- Folium — makes it easy to visualize data that’s been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map.
- scavenger-hunt — A scavenger hunt to learn Linux commands.
- SEE — F-Secure’s open source Sandboxed Execution Environment (SEE) is a framework for building test automation in secured Environments.
- The Problem with Self-Driving Cars: Who Controls the Code? (Cory Doctorow) — Here’s a different way of thinking about this problem: if you wanted to design a car that intentionally murdered its driver under certain circumstances, how would you make sure that the driver never altered its programming so that they could be assured that their property would never intentionally murder them?
See, extract, and create value with networks.
Networks of all kinds drive the modern world. You can build a network from nearly any kind of data set, which is probably why network structures characterize some aspects of most phenomenon. And yet, many people can’t see the networks underlying different systems. In this post, we’re going to survey a series of networks that model different systems in order to understand different ways networks help us understand the world around us.
We’ll explore how to see, extract, and create value with networks. We’ll look at four examples where I used networks to model different phenomenon, starting with startup ecosystems and ending in network-driven marketing.
Networks and markets
Commerce is one person or company selling to another, which is inherently a network phenomenon. Analyzing networks in markets can help us understand how market economies operate.
Strength of weak ties
From unique data applications to factories of the future, here are key insights from Strata + Hadoop World New York 2014.
Experts from across the data world came together in New York City for Strata + Hadoop World New York 2014. Below we’ve assembled notable keynotes, interviews, and insights from the event.
Unusual data applications and the correct way to say “Hadoop”
Hadoop creator and Cloudera chief architect Doug Cutting discusses surprising data applications — from dating sites to premature babies — and he reveals the proper (but in no way required) pronunciation of “Hadoop.”
Cloudera ventures into real-time queries with Impala, data centers are the new landfill, and Jesper Andersen looks at the relationship between art and data.
Here are a few stories from the data space that caught my attention this week.
Cloudera’s Impala takes Hadoop queries into real-time
Cloudera ventured into real-time Hadoop querying this week, opening up its Impala software platform. As Derrick Harris reports at GigaOm, Impala — an SQL query engine — doesn’t rely on MapReduce, making it faster than tools such as Hive. Cloudera estimates its queries run 10 times faster than Hive, and Charles Zedlewski, Cloudera’s cloud VP of products, told Harris that “small queries can run in less than a second.”
Harris notes that Zedlewski pointed out that Impala wasn’t designed to replace business intelligence (BI) tools, and that “Cloudera isn’t interested in selling BI or other analytic applications.” Rather, Impala serves as the execution engine, still relying on software from Cloudera partners — Zedlewski told Harris, “We’re sticking to our knitting as a platform vendor.”
Joab Jackson at PC World reports that “[e]ventually, Impala will be the basis of a Cloudera commercial offering, called the Cloudera Enterprise RTQ (Real-Time Query), though the company has not specified a release date.”
Impala has plenty of competition on this playing field, which Harris also covers, and he notes the significance of all the recent Hadoop innovation:
“I can’t underscore enough how critical all of this innovation is for Hadoop, which in order to add substance to its unparalleled hype needed to become far more useful to far more users. But the sudden shift from Hadoop as a batch-processing engine built on MapReduce into an ad hoc SQL querying engine might leave industry analysts and even Hadoop users scratching their heads.”
You can read more from Harris’ piece here and Jackson’s piece here. Wired also has an interesting piece on Impala, covering the Google F1 database upon which it is based and the Googler Cloudera hired away to help build it.
(Cloudera CEO Mike Olson discussed Impala, Hadoop and the importance of real-time at this week’s Strata Conference + Hadoop World.)
Bitsy Bentley on the work behind a good visualization and why she hopes users will take data interactions for granted.
Because of the size, complexity and density of big data, it’s not always easy to find the important insights hiding in all that information. That’s where data visualization comes into play. A great visualization creates meaning where none existed.
Bitsy Bentley (@bitsybot) is the director of data visualization at GfK Custom Research, where she works with information designers to craft meaningful data experiences for a variety of business audiences. In the following interview, she discusses the space between a “wow” response and an “aha” moment, how her team addresses privacy concerns, and why practice is vital for both visualization creators and viewers.
Bentley will explore related visualization topics during her presentation at Strata Conference + Hadoop World in New York City later this month.
Why are data visualizations an effective way to understand the underlying data?
Bitsy Bentley: There is so much beauty and richness in big datasets, and now that we have enough processing power to harness that richness, it’s little wonder that interest in data visualization is exploding. To quote John Tukey: “The greatest value of a picture is when it forces us to notice what we never expected to see.” My clients find that, whether they’re more concerned with numbers or more concerned with stories, an appropriate visual is integral to their understanding of the data.
Visualization unlocks the serendipity of data analysis. It provides a language that is less intimidating than an overwhelming array of digits. Something as simple as a set of histograms breaking down the distribution of a data store makes it easy to find irregularities and outliers in the data. Read more…