- Ashley Madison’s Fembot Con (Gizmodo) — As documents from company e-mails now reveal, 80% of first purchases on Ashley Madison were a result of a man trying to contact a bot, or reading a message from one.
- Terrapin — Pinterest’s low-latency NoSQL replacement for HBase. See engineering blog post.
- Why Futurism Has a Cultural Blindspot (Nautilus) — As the psychologist George Lowenstein and colleagues have argued, in a phenomenon they termed “projection bias,” people “tend to exaggerate the degree to which their future tastes will resemble their current tastes.”
- Mind-Controlled Prosthetic Arm (Quartz) — The robotic arm is connected by wires that link up to the wearer’s motor cortex — the part of the brain that controls muscle movement — and sensory cortex, which identifies tactile sensations when you touch things. The wires from the motor cortex allow the wearer to control the motion of the robot arm, and pressure sensors in the arm that connect back into the sensory cortex give the wearer the sensation that they are touching something.
The O'Reilly Data Show Podcast: Mike Cafarella on the early days of Hadoop/HBase and progress in structured data extraction.
Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
February 2016 marks the 10th anniversary of Hadoop — at a point in time when many IT organizations actively use Hadoop, and/or one of the open source, big data projects that originated after, and in some cases, depend on it.
During the latest episode of the O’Reilly Data Show Podcast, I had an extended conversation with Mike Cafarella, assistant professor of computer science at the University of Michigan. Along with Strata + Hadoop World program chair Doug Cutting, Cafarella is the co-founder of both Hadoop and Nutch. In addition, Cafarella was the first contributor to HBase.
We talked about the origins of Nutch, Hadoop (HDFS, MapReduce), HBase, and his decision to pursue an academic career and step away from these projects. Cafarella’s pioneering contributions to open source search and distributed systems fits neatly with his work in information extraction. We discussed a new startup he recently co-founded, ClearCutAnalytics, to commercialize a highly regarded academic project for structured data extraction (full disclosure: I’m an advisor to ClearCutAnalytics). As I noted in a previous post, information extraction (from a variety of data types and sources) is an exciting area that will lead to the discovery of new features (i.e., variables) that may end up improving many existing machine learning systems. Read more…
The O’Reilly Podcast: Ben Sharma on the business impact of Hadoop and the evolution of tools
In this episode of the O’Reilly Podcast, O’Reilly’s Ben Lorica chats with Ben Sharma, CEO and co-founder of Zaloni, a company that provides enterprise data management solutions for Hadoop. Sharma was one of the first users of Apache Hadoop, and has a background in enterprise solutions architecture and data analytics.
Before starting Zaloni, Sharma spent many years as a business consultant and began to see that companies across industries were struggling to process, store, and extract value from their data. Having worked extensively in telecom, Sharma helped equipment vendors deploy large-scale network infrastructures at carriers across the world. He began to see how Hadoop could have an impact in the business analytics aspect of companies, not just in IT.
In this interview, Lorica and Sharma discuss the early days of Hadoop and how businesses across industries are benefitting from Hadoop. They also discuss the evolution of tools in the space and how more companies are moving toward real-time decision-making with the growth of streaming tools and real-time data. Read more…
An ETL offload solution addresses the challenges of data overload, rising costs, and the skills gap.
As the amount of data continues to double in size every two years, organizations are struggling more than ever before to manage, ingest, store, process, transform, and analyze massive data sets. It has become clear that getting started on the road to using data successfully can be a difficult task, especially with a growing number of new data sources, demands for fresher data, and the need for increased processing capacity. In order to advance operational efficiencies and drive business growth, however, organizations must address and overcome these challenges.
In recent years, many organizations have heavily invested in the development of enterprise data warehouses (EDW) to serve as the central data system for reporting, extract/transform/load (ETL) processes, and ways to take in data (data ingestion) from diverse databases and other sources both inside and outside the enterprise. Yet, as the volume, velocity, and variety of data continues to increase, already expensive and cumbersome EDWs are becoming overloaded with data. Furthermore, traditional ETL tools are unable to handle all the data being generated, creating bottlenecks in the EDW that result in major processing burdens.
As a result of this overload, organizations are now turning to open source tools like Hadoop as cost-effective solutions to offloading data warehouse processing functions from the EDW. While Hadoop can help organizations lower costs and increase efficiency by being used as a complement to data warehouse activities, most businesses still lack the skill sets required to deploy Hadoop. Read more…
A field guide to the Apache Hadoop projects, subprojects, and related technologies.
IT managers, developers, data analysts, and system architects are encountering the largest and most disruptive change in data analysis since the ascendency of the relational database in early 1980s — the challenge to process, organize, and take full advantage of big data. With 73% of organizations making big data investments in 2014 and 2015, this transition is occurring at a historic pace, requiring new ways of thinking to go along with new tools and techniques.
Hadoop is the cornerstone of this change to a landscape of systems and skills we’ve traditionally possessed. In the nine short years since the project revolutionized data science at Yahoo!, an entire ecosystem of technologies has sprung up around it. While the power of this ecosystem is plain to see, it can be a challenge to navigate your way through the complex and rapidly evolving collection of projects and products.
A couple years ago, my coworker Marshall Presser and I started our journey into the world of Hadoop. Like many folks, we found the company we worked for was making a major investment in the Hadoop ecosystem, and we had to find a way to adapt. We started in all of the typical places — blog posts, trade publications, Wikipedia articles, and project documentation. Quickly, we learned that many of these sources are often highly biased, either too shallow or too deep, and just plain inconsistent. Read more…
With Myriad, analytics can be performed on the same hardware that runs your production services.
This is a tale of two siloed clusters. The first cluster is an Apache Hadoop cluster. This is an island whose resources are completely isolated to Hadoop and its processes. The second cluster is the description I give to all resources that are not a part of the Hadoop cluster. I break them up this way because Hadoop manages its own resources with Apache YARN (Yet Another Resource Negotiator). Which is nice for Hadoop, but all too often those resources are underutilized when there are no big data workloads in the queue. And then when a big data job comes in, those resources are stretched to the limit, and they are likely in need of more resources. That can be tough when you are on an island.
Hadoop was meant to tear down walls — albeit, data silo walls — but walls, nonetheless. What has happened is that while tearing some walls down, other types of walls have gone up in their place.
Another technology, Apache Mesos, is also meant to tear down walls — but Mesos has often been positioned to manage the “second cluster,” which are all of those other, non-Hadoop workloads.
This is where the story really starts, with these two silos of Mesos and YARN. They are often pitted against each other, as if they were incompatible. It turns out they work together, and therein lies my tale. Read more…
Changing your frame of reference when starting with SQL on Hadoop.
Editor’s note: John Russell will be one of the teachers of the tutorial Getting Started with Interactive SQL-On-Hadoop at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program.
If you’re just getting started doing analytic work with SQL on Hadoop, a table with a million rows might seem like a good starting point for experimentation. Isn’t that a lot of data? While you can exercise the features of a traditional database with a million rows, for Hadoop it’s not nearly enough. Think billions of rows instead.
Let’s look at the ways a million-row table falls short. Understanding the data volumes involved with big data can help you avoid going down unproductive pathways based on misleading assumptions.
With a million-row table, every byte in each row represents a megabyte of total data volume. Let’s say your table represents people and has fields for name, address, occupation, salary, height, weight, number of children, and favorite food. Here’s what a sample field might look like, with a scale underneath to illustrate length:
This particular record takes up 78 characters, including the comma separators. A back-of-the-envelope calculation suggests that, if this is an average row, we’ll end up with about 78 megabytes of data in the table. (And don’t recycle that envelope just yet — doing analytics with Hadoop, you’ll do a lot of rough estimates like this to sanity-check your expectations about performance and scalability.) Read more…
In this O'Reilly Radar Podcast: Edd Dumbill on the data lake, and Rajiv Maheswaran on the science of moving dots.
In a recent blog post, Edd Dumbill, VP of strategy at Silicon Valley Data Science, wrote about the phrase “data lake.” Likening it to a dream, he described a data lake as “a place with data-centered architecture, where silos are minimized, and processing happens with little friction in a scalable, distributed environment…Data itself is no longer restrained by initial schema decisions, and can be exploited more freely by the enterprise.” He explained that he called it a “dream” because “we’ve a way to go to make the vision come true” — but noted he’s optimistic the dream can be realized.