Last week, Andy Stubley interviewed by Briefings Direct, discussed how HP Vertica is a critical component to System Mechanic’s Zen, a fault, performance and social media service assurance solution for mobile networks. Below is a quick excerpt along with a link to the full article, check it out!
Gardner: Now that we understand what you do, let’s get into how you do it. What’s beneath the covers in your Zen system that allows you to confidently say you can take any volume of data you want?
Stubley: Fundamentally, that comes down to the architecture we built for Zen. The first element is our data-integration layer. We have a technology that we developed over the last 10 years specifically to capture data in telco networks. It’s real-time and rugged and it can deal with any volume. That enables us to take anything from the network and push it into our real-time database, which is HP’s Vertica solution, part of the HP HAVEn family.
Vertica analysis is to basically record any amount of data in real time and scale automatically on the HP hardware platform we also use. If we need more processing power, we can add more services to scale transparently. That enables us to get any amount of data, which we can then process…”
“We missed our numbers last quarter because we’re not leveraging Big Data! How did we miss this?!”
Continuing this five part series focused on how organizations frequently go through the five stages of grief when confronting big data challenges, this post will focus on the second stage: anger.
It’s important to note that while an organization may begin confronting big data with something very like denial, anger usually isn’t far behind. As mentioned previously, very often the denial is rooted in the fact that the company doesn’t see the benefit in big data, or the benefits appear too expensive. And sometimes the denial can be rooted in a company’s own organizational Read More »
Automatic physical database design is a challenging task. Different customers have different requirements and expectations, bounded by their resource constraints. To deal with these challenges in HP Vertica, we adopt a customizable approach by allowing users to tailor their designs for specific scenarios and applications. To meet different customer requirements, any physical database design tool should allow its users to trade off query performance and storage footprint for different applications.
In this blog, we present a technical overview of the Database Designer (DBD), a customizable physical design tool that primarily operates under three design policies:
Load-optimized –DBD proposes the minimum required set of super projections (containing all columns) that permit fast load and deliver required fault tolerance. Query-optimized –DBD may Read More »
Modern databases are often required to process many different kinds of workloads, ranging from short/tactical queries, to medium complexity ad-hoc queries, to long-running batch ETL jobs to extremely complex data mining jobs (See my previous blog on workload classification for more information.) DBAs must ensure that all concurrent workload, along with their respective Service Level Agreements (SLAs), can co-exist well with each other while maximizing a system’s overall performance.
So what is concurrency? Why should a customer care about concurrency?
Concurrency is a term used to describe having multiple jobs running in an overlapping time interval in a system. It doesn’t necessarily mean that they are or ever will be running at the same instant. Concurrency is synonymous to multi-tasking Read More »
My father passed away recently, and so I’ve found myself in the midst of a cycle of grief. And, in thinking about good blog topics, I realized that many of the organizations I’ve worked with over the years have gone through something very much like grief as they’ve come to confront big data challenges…and the stages they go through even map pretty cleanly to the five stages of grief! So this series was born.
So it’ll focus on the five stages of grief: denial, anger, bargaining, depression, and acceptance. I’ll explore the ways in which organizations experience each of these phases when confronting the challenges of big data, and also present strategies for coping with these challenges and coming to Read More »
This week I sat down with Ben Vandiver, a Vertica veteran who’s been with the company since 2008, and talked about everything from influencing presidential elections, making an impact, and sword-fighting with interns.
Au∙to∙mag∙ic: (Of a usually complicated technical or computer process) done, operating, or happening in a way that is hidden from or not understood by the user, and in that sense, apparently “magical”
In previous installments of this series, I de-bunked some of the more common myths around big data analytics. In this final installment, I’ll address one of the most pervasive and costly myths: that there exists an easy button that organizations can press to automagically solve their big data problems. I’ll provide some insights as to how this myth has come about, and recommend strategies for dealing with the real challenges inherent in big data analytics.