In this post I am goIing to summarize some of the things that I learned at Strata Santa Clara 2013. For now, I will only discuss the conference sessions as I have a much longer post about the tutorial sessions that I am still working on and will post at a later date. I will add to this post as the conference winds down.
The slides for most talks will be available here but not all speakers will share their slides.
This is/was my first trip to Strata so I was eagerly awaiting participating as an attendant. In the past, I had been put off by the cost and was also concerned that the conference would be an endless advertisement for the conference sponsors and Big Data platforms. I am happy to say that for the most part I was proven wrong. For easier reading, I am summarizing talks by topic rather than giving a laundry list schedule for a long day and also skip sessions that I did not find all that illuminating. I also do not claim 100% accuracy of this text as the days are very long and my ears and mind can only process so much data when I am context […]
Lately I have doing a lot of work with the Wikipedia XML dump as a corpus. Wikipedia provides a wealth information to researchers in easy to access formats including XML, SQL and HTML dumps for all language properties. Some of the data freely available from the Wikimedia Foundation include
article content and template pages
article content with revision history (huge files)
article content including user pages and talk pages
page-to-page link lists: redirects, categories, image links, page links, interwiki etc.
The above resources are available not only for Wikipedia, but for other Wikimedia Foundation projects such as Wiktionary, Wikibooks and Wikiquotes.
As Wikipedia readers will notice, the articles are very well formatted and this formatting is generated by a somewhat unusual markup format defined by the MediaWiki project. As Dirk Riehl stated:
There was no grammar, no defined processing rules, and no defined output like a DOM tree based on a well defined document object model. This is to say, the content of Wikipedia is stored in a format that is not an open standard. The format is defined by 5000 lines of php code (the parse function of MediaWiki). That code may be open source, but it is incomprehensible to most. That’s why […]
It’s been a while since I have posted… in the midst of trying to plow through this dissertation while working on papers for submission to some conferences.
Hadoop has become the de facto standard in the research and industry uses of small and large-scale MapReduce. Since its inception, an entire ecosystem has been built around it including conferences (Hadoop World, Hadoop Summit), books, training, and commercial distributions (Cloudera, Hortonworks, MapR) with support. Several projects that integrate with Hadoop have been released from the Apache incubator and are designed for certain use cases:
Pig, developed at Yahoo, is a high-level scripting language for working with big data and Hive is a SQL-like query language for big data in a warehouse configuration.
HBase, developed at Facebook, is a column-oriented database often used as a datastore on which MapReduce jobs can be executed.
ZooKeeper and Chukwa
Mahout is a library for scalable machine learning, part of which can use Hadoop.
Cascading (Chris Wensel), Oozie (Yahoo) and Azkaban (LinkedIn) provide MapReduce job workflows and scheduling.
Hadoop is meant to be modeled after Google MapReduce. To store and process huge amounts of data, we typically need several machines in some cluster configuration. A distributed filesystem (HDFS for Hadoop) uses space across […]
I woke up early and cheery Wednesday morning to attend the 2011 Hadoop Summit in Santa Clara, after a long drive from Los Angeles and the Big Data Camp that lasted until 10pm the night before. Having been to Hadoop Summit 2010, I was interested to see how much of the content in the conference had changed.
This year, there were approximately 1,600 participants and the summit was moved a few feet away to the Convention Center rather than the Hyatt. Still, space and seating was pretty cramped. That just goes to show how much the Hadoop field has grown in just one year.
We first heard a series of keynote speeches which I will summarize. The first keynote was from Jay Rossiter, SVP of the Cloud Platform Group at Yahoo. He introduced how Hadoop is used at Yahoo, which is fitting since they organized the event. The content of his presentation was very similar to last year’s. One interesting application of Hadoop at Yahoo was for “retiling” the map of the United States. I imagine this refers to the change in aerial imagery over time. When performed by hand, retiling took 6 weeks; with Hadoop, it took 5 days. Yahoo also […]
It has been a while since I have been to Silicon Valley, but Hadoop Summit gave me the opportunity to go. To make the most of the long trip, I also decided to check out BigDataCamp held the night before from 5:30 to 10pm. Although the weather was as predicted, I was not prepared for the deluge of pouring rain in the end of June. The weather is one of the things that is preventing me from moving up to Silicon Valley.
The food/drinks/networking event must have been amazing because it was very difficult to get everyone to come to the main room to start the event! We started with a series of lightning talks from some familiar names and some unfamiliar ones.
Chris Wensel, the developer of Cascading, is also the founder of Concurrent, Inc. Cascading is an alternate API for Map-Reduce written in Java. With Cascading, developers can chain multiple map-reduce jobs to form an ad hoc workflow. Cascading adds a built-in planner to manage jobs. Cascading usually infers Hadoop, but Cascading can run on other platforms including EMC Greenplum and the new MapR project. RazorFish and BestBuy use Cascading for behavioral targeting. Flightcaster uses a domain specific language (DSL) […]
Some time over the past 6 weeks I randomly saw a tweet announcing the “Data Scientist Summit” and shortly below it I saw that it would be held in Las Vegas at the Venetian. Being a Data Scientist myself is reason enough to not pass up this opportunity, but Vegas definitely sweetens the deal! On Wednesday I woke up at 6am to partake on the 5.5 hour voyage to Las Vegas.
The Venetian and all close hotels were booked, so I ended up at the Aria; a new experience. The hotel is beautiful and very ritzy. I had heard that the rooms were very technologically advanced but I wasn’t prepared for the recorded welcome message, music and automatic shades opening upon entry to the room. The Aria is a geek’s paradise. Everything is computerized. Key cards are “waved” rather than swiped, lights are turned on/off and dimmed by use case (“sleep”, “read” etc.), rather than manually. There are no paper “Do Not Disturb” signs; rather, a switch on the wall (or via TV) toggles an indicator light outside the door. And the best part… Internet is FREE!
The rhododendrons hydrangeas are real!
Elastic Compute Cloud (EC2) is a service provided a Amazon Web Services that allows users to leverage computing power without the need to build and maintain servers, or spend money on special hardware. The idea is simple, the user “boots” up one or more machines and then accesses those machines as if they were logged into any other machine remotely. I used EC2 and Elastic MapReduce extensively for my M.S. thesis last spring, but mainly used its large memory capabilities rather than its potential for explicit parallelism.
Recently, I ran a crawling job on EC2 using a parellel crawler I wrote in Python with twill. Using EC2 poses its own challenges. Using parallel code poses more challenges. Combining these two facts with the fact that crawling is I/O bound can create some more interesting challenges. If you have taken a course in operating systems, you have heard this stuff over and over again. So have I, but I am stubborn. I tend to learn lessons from experience, and this was no exception. Through this series of posts, I want to point out difficulties and “gotchas” that are important to keep in mind when using EC2, and in this post, with […]
This week, a few different big data processing tools were released to the open-source community. I know, I know, this is probably the 1000th blog post about this, and perhaps the train has left the station without me, but here I am.
Yahoo’s S4: Distributed Stream Computing Platform
First off, it must be said. S4 is NOT real-time map-reduce! This is the meme that has been floating around the Internets lately.
S4 is a distributed, scalable, partially fault-tolerant, pluggable platform that allows users to create applications that process unbounded streaming data. It is not a Hadoop project. A matter of fact, it is not even a form of map-reduce. S4 was developed at Yahoo for personalization of search advertising products. Map-reduce, so far, is not a great platform for dealing with streaming/non-stored data.
Pieces of data, apparently called events, are sent and consumed by a Processing Element (yes, PE, but not the kind that requires you to sweat). The PEs can do one of two things:
emit another event that will be consumed by another PE, or
publish some result
Streaming data is different from non-streaming data in that the user does not know how much data will be transmitted, and at what rate. Analysis on […]
This week I had the opportunity the trek up north to Silicon Valley to attend Yahoo’s Hadoop Summit 2010. I love Silicon Valley. The few times I’ve been there the weather was perfect (often warmer than LA), little to no traffic, no road rage and people overall seem friendly and happy. Not to mention there are so many trees it looks like a forest!
The venue was the Hyatt Regency Great America which seemed like a very posh business hotel. Walking into the lobby and seeing a huge crowd of enthusiasts and an usher every two steps was overwhelming!
After being welcomed by the sound of the vuvuzela, we heard about some statistics about the growth of Hadoop over the years. Apparently in 2008 there were 600 attendees to Hadoop Summit and in 2010 attendance grew to 1000. The conference started with three hours of keynotes and sessions that everyone attended in one room. The tone was somewhat corporate in nature, but there were also several gems in there about how Yahoo uses Hadoop:
A user’s click streams are mapped to latent interests, similar to mapping words to interests in topic models like LSA and LDA. Yahoo uses Hadoop to recompute a user’s […]
A week or so ago I had my first experience using someone else’s cluster on Amazon EC2. EC2 is the Amazon Elastic Compute Cloud. Users set up a virtual computing platform that runs on Amazon’s servers “in the cloud.” Amazon EC2 is not just another cluster. EC2 allows the user to create a disk image containing an operating system and all of the software they need to perform their computations. In my case, the disk image would contain Hadoop, R, Python and all of the R and Python packages I need for my work. This prevents the user (and the provider) from having to worry about providing or upgrading software and having compatibility issues.
No subscription is required. Users pay for the amount of resources used for the computing session. Hourly prices are very cheap, but accrue quickly. Additionally, Amazon charges for pretty much everything single thing you can do with an OS: transferring data to/from the cloud per GB, data storage per GB, CPU time per hour per core etc.
This is somewhat of a tangent, but EC2 was a brilliant business move in my opinion.
Anyway, life gets a bit more difficult when the EC2 instance you’re working with […]