ENTRIES TAGGED "strata"
Preview of upcoming session at the Strata Conference
As a preview, let’s talk about two pretty pictures.
I’m running some typical distributed systems (HDFS, MapReduce, Impala, HBase, Zookeeper) on a small, seven-node cluster. The diagram above has individual processes and the TCP connections they’ve established to each other. Some processes are “masters” and they end up talking to many other processes.
The biggest problems will almost always be those for which the size of the data is part of the problem.
A recent VentureBeat article argues that “Big Data” is dead. It’s been killed by marketers. That’s an understandable frustration (and a little ironic to read about it in that particular venue). As I said sarcastically the other day, “Put your Big Data in the Cloud with a Hadoop.”
You don’t have to read much industry news to get the sense that “big data” is sliding into the trough of Gartner’s hype curve. That’s natural. Regardless of the technology, the trough of the hype cycle is driven by by a familiar set of causes: it’s fed by over-agressive marketing, the longing for a silver bullet that doesn’t exist, and the desire to spout the newest buzzwords. All of these phenomena breed cynicism. Perhaps the most dangerous is the technologist who never understands the limitations of data, never understands what data isn’t telling you, or never understands that if you ask the wrong questions, you’ll certainly get the wrong answers.
Big data is not a term I’m particularly fond of. It’s just data, regardless of the size. But I do like Roger Magoulas’ definition of “big data”: big data is when the size of the data becomes part of the problem. I like that definition because it scales. It was meaningful in 1960, when “big data” was a couple of megabytes. It will be meaningful in 2030, when we all have petabyte laptops, or eyeglasses connected directly to Google’s yottabyte cloud. It’s not convenient for marketing, I admit; today’s “Big Data!!! With Hadoop And Other Essential Nutrients Added” is tomorrow’s “not so big data, small data actually.” Marketing, for better or for worse, will deal. Read more…
Preview of an upcoming session at Strata Santa Clara
In many modern web and big data applications the data arrives in a streaming fashion and needs to be processed on the fly. In these applications, the data is usually too large to fit in main memory, and the computations need to be done incrementally upon arrival of new pieces of data. Sketching techniques allow these applications to be realized with high levels of efficiency in memory, computation, and network communications.
In the algorithms research community, sketching techniques first appeared in the literature in 1980s, e.g., in the seminal work of Philippe Flajolet and G. Nigel Martin, then caught attentions in late 1990s, partially inspired by the award-winning work of Noga Alon, Yossi Matias, and Mario Szegedy, and were/are on fire in 2000’s/2010’s, when sketches got successfully designed not only for fundamental problems such as heavy hitters, but also for matrix computations, network algorithms, and machine learning. These techniques are now at an inflection point in the course of their history, due to the following factors:
1. Untapped potential: Being so new, their huge practical potential has been yet barely tapped into.
2. Breadth and maturity: They are now both broad and mature enough to start to be widely used across a variety of big data applications, and even act as basic building blocks for new highly efficient big data management systems.
Preview of upcoming session "Who is Fake?" at the Strata Conference
By Lutz Finger
In the Matrix, the idea of a computer algorithm determining what we think may seemed far-fetched. Really? Far-fetched? Let’s look at some numbers.
About half of all Americans get their news in digital form. This news is written up by journalists, half of whom at least partially source their stories from social media. They use tools to harvest the real time knowledge of 100,000 tweets per second and more.
But what if someone could influence those tools and create messages that look as though they were part of a common consensus? Or create the appearance of trending?
Preview of upcoming session at Strata Santa Clara
Is your organization considering embracing data science? If so, we would like to give you some helpful advice on organizational and technical issues to consider before you embark on any initiatives or consider hiring data scientists. Join us, Sean Murphy and Marck Vaisman, two Washington, D.C. based data scientists and founding members of Data Community DC, as we walk you through the trials and tribulations of practicing data scientists at our upcoming talk at Strata.
We will discuss anecdotes and best practices, and finish by presenting the results of a survey we conducted last year to help understand the varieties of people, skills, and experiences that fall under the broad term of “Data Scientist”. We analyzed data from over 250 survey respondents, and are excited to share our findings, which will also be published soon by O’Reilly.
Preview of upcoming Strata session on data exploration
Amy Heineike is Director of Mathematics for Quid Inc, where she has been since its inception, prototyping and launching the company’s technology for analyzing document sets. Below is the teaser for her upcoming talk at Strata Santa Clara.
I recently discovered that my favorite map is online. It used to hang on my housemate’s wall in our little house in London back in 2005. At the time I was working to understand how London was evolving and changing, and how different policy or infrastructure changes (a new tube line, land use policy changes) would impact that.
The map was originally published as a center-page pull out from the Guardian, showing the ethnic groups that dominate different neighborhoods across the city. The legend was as long as the image, and the small print labels necessitated standing up close, peering and reading, tracing your finger to discover the Congolese on the West Green Road, our neighbors the Portuguese on the Stockwell Road, or the Tamils in Chessington in the distant south west.
We are simply not good at playing with others when it comes to data
Russia’s railway gauge is different from Western Europe’s. At the border of the former Soviet states, the Russian gauge of 1.524m meets the European & American ‘Standard’ gauge of 1.435m. The reasons for this literal disconnect arise from discussions between the Tsar and his War Minister. When asked the most effective way to prevent Russia’s own rail lines being used against them in times of invasion, the Minister suggested a different gauge to prevent supply trains rolling through the border. The artifact of this decision remains visible today at all rail crossings between Poland and Belarus or Slovakia and Ukraine. The rail cars are jacked up at the border, new wheels inserted underneath, and the car lowered again. It is about a 2-4 hour time burn for each crossing.
Per head, per crossing, over 170 years, is a heck of a lot of resource wasted. But to change it would entail changing the rail stock of the entire country and realigning about 225,000 km (140,000 mi) of track.
Talk about technical debt.
Data suffers from a similar disconnect. It really wasn’t until the advent of XML 15 years ago that we had an agreed (but not entirely satisfactory) mechanism for storing arbitrary data structures outside the application layer. This is as much a commentary on our technical priorities as it is a social indictment. We are simply not good at playing with others when it comes to data.
A Call for Industry-Standard Benchmarks for Big Data Platforms at Strata SC 2013
Big data systems are characterized by their flexibility in processing diverse data genres, such as transaction logs, connection graphs, and natural language text, with algorithms characterized by multiple communication patterns, e.g. scatter-gather, broadcast, multicast, pipelines, and bulk-synchronous. A single benchmark that characterizes a single workload could not be representative of such a multitude of use-cases. However, our systematic study of several use-cases of current big data platforms indicates that most workloads are composed of a common set of stages, which capture the variety of data genres and algorithms commonly used to implement most data-intensive end-to-end workloads. Our upcoming session at Strata SC discusses the BigData Top 100 List, a new community-based initiative for benchmarking big data systems.
Tips for interacting with analytics colleagues
To quote Pride and Prejudice, businesses have for many years “labored under the misapprehension” that their analytics talent was made up of misanthropes with neither the will nor the ability to communicate or work with others on strategic or creative business problems. These employees were meant to be kept in the basement out of sight, fed bad pizza, and pumped for spreadsheets to be interpreted in the sunny offices aboveground.
This perception is changing in industry as the big data phenomenon has elevated data science to a C-level priority. Suddenly folks once stereotyped by characters like Milton in Office Space are now “sexy.” The truth is there have always been well-rounded, articulate, friendly analytics professionals (they may just like Battlestar more than you), and now that analytics is an essential business function, personalities of all types are being attracted to practice the discipline.