Julie B Peterson

(Ghostwriter) First, there were data lakes, then data warehouses and now data platforms. The key commonality is data. Data deserves its own category in the infrastructure architecture, and, adopting a term we’ve heard from our customers, we’ve been calling it “dataware.”

(Ghostwriter) If you want to harness the full potential of AI/ML for cybersecurity, due diligence is required. Understanding your needs and asking the right questions up front ensures you’re buying the right tool for your enterprise.

Hailed as the “next big thing” in the information security space, artificial intelligence (AI) and machine learning (ML) are poised to disrupt the cybersecurity industry. If you believe everything you read, AI and ML is the miracle solution coming to save the day. No more exha...

(Ghostwriter) Marketing for information security products is filled with buzzwords of the day, especially when looking at artificial intelligence and machine learning. Even by themselves, AI/ML are hard words to define, so how do decision-makers untangle the marketing jargon to really understand what they are buying? Before purchasing a security solution, decision-makers need to look under the covers at what security vendors are offering in the way of artificial intelligence and machine learning.

(Ghostwriter) Ask the tough questions before you invest in artificial intelligence and machine learning technology. The security of your enterprise depends on it.

When looking at the artificial intelligence (AI) and machine learning (ML) components of information security products, it's easy to get overwhelmed by the glut of marketing buzzwords. As a decision maker, how do you cut through the jargon to fully understand what you're purchasing?

The key is in asking the right questions before purchasing.

(Ghostwriter) By William Peterson, MapR

Naming is hard. Once you’ve chosen a name and your product or solution is out the door, it’s still not over. What do you call the next version?

(Ghostwriter) In the beginning, workloads, tools, and requirements for big data were simple because big data wasn’t really all that big. When we hit 5TB of data, however, things got complicated. Large data sets weren’t well suited to traditional storage like NAS, and large sequential reading of terabytes of data didn’t work well with traditional shared storage.

As big data evolved, the analytics tools graduated from custom code like MapReduce, Hive, and Pig to tools like Spark, Python, and Tensorflow, whic...

(Ghostwriter) Data scientists and information security practitioners have long operated in their own independent spheres of influence. When you look at their responsibilities, however, you begin to see they are more alike than different.

(Ghostwriter) Life is a journey, but getting to your data shouldn’t have to be.

Data is the lifeblood of most businesses, but many organizations aren’t taking full advantage of their data because it’s complex, globally distributed and hard to access.

From high-resolution sensors on the edge to industrial IoT devices, the growth in unstructured data is exploding. There are more data sources emitting more data than ever before. This diversity of data types causes new silos to appear for specific kinds of pro...

(Ghostwriter) Big-data driven changes are sweeping through the retail industry. Though the retail sector is no stranger to big data, much of the focus has been on analyzing historical data. While this has been useful in creating efficiencies and identifying consumer trends, retailers need to go further if they want to remain competitive and increase their margins. Using artificial intelligence (AI) and machine learning in real time is the key to unlocking deeper insights in all aspects of retail operations...

(Ghostwriter) Demand for data in the telecommunications industry is growing at an accelerated pace.

How big data helps optimise mobile network infrastr...

(Ghostwriter) Big data technologies, when combined with streaming analytics and analytics at scale, are changing the way CSPs do business by uncovering significant new insights about their infrastructure and customers.

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