ºÝºÝߣshows by User: KeithKraus / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: KeithKraus / Tue, 23 Oct 2018 12:54:14 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: KeithKraus RAPIDS: GPU-Accelerated ETL and Feature Engineering /slideshow/rapids-gpuaccelerated-etl-and-feature-engineering/120435302 gtcisraelkkraus-181023125414
The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.]]>
The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.]]> Tue, 23 Oct 2018 12:54:14 GMT /slideshow/rapids-gpuaccelerated-etl-and-feature-engineering/120435302 KeithKraus@slideshare.net(KeithKraus) RAPIDS: GPU-Accelerated ETL and Feature Engineering KeithKraus The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gtcisraelkkraus-181023125414-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
RAPIDS: GPU-Accelerated ETL and Feature Engineering from Keith Kraus
]]> 2458 4 https://cdn.slidesharecdn.com/ss_thumbnails/gtcisraelkkraus-181023125414-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 GPU-Accelerating UDFs in PySpark with Numba and PyGDF /slideshow/gpuaccelerating-udfs-in-pyspark-with-numba-and-pygdf/93472365 pygdfudfanacondacon-180410182441
With advances in computer hardware such as 10 gigabit network cards, infiniband, and solid state drives all becoming commodity offerings, the new bottleneck in big data technologies is very commonly the processing power of the CPU. In order to meet the computational demand desired by users, enterprises have had to resort to extreme scale out approaches just to get the processing power they need. One of the most well known technologies in this space, Apache Spark, has numerous enterprises publicly talking about the challenges in running multiple 1000+ node clusters to give their users the processing power they need. This talk is based on work completed by NVIDIA’s Applied Solutions Engineering team. Attendees will learn how they were able to GPU-accelerate UDFs in PySpark using open source technologies such as Numba and PyGDF, the lessons they learned in the process, and how they were able to accelerate workloads in a fraction of the hardware footprint.]]>
With advances in computer hardware such as 10 gigabit network cards, infiniband, and solid state drives all becoming commodity offerings, the new bottleneck in big data technologies is very commonly the processing power of the CPU. In order to meet the computational demand desired by users, enterprises have had to resort to extreme scale out approaches just to get the processing power they need. One of the most well known technologies in this space, Apache Spark, has numerous enterprises publicly talking about the challenges in running multiple 1000+ node clusters to give their users the processing power they need. This talk is based on work completed by NVIDIA’s Applied Solutions Engineering team. Attendees will learn how they were able to GPU-accelerate UDFs in PySpark using open source technologies such as Numba and PyGDF, the lessons they learned in the process, and how they were able to accelerate workloads in a fraction of the hardware footprint.]]> Tue, 10 Apr 2018 18:24:41 GMT /slideshow/gpuaccelerating-udfs-in-pyspark-with-numba-and-pygdf/93472365 KeithKraus@slideshare.net(KeithKraus) GPU-Accelerating UDFs in PySpark with Numba and PyGDF KeithKraus With advances in computer hardware such as 10 gigabit network cards, infiniband, and solid state drives all becoming commodity offerings, the new bottleneck in big data technologies is very commonly the processing power of the CPU. In order to meet the computational demand desired by users, enterprises have had to resort to extreme scale out approaches just to get the processing power they need. One of the most well known technologies in this space, Apache Spark, has numerous enterprises publicly talking about the challenges in running multiple 1000+ node clusters to give their users the processing power they need. This talk is based on work completed by NVIDIA’s Applied Solutions Engineering team. Attendees will learn how they were able to GPU-accelerate UDFs in PySpark using open source technologies such as Numba and PyGDF, the lessons they learned in the process, and how they were able to accelerate workloads in a fraction of the hardware footprint. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pygdfudfanacondacon-180410182441-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> With advances in computer hardware such as 10 gigabit network cards, infiniband, and solid state drives all becoming commodity offerings, the new bottleneck in big data technologies is very commonly the processing power of the CPU. In order to meet the computational demand desired by users, enterprises have had to resort to extreme scale out approaches just to get the processing power they need. One of the most well known technologies in this space, Apache Spark, has numerous enterprises publicly talking about the challenges in running multiple 1000+ node clusters to give their users the processing power they need. This talk is based on work completed by NVIDIA’s Applied Solutions Engineering team. Attendees will learn how they were able to GPU-accelerate UDFs in PySpark using open source technologies such as Numba and PyGDF, the lessons they learned in the process, and how they were able to accelerate workloads in a fraction of the hardware footprint.
GPU-Accelerating UDFs in PySpark with Numba and PyGDF from Keith Kraus
]]> 3435 5 https://cdn.slidesharecdn.com/ss_thumbnails/pygdfudfanacondacon-180410182441-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 ASGARD Splunk Conf 2016 /slideshow/asgard-splunk-conf-2016/73190046 splunkconf2016-170315213422
ASGARD Splunk Conf 2016]]>
ASGARD Splunk Conf 2016]]> Wed, 15 Mar 2017 21:34:22 GMT /slideshow/asgard-splunk-conf-2016/73190046 KeithKraus@slideshare.net(KeithKraus) ASGARD Splunk Conf 2016 KeithKraus ASGARD Splunk Conf 2016 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/splunkconf2016-170315213422-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ASGARD Splunk Conf 2016
ASGARD Splunk Conf 2016 from Keith Kraus
]]> 364 3 https://cdn.slidesharecdn.com/ss_thumbnails/splunkconf2016-170315213422-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph and Blazegraph /KeithKraus/streaming-cyber-security-into-graph-accelerating-data-into-datastax-graph-and-blazegraph stratanyc-161004192604
Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense. Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.]]>
Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense. Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.]]> Tue, 04 Oct 2016 19:26:04 GMT /KeithKraus/streaming-cyber-security-into-graph-accelerating-data-into-datastax-graph-and-blazegraph KeithKraus@slideshare.net(KeithKraus) Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph and Blazegraph KeithKraus Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense. Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/stratanyc-161004192604-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense. Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph and Blazegraph from Keith Kraus
]]> 1219 4 https://cdn.slidesharecdn.com/ss_thumbnails/stratanyc-161004192604-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/gtcisraelkkraus-181023125414-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/rapids-gpuaccelerated-etl-and-feature-engineering/120435302 RAPIDS: GPU-Accelerate... https://cdn.slidesharecdn.com/ss_thumbnails/pygdfudfanacondacon-180410182441-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/gpuaccelerating-udfs-in-pyspark-with-numba-and-pygdf/93472365 GPU-Accelerating UDFs ... https://cdn.slidesharecdn.com/ss_thumbnails/splunkconf2016-170315213422-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/asgard-splunk-conf-2016/73190046 ASGARD Splunk Conf 2016