Presented by SQream
The rapid advancement of AI brings numerous challenges, including data preparation, managing large datasets, ensuring data quality, and coping with the inefficiencies of long-running queries and batch processes. In this VB Spotlight, William Benton, principal product architect at NVIDIA, alongside other experts, shares how organizations can streamline their analytics processes today.
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The transformative potential of AI is often hindered by the complexities of analytics and the lengthy wait for insights from queries.
“Everyone’s encountered dashboards with some latency,” notes Deborah Leff, chief revenue officer at SQream, “but with complex queries, you might wait hours, or even days, to gain key insights.”
During a recent VB Spotlight event, Leff joined Benton and data scientist Tianhui “Michael” Li to discuss how organizations can overcome barriers in enterprise-level data analytics. They emphasized the critical need for investment in powerful GPUs to enhance the speed, efficiency, and capabilities of analytics processes, paving the way for a new approach to data-driven decision-making.
Accelerating Enterprise Analytics
Despite the excitement surrounding generative AI, enterprise analytics have lagged in advancement.
“Many still approach analytics challenges with outdated architectures,” Benton explains. “Although databases have seen incremental updates, we haven't witnessed a revolutionary change that significantly benefits practitioners, analysts, and data scientists.”
This ongoing challenge stems from the significant time required for analytics, which has kept effective solutions out of reach. While adding more cloud resources is costly and complicated, an effective combination of CPU and GPU power can significantly enhance analytics performance.
Today’s GPUs would have been deemed extraordinary in the past, Benton states. “While supercomputers are used for massive scientific problems, that immense computing power can now benefit various use cases.”
Organizations no longer need to make do with minor query optimizations. Instead, they can significantly reduce the entire analytics timeline, enhancing speed in data ingestion, querying, and presentation.
“SQream and similar technologies leverage the combined power of GPUs and CPUs, revolutionizing traditional analytics processes and delivering an unprecedented impact,” Benton adds.
Revolutionizing the Data Science Ecosystem
Unstructured data lakes, often centered around Hadoop, offer flexibility for vast amounts of semi-structured and unstructured data but require extensive preparation before model deployment. SQream harnesses GPUs for accelerated data processing, significantly streamlining the workflow from data preparation to actionable insights.
“The capabilities of GPUs empower organizations to analyze massive datasets effectively,” Leff asserts. “We’ve often had to impose limits on data sizes, but GPUs enable us to unlock vast volumes of data.”
NVIDIA’s RAPIDS, an open-source suite of GPU-accelerated libraries, further enhances performance at scale across data pipelines. It taps into the power of parallel processing, boosting efficiency in the Python and SQL data science ecosystems.
Unlocking Deeper Insights
Benton highlights that improved analytics isn’t just about speed. “Slow processes often stem from communication delays across teams or even desks. On optimizing these interactions, we see significant performance gains.”
Achieving sub-second response times allows for immediate answers, keeping data scientists in a productive flow state. Extending this efficiency to various business leaders enhances decision-making processes that directly impact revenue, cost management, and risk reduction.
Harnessing the full potential of data becomes feasible with the power of CPUs and GPUs, enabling previously impossible queries.
“For me, this is the democratization of acceleration,” Leff notes. “Many decision-makers must rely on outdated assumptions. When told that an inquiry will take eight hours, they accept it, unaware that it could be processed in under eight minutes.”
Benton adds, “Many organizations cling to old paradigms established over decades. With the advancements made by technologies like SQream, we can challenge these assumptions. When a query that once took two weeks now completes in half an hour, it opens the door to explore new business possibilities.”
For further insights into the transformative power of data analytics, including cost-saving strategies and groundbreaking solutions, don’t miss this VB Spotlight.
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Agenda
- Technologies to drastically reduce time-to-market for product innovation
- Enhancing efficiencies of AI and ML systems while minimizing costs
- Improving data integrity, optimizing workflows, and maximizing data asset value
- Strategic solutions to revolutionize data analytics and drive impactful business outcomes
Speakers:
William Benton, Principal Product Architect, NVIDIA
Deborah Leff, Chief Revenue Officer, SQream
Tianhui “Michael” Li, Technology Contributor and Moderator
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