This post is co-authored by Martin Lee, Armin Pelkmann, and Preetham Raghunanda.
Cyber security analysts tend to redundantly perform the same attack queries with different input data. Unfortunately, the search for useful meta-data correlation across proprietary and open source data sets may be laborious and time consuming with relational databases as multiple tables are joined, queried, and the results inevitably take too long to return. Enter the graph database, a fundamentally improved database technology for specific threat analysis functions. Representing information as a graph allows the discovery of associations and connection that are otherwise not immediately apparent.
Within basic security analysis, we represent domains, IP addresses, and DNS information as nodes, and represent the relationships between them as edges connecting the nodes. In the following example, domains A and B are connected through a shared name server and MX record despite being hosted on different servers. Domain C is linked to domain B through a shared host, but has no direct association with domain A.
This ability to quickly identify domain-host associations brings attention to further network assets that may have been compromised, or assets that will be used in future attacks.
“This conference is designed not only to make you think about the application of automation, but also to help you take action” -- David Greenfield, Automation World, editor in chief and TAC event director
The conference achieved this goal and more. The framework of the sessions encouraged audience collaboration and dialogue around the challenges and practical steps and strategies being designed and deployed to achieve an integrated and scalable IoE architecture that drives value across the entire manufacturing value chain, as depicted in the video below:
What better way to meet that objective than to leverage a manufacturing use case around beer!!!!
Automating Brewing Operations from Two Different Perspectives
I attended this session where Highland Brewing, Sierra Nevada and Vicinity Manufacturing gave an interesting perspective around the challenges and strategies in deploying their next generation manufacturing operation.
Highland Brewing is a regional brewer of craft beers based in the Southeast and Sierra Nevada is a larger brewer with more of national brand. The interesting contrast between the two is that Highland Brewing is designing more automation into their operational facility and Sierra Nevada is scaling their automation and IoE strategies across all their facilities. Both perspectives and approaches have the same objective. How do I effectively integrate all the various technologies into an intelligent, flexible and scalable system/architecture to meet the following business outcomes:
Increase Customer Loyalty
Supply Chain Optimization
To paraphase Kevin Wheeler, Director of Operations, Highland Brewing Co,“Our core competency is crafting great beer. We have an opportunity to drive efficiency into our operation by an integrating IoT/IoE platform … the challenge is figuring out the best approach.”
Like Highland Brewing, manufacturers must begin to transform existing business processes and fundamentally rethink how they create, operate, and service smart, connected products in the IoE. For those that get it right, the future represents a huge opportunity to create product and service advantages.
Are you having challenges putting together the “IoE technology puzzle?” Is security the main barrier to IoE adoption?
Introducing New Adapter for Cisco UCS Manager, Lightweight Java Client, Multi-domain AD/LDAP Authentication & Runtime User Support for TES Workgroups
Managing and automating mission-critical business process is key to the success of your enterprise. By managing the delivery of the right data to the right application at the right time you can ensure that everyone in your organization has the information they need to be successful.
End-to-end Workload Management with Tidal Enterprise Scheduler (TES) 6.2
Adapter for Cisco UCS Manager The Enterprise Adapter for UCS Manager allows users to schedule UCS Manager component infrastructure jobs through Cisco TES so that users can leverage the scheduler’s capabilities to automate, simplify, and improve job scheduling and workload performance. The UCS Manager Adapter integrates with UCS Manager using the XML API and provides for the automation of UCS Manager activities for blade and rack-mount server management in the form of UCS Manager jobs. This allows you to control and manages server instances as part of an overall infrastructure and data processing workload management solution.
Job Definitions for Cisco UCS Manager
Lightweight Java Client For power users who are managing thousands of workloads and their associated objects in their database, the Java client syncs data directly from the Master, but it is many times faster than the client manager because all data is stored in-memory on the Java VM rather than to an external database. Many interactions through the Java client will see marked increases in performance including smooth scrolling with zero latency and faster search and filtering.
Lightweight Java Client for TES 6.2
Flexible Security Feature For large enterprises that segment globally or for any user who creates domains for their data center, Cisco TES now supports multi-domain coverage from a single client manager allowing greater flexibility and ease of use. And for greater runtime flexibility, Cisco TES allows users to associate runtime users to workgroups to be used while defining the workloads.
Data in data warehouses doubles every 2.5 years. For users, this means more data to analyze, leading to better business outcomes. That’s the good news. The bad news is that this extra storage capacity and computing power comes at a cost. A high cost it turns out.
So what is an enterprise to do?
Keep writing bigger and bigger checks to the data warehouse vendor? At least the business can take advantage of the extra data?
Or should they move some of the lesser-used data to tape? That will save money. But it will also limit business access to this now “off-line” data which may mean missed business opportunities.
What if there was a third option that would preserve the on-line access for the business analysts and control these escalating costs for IT?
Log in here to access the presentations at Cisco Live on Cisco’s new Big Data Warehouse Expansion.
Cisco Big Data Warehouse Expansion is a new offering that combines hardware, software and services to help customers control the costs of their ever-expanding data warehouses by offloading infrequently used data to low-cost big data stores. Analytics are enriched as more data is retained and all data remains accessible.
Big Data remains one of the hottest topics in the industry due to the actual dollar value that businesses are deriving from making sense from tons of structured and unstructured data. Virtually every field is leveraging a data-driven strategy as people, process, data and things are increasing being connected (Internet of Everything). New tools and techniques are being developed that can mine vast stores of data to inform decision making in ways that were previously unimagined. The fact that we can derive more knowledge by joining related information and recognizing correlations can inform and enrich numerous aspects of every day life. There’s a good reason why Big Data is so hot!
This year at Hadoop Summit, Cisco invites you to learn how to unlock the value of Big Data. Unprecedented data creation opens the door to responsive applications and emerging analytics techniques and businesses need a better way to analyze data. Cisco will be showcasing Infrastructure Innovations from both Cisco Unified Computing System (UCS) and Cisco Applications Centric Infrastructure (ACI). Cisco’s solution for deploying big data applications can help customers make informed decisions, act quickly, and achieve better business outcomes.
Cisco is partnering with leading software providers to offer a comprehensive infrastructure and management solution, based on Cisco UCS, to support our customers’ big data initiatives. Taking advantage of Cisco UCS’s Fabric based infrastructure, Cisco can apply significant advantage to big data workloads.