In a world driven by the rapid evolution of artificial intelligence, organizations face formidable challenges. These hurdles encompass data preparation, managing large datasets, ensuring data quality, and the time-consuming nature of analytical processes. This article explores how industry experts are addressing these complexities and offers insights into streamlining data analytics.
The landscape of artificial intelligence is marked by its transformative power, yet this potential is often hindered by the practical challenge of navigating the complexities of analytics processes. Deborah Leff, Chief Revenue Officer at SQream, notes the prevalent issue of extended waiting times associated with running complex queries.
"But you get to some really complex processes where now you’re waiting hours, sometimes days or weeks for something to finish and get to a specific piece of insight," says Leff.
In a recent VB Spotlight event, Leff, along with William Benton, Principal Product Architect at NVIDIA, and data scientist/journalist Tianhui “Michael” Li, discussed overcoming common obstacles in leveraging enterprise-level data analytics. The key focus was on the imperative role of powerful Graphics Processing Units (GPUs) in enhancing analytics processes' speed, efficiency, and overall capabilities.
Evolution of Enterprise Analytics: The Need for a Paradigm Shift
Despite the excitement surrounding generative AI, enterprise-level analytics has not witnessed a parallel evolution. Benton highlights the persistence of outdated architectures in tackling analytics problems.
"Databases have had a lot of incremental improvements, but we haven’t seen this revolutionary improvement that impacts everyday practitioners, analysts and data scientists," Benton points out.
Leff underscores the time-intensive nature of analytics processes, citing the expense and complexity associated with adding more hardware and compute resources in the cloud. The solution lies in combining the computational power of Central Processing Units (CPUs) and GPUs.
"The GPU you can buy today would have been unbelievable from a supercomputing perspective 10 or 20 years ago," Benton remarks.
Accelerating the Data Science Ecosystem: The GPU Advantage
The rise of unstructured and ungoverned data lakes, often built around the Hadoop ecosystem, presents an alternative to traditional data warehouses. SQream addresses the preparation challenges by leveraging the power and high throughput capabilities of GPUs.
"The power of GPUs allows them to analyze as much data as they want," says Leff. "You completely unlock that because of GPUs."
Nvidia's RAPIDS, an open-source suite of GPU-accelerated data science and AI libraries, contributes to scaling performance across data pipelines. This is achieved by harnessing massive parallelism to accelerate Python and SQL data science ecosystems.
Unlocking New Levels of Insight: The Significance of Speed
Beyond just speeding up individual steps of the analytics process, the focus is on enhancing overall communication and feedback loops. Sub-second response speeds enable immediate answers, fostering a continuous flow state for data scientists.
"Going into sub-second response speeds means answers are returned immediately, and data scientists stay in the flow state, remaining as creative and productive as possible," Benton emphasizes.
By democratizing access to acceleration technologies like SQream, organizations experience a paradigm shift. Leaders can now challenge assumptions about what is possible, driving faster decision-making processes and unlocking new avenues for business growth.
"We need to set the bar a little higher. We need to say, hey, I used to think this wasn’t possible because this query didn’t complete after two weeks. Now it completes in half an hour. What should I be doing with my business?" Benton urges.
In conclusion, the article sheds light on how the integration of powerful GPUs is revolutionizing data analytics, making previously intricate and time-consuming processes more accessible and efficient. The democratization of acceleration technologies signals a transformative era for organizations, enabling them to harness the full potential of their data for informed decision-making.
