Joshua Patterson

Business leaders looking for that next competitive edge should consider GPU acceleration.

Whether used to speed big data processing or prepare for a future of AI-related technologies, the opportunities for generating better outcomes through GPUs are a smart bet.

Read more about how GPU solutions can power growth and how to map out your next steps.

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… Show more

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. Show less

Organizations routinely monitor user accounts in an attempt to improve their security posture.

However, it can be difficult to collect the right amount of data—too little data, and the company cannot effectively inform security professionals’ decisions; too much, and the company quickly reaches data overload. The proverbial “needle in a haystack” soon follows.

Read on as we explain how leading enterprises are taking a more strategic and systematic approach to this security… Show more

Organizations routinely monitor user accounts in an attempt to improve their security posture.

However, it can be difficult to collect the right amount of data—too little data, and the company cannot effectively inform security professionals’ decisions; too much, and the company quickly reaches data overload. The proverbial “needle in a haystack” soon follows.

Read on as we explain how leading enterprises are taking a more strategic and systematic approach to this security challenge. Show less

As companies go digital and leverage exponentially larger big data sources, they must apply a new logic to how they manage their data supply chain—from ingestion to insights. Yesterday’s model of data warehousing and periodic reporting is a slow, linear approach; the future model is nimble and multi-faceted, supplying the right data to companies and executives in real time.

But having instant access to the necessary data is not enough. The data must also be presented in a visually… Show more

As companies go digital and leverage exponentially larger big data sources, they must apply a new logic to how they manage their data supply chain—from ingestion to insights. Yesterday’s model of data warehousing and periodic reporting is a slow, linear approach; the future model is nimble and multi-faceted, supplying the right data to companies and executives in real time.

But having instant access to the necessary data is not enough. The data must also be presented in a visually appealing, meaningful way that people can quickly understand. This emerging field is called data visualization. With it, companies can turn raw data into meaning and ultimately into understanding to make better, faster decisions about strategy, operations, supply chains, logistics and more. Show less

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