The O'Reilly Data Show Podcast: Gary Kazantsev on how big data and data science are making a difference in finance. Kazantsev also talks about the types of problems he and his group are working on and the tools and techniques they’ve found useful. Read more...
The O'Reilly Data Show Podcast: Ihab Ilyas on building data wrangling and data enrichment tools in academia and industry.
As I’ve written in previous posts, data preparation and data enrichment are exciting areas for entrepreneurs, investors, and researchers. Startups like Trifacta, Tamr, Paxata, Alteryx, and CrowdFlower continue to innovate and attract enterprise customers. I’ve also noticed that companies — that don’t specialize in these areas — are increasingly eager to highlight data preparation capabilities in their products and services.
During a recent episode of the O’Reilly Data Show Podcast, I spoke with Ihab Ilyas, professor at the University of Waterloo and co-founder of Tamr. We discussed how he started working on data cleaning tools, academic database research, and training computer science students for positions in industry.
Academic database research in data preparation
Given the importance of data integrity, it’s no surprise that the database research community has long been interested in data preparation and data wrangling. Ilyas explained how his work in probabilistic databases led to research projects in data cleaning:
In the database theory community, these problems of handling, dealing with data inconsistency, and consistent query answering have been a celebrated area of research. However, it has been also difficult to communicate these results to industry. And database practitioners, if you like, they were more into the well-structured data and assuming a lot of good properties around this data, [and they were also] more interested in indexing this data, storing it, moving it from one place to another. And now, dealing with this large amount of diverse heterogeneous data with tons of errors, sidled across all business units in the same enterprise became a necessity. You cannot really avoid that anymore. And that triggered a new line of research for pragmatic ways of doing data cleaning and integration. … The acquisition layer in that stack has to deal with large sets of formats and sources. And you will hear about things like adapters and source adapters. And it became a market on its own, how to get access and tap into these sources, because these are kind of the long tail of data.
The way I came into this subject was also funny because we were talking about the subject called probabilistic databases and how to deal with data uncertainty. And that morphed into trying to find data sets that have uncertainty. And then we were shocked by how dirty the data is and how data cleaning is a task that’s worth looking at.
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
The O'Reilly Radar Podcast: Turing Award winner Michael Stonebraker on the future of data science.
Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.
In March 2015, database pioneer Michael Stonebraker was awarded the 2014 ACM Turing Award “for fundamental contributions to the concepts and practices underlying modern database systems.” In this week’s Radar Podcast, O’Reilly’s Mike Hendrickson sits down with Stonebraker to talk about winning the award, the future of data science, and the importance — and difficulty — of data curation.
One size does not fit all
Stonebraker notes that since about 2000, everyone has realized they need a database system, across markets and across industries. “Now, it’s everybody who’s got a big data problem,” he says. “The business data processing solution simply doesn’t fit all of these other marketplaces.” Stonebraker talks about the future of data science — and data scientists — and the tools and skill sets that are going to be required:
It’s all going to move to data science as soon as enough data scientists get trained by our universities to do this stuff. It’s fairly clear to me that you’re probably not going to retread a business analyst to be a data scientist because you’ve got to know statistics, you’ve got to know machine learning. You’ve got to know what regression means, what Naïve Bayes means, what k-Nearest Neighbors means. It’s all statistics.
All of that stuff turns out to be defined on arrays. It’s not defined on tables. The tools of future data scientists are going to be array-based tools. Those may live on top of relational database systems. They may live on top of an array database system, or perhaps something else. It’s completely open.
How big data, fast data, and real-time analytics work together in the real world.
Today, we often hear the phrase “The 3 Vs” in relation to big data: Volume, Variety and Velocity. With the interest and popularity of big data frameworks such as Hadoop, the focus has mostly centered on volume and data at rest. Common requirements here would be data ingestion, batch processing, and distributed queries. These are well understood. Increasingly, however, there is a need to manage and process data as it arrives, in real time. There may be great value in the immediacy of that data and the ability to act upon it very quickly. This is velocity and data in motion, also known as “fast data.” Fast data has become increasingly important within the past few years due to the growth in endpoints that now stream data in real time.
Big data + fast data is a powerful combination. However, adding real-time analytics to this mix provides the business value. Let’s look at a real example, originally described by Scott Jarr of VoltDB.
Consider a company that builds systems to manage physical assets in precious metal mines. Inside a mine, there are sensors on miners as well as shovels and other assets. For a lost shovel, minutes or hours of reporting latency may be acceptable. However, a sensor on a miner indicating a stopped heart should require immediate attention. The system should, therefore, be able to receive very fast data. Read more…
The O'Reilly Data Show Podcast: Phil Liu on the evolution of metric monitoring tools and cloud computing.
One of the main sources of real-time data processing tools is IT operations. In fact, a previous post I wrote on the re-emergence of real-time, was to a large extent prompted by my discussions with engineers and entrepreneurs building monitoring tools for IT operations. In many ways, data centers are perfect laboratories in that they are controlled environments managed by teams willing to instrument devices and software, and monitor fine-grain metrics.
During a recent episode of the O’Reilly Data Show Podcast, I caught up with Phil Liu, co-founder and CTO of SignalFx, a SF Bay Area startup focused on building self-service monitoring tools for time series. We discussed hiring and building teams in the age of cloud computing, building tools for monitoring large numbers of time series, and lessons he’s learned from managing teams at leading technology companies.
Evolution of monitoring tools
Having worked at LoudCloud, Opsware, and Facebook, Liu has seen first hand the evolution of real-time monitoring tools and platforms. Liu described how he has watched the number of metrics grow, to volumes that require large compute clusters:
One of the first services I worked on at LoudCloud was a service called MyLoudCloud. Essentially that was a monitoring portal for all LoudCloud customers. At the time, [the way] we thought about monitoring was still in a per-instance-oriented monitoring system. [Later], I was one of the first engineers on the operational side of Facebook and eventually became part of the infrastructure team at Facebook. When I joined, Facebook basically was using a collection of open source software for monitoring and configuration, so these are things that everybody knows — Nagios, Ganglia. It started out basically using just per-instance instant monitoring techniques, basically the same techniques that we used back at LoudCloud, but interestingly and very quickly as Facebook grew, this per-instance-oriented monitoring no longer worked because we went from tens or thousands of servers to hundreds of thousands of servers, from tens of services to hundreds and thousands of services internally.
Practical applications of human-in-the-loop machine learning.
With hundreds, thousands, or even just tens of suppliers — each with different business units, payment terms, and locations — businesses are faced with a monumental task: unifying all of their supplier-related data, and fast so that it can be useful. In order to ask deep questions about their data, companies are increasingly looking for a single, unified view of their supply chain.
And yet, business data is often stored in different sources, systems, and formats, resulting in silos of information. These data silos take the form of enterprise resource planning systems, CSV files, spreadsheets, and relational databases. To pull together all of the data from these disparate sources, a business faces three interrelated challenges:
- Speed. Traditionally, businesses have attempted to catalog and organize supply chain data manually — profiling and integrating data themselves, which leads directly to the next challenge: cost.
- Cost. Manual work is expensive work. Usually more than one employee will need to work on the same data set in order to move quickly enough for the results to have any value for the business. Even with several employees working on the same data sets, this work will still not achieve what could be done on a machine scale.
- Efficiency. Relying completely on humans to organize and unify data is a situation ripe for error. Plus, there’s often no audit trail, and the work results in inherently incomplete views of information.
In a recent live demo by Dr. Clare Bernard, a field engineer at Tamr, I got a glimpse into how Tamr is using a combination of machine learning algorithms and input from subject matter experts to help businesses unify their data for analysis. A practice that uses short-term human intervention to actively improve machine models, human-in-the-loop machine learning is taking off across all types of industries, including fashion, automotive, and cloud services such as Google Maps. Read more…