlight event, Leff was accompanied by William Benton, the primary product architect at NVIDIA, and Tianhui “Michael” Li, a data scientist and journalist, to discuss strategies for organizations of all sizes to surmount the common hurdles in harnessing the potential of enterprise-level data analytics. They emphasized the significance of investing in cutting-edge GPUs to bolster the speed, efficiency, and capabilities of analytics processes, ushering in a transformative shift in how businesses approach data-driven decision-making.
The Evolution of Venture Analytics
While there is a surge of excitement surrounding theoretical AI and its already substantial impact on organizations, enterprise-level analytics have also undergone significant advancements over a similar timeframe.
Benton notes, “Many individuals are tackling analytical challenges using conventional methods. Although databases have seen incremental enhancements, we have yet to witness a revolutionary breakthrough that profoundly affects everyday practitioners, analysts, and data scientists, similar to the impact of AI on perceptual problems.”
Leff adds, “The primary hurdle lies in the considerable time investment required, and solutions to these challenges have traditionally been costly.”
“Augmenting hardware and evaluating resources on-site incur expenses and complexity,” she explains. A blend of intelligence (the CPU) and power (GPUs) is essential.
“The current NVIDIA GPUs offer computing capabilities that would have been deemed extraordinary a decade or two ago,” Benton remarks. “Previously reserved for high-performance computing tasks like weather modeling and complex simulations, these substantial computing capacities can now be harnessed for diverse applications.”
Rather than merely optimizing queries for marginal time savings, organizations can significantly reduce the overall duration of the analytics process, from inception to completion, thereby supercharging data ingestion, querying, and presentation frequency.
Innovative solutions like SQream, which integrate GPUs and CPUs to revolutionize analytics processing, leverage immense computational power to derive actionable insights. The impact is monumental.
Empowering the Data Exploration Community
Conventional data warehouses have given way to unstructured and unregulated data lakes, often centered around the Hadoop ecosystem. While these platforms are versatile and capable of accommodating vast amounts of semi-structured and unorganized data, they require unique preparation before analysis. SQream has harnessed the power and enhanced throughput capabilities of GPUs to expedite data processing across the entire pipeline, from data preparation to insights generation, addressing this challenge effectively.
According to Leff, “The potency of GPUs allows for the analysis of extensive datasets. While our system may not handle unlimited data, the potential is immense. However, constraints must be applied. Processing a billion rows with a thousand columns is impractical; hence, data must be sampled and summarized to a manageable scale. GPUs enable this process seamlessly.”
By capitalizing on the massive parallelism achievable today and empowering organizations to accelerate Python and SQL data analysis ecosystems, RAPIDS, Nvidia’s suite of open-source GPU-accelerated data science and AI libraries, drives efficiency gains of unprecedented scale across data pipelines.
Exploring New Horizons of Knowledge
Benton emphasizes, “Acceleration isn’t limited to specific stages of the process alone.”
Delays in processes stem from inter-organizational communication and human interaction. Addressing these inefficiencies leads to accelerated insights. By optimizing user-computer interactions to reduce latency, substantial performance enhancements can be achieved.
When response times reach sub-second levels, immediate feedback empowers data scientists to be more innovative and efficient. Extending this concept across the organization enables business leaders to make informed decisions swiftly, driving revenue growth, cost reduction, and risk mitigation.
“By combining the cognitive capabilities of CPUs with the raw power of GPUs, organizations can now tackle previously daunting or time-consuming queries instantly, unlocking a realm of possibilities,” Leff asserts.
She elaborates, “This paradigm shift is truly game-changing. People have been limited by their existing knowledge. If the IT team states that retrieving certain information will take eight days, that timeline is accepted—even at the executive level—despite the potential for quicker insights.”
Benton underscores the need to break free from entrenched patterns, stating, “We have been operating under assumptions based on outdated limitations. With advancements like those offered by SQream, we need to reassess our software design and raise the bar. Tasks that previously took weeks can now be completed in minutes. This prompts a critical question: What strategic decisions should we be making now that were previously unattainable?”
