Small companies, big data.

Big data is sometimes at odds with the business-savvy entrepreneur who wants to exploit its full potential.   In essence, the business potential of big data is the massive (but promising) elephant in the room that remains invisible because the available talent necessary to take full advantage of the technology is difficult to obtain.

Inventing new technology for the platform is critical, but so too is making it easier to use.

The future of big data may not be a technological breakthrough by a select core of contributing engineers, but rather a platform that allows common, non-PhD holding entrepreneurs and developers to innovate.  Some incredible progress has been made in Apache Hadoop with Hortonworks’ HDP (Hortonworks Data Platform) in minimizing the installation process required for full implementation.  Further, the improved MapReduce v2 framework also greatly lowers the risk of adoption for businesses by expressly creating features designed to increase efficiency and usability (e.g.…

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As organizations continue to ramp the number of MapReduce jobs processed in their Hadoop clusters, we often get questions about how best to share clusters. I wanted to take the opportunity to explain the role of Capacity Scheduler, including covering a few common use cases.

Let me start by stating the underlying challenge that led to the development of Capacity Scheduler and similar approaches.

As organizations become more savvy with Apache Hadoop MapReduce and as their deployments mature, there is a significant pull towards consolidation of Hadoop clusters into a small number of decently sized, shared clusters. This is driven by the urge to consolidate data in HDFS, allow ever-larger processing via MapReduce and reduce operational costs & complexity of managing multiple small clusters. It is quite common today for multiple sub-organizations within a single parent organization to pool together Hadoop/IT budgets to deploy and manage shared Hadoop clusters.…

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As Apache Hadoop has risen in visibility and ubiquity we’ve seen a lot of other technologies and vendors put forth as replacements for some or all of the Hadoop stack. Recently, GigaOM listed eight technologies that can be used to replace HDFS (Hadoop Distributed File System) in some use cases. HDFS is not without flaws, but I predict a rosy future for HDFS.  Here is why…

To compare HDFS to other technologies one must first ask the question, what is HDFS good at:

• Extreme low cost per byte
  HDFS uses commodity direct attached storage and shares the cost of the network & computers it runs on with the MapReduce / compute layers of the Hadoop stack. HDFS is open source software, so that if an organization chooses, it can be used with zero licensing and support costs. This cost advantage lets organizations store and process orders of magnitude more data per dollar than tradition SAN or NAS systems, which is the price point of many of these other systems.  

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Last week was an important milestone for Hortonworks: our one year anniversary. Given all of the activity around Apache Hadoop and Hortonworks, it’s hard to believe it’s only been one year. In honor of our birthday, I thought I would look back to contrast our original intentions with what we delivered over the past year.

Hortonworks was officially announced at Hadoop Summit 2011. At that time, I published a blog on the Hortonworks Manifesto. This blog told our story, including where we came from, what motivated the original founders and what our plans were for the company. I wanted to address many of the important statements from this blog here:

Hortonworks was formed to “accelerate the development and adoption of Apache Hadoop”. I returned to this point often throughout the manifesto. We committed to working with the community to accelerate the development and adoption of Apache Hadoop and we absolutely delivered on this promise.…

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The following is Part 2 of 2 on data in education. The first article introduces the concept and application of data in education. The second article looks at recent movements by the Department of Education in data mining, modeling and learning systems.

Big data analytics are coming to public education. In 2012, the US Department of Education (DOE) was part of a host of agencies to share a $200 million initiative to begin applying big data analytics to their respective functions. The DOE targeted its $25 million share of the budget toward efforts to understand how students learn at an individualized level. This segment reviews the efforts enumerated in the draft paper released by the DOE on their big data analytics.

The ultimate goal of incorporating big data analytics in education is to improve student outcomes – as determined common metrics like end-of-grade testing, attendance, and dropout rates.…

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Big Data Shopping Bag

With big data basking in the limelight, it is no surprise that large retailers have been closely watching its development… and more power to them! By learning to effectively utilize big data, retailers can significantly mold the market to their advantage, making themselves more competitive and increasing the likelihood that they will come out on top as a successful retailer. Now that there are open source analytical platforms like Hadoop, which allow for unstructured data to be transformed and organized, large retailers are able to make smart business decisions using the information they collect about customers’ habits, preferences, and needs.

As IT industry analyst Jeff Kelly explained on Wikibon, “Big Data combined with sophisticated business analytics have the potential to give enterprises unprecedented insights into customer behavior and volatile market conditions, allowing them to make data-driven business decisions faster and more effectively than the competition.” Predicting what customers want to buy, without a doubt, affects how many products they want to buy (especially if retailers add on a few of those wonderful customer discounts).…

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We’re heading to our very first OSCON conference to talk all things Apache Hadoop, the biggest gathering for the entire open source community in Portland, Oregon, and we would love to meet you there!

Meet our founders, Arun Murthy and Mahadev Konar, along with others from the Hortonworks team at this year’s conference.

There are many ways to meet the Hortonworks team and we would love to chat with you about how you are considering using Hadoop.

We’ll be speaking!

Arun Murthy will be presenting “Apache Hadoop- The future is Now” on Wednesday, July 18 @ 10:40am in Portland 252

Mahadev Konar will present “ Apache ZooKeeper in Action” on Wednesday, 7/18 @ 2:30pm in D139-140

And hosting!

Birds of a Feather (BoF) session on the Next Generation of Apache Hadoop, Wednesday 7/18 @ 7pm

And we’re exhibiting!

Come by booth #207, say hello, geek out to Hadoop and big data and pick up an awesome shirt while you’re at it.…

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Working code examples for this post (for both Pig 0.10 and ElasticSearch 0.18.6) are available here.

ElasticSearch makes search simple. ElasticSearch is built over Lucene and provides a simple but rich JSON over HTTP query interface to search clusters of one or one hundred machies. You can get started with ElasticSearch in five minutes, and it can scale to support heavy loads in the enterprise. ElasticSearch has a Whirr Recipe, and there is even a Platform-as-a-Service provider, Bonsai.io.

Apache Pig makes Hadoop simple. In a previous post, we prepared the Berkeley Enron Emails in Avro format. The entire dataset is available in Avro format here: https://s3.amazonaws.com/rjurney.public/enron.avro. Lets check them out:…

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The following is Part 1 of 2 on data in education.  The first article introduces the concepts of how data is used in education.  The second article looks at recent movements by the Department of Education in data mining, modeling and learning systems.

Learning to Learn

The education industry is transforming into a 21st century data-driven enterprise.   Metrics based assessment has been a powerful force that has swept the national education community in response to widespread policy reform.  Passed in 2001, the No-Child-Left-Behind Act pushed the idea of standards-based education whereby schoolteachers and administrators are held accountable for the performance of their students.  The law elevated standardized tests and dropout rates as the primary way officials measure student outcomes and achievement.  Underperforming schools can be placed on probation, and if no improvement is seen after 3-4 years, the entire staff of the school can be replaced.…

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What lessons might the anime (Japanese animation) “Ghost in the Shell” teach us about the future of big data?  The show, originally a graphic novel from creator Masamune Shirow, explores the consequences of a “hyper”-connected society so advanced one is able to download one’s consciousness temporarily into human-like android shells (hence the work’s title).  If this sounds familiar, it’s because Ghost in the Shell was a major point of inspiration for the Wachowski brothers, the creators of the  Matrix Trilogy.

The ability to handle, process, and manipulate big data is a major theme of the show and focuses on the challenges of a high tech police unit in thwarting potential cyber crimes.  The graphic novel was originally created in 1991, long before the concept of big data had grown to prominence (and for-all-intents-and-purposes even before what we now think of as the internet…)

Visions of a “Big Data” Future

While such visions of an interconnected techno-future are common in anime, what makes Ghost in the Shell special is its treatment of the power of big data. …

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I wanted to take this opportunity to say thanks to the more than 2,200 attendees, speakers and sponsors that helped to make Hadoop Summit 2012 a great success. There was tremendous buzz throughout the conference; exceeding the excitement levels of all past Hadoop conferences. It’s a great indicator for the future of Apache Hadoop and the broader big data ecosystem.

The content from this conference was outstanding, from the opening keynotes to the last round of breakout sessions. I wanted to thank the track chairs (Abhishek Mehta, Ashish Thusoo, Avik Dey, Ben Reed, Peter Sirota and Val Bercovici) for making the hard decisions that led to such an outstanding agenda. I thought the group did a great job selecting the right mix of technical, use case and best practices sessions for developers, operators and analysts. I would also like to thank the more than 110 speakers for putting in the time and effort to share their Apache Hadoop experiences.…

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What’s possible with all this data?

Data Integration is a key component of the Hadoop solution architecture. It is the first obstacle encountered once your cluster is up and running. Ok, I have a cluster… now what? Do I write a script to move the data? What is the language? Isn’t this just ETL with HDFS as another target?Well, yes…

Sure you can write custom scripts to perform a load, but that is hardly repeatable and not viable in the long term. You could also use Apache Sqoop (available in HDP today), which is a tool to push bulk data from relational stores into HDFS. While effective and great for basic loads, there is work to be done on the connections and transforms necessary in these types of flows. While custom scripts and Sqoop are both viable alternatives, they won’t cover everything and you still need to be a bit technical to be successful.…

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Series Introduction

This is part three of a series of blog posts covering new developments in the Hadoop pantheon that enable productivity throughout the lifecycle of big data.  In a series of posts, we’re exploring the full lifecycle of data in the enterprise: Introducing new data sources to the Hadoop filesystem via ETL, processing this data in data-flows with Pig and Python to expose new and interesting properties, consuming this data as an analyst in Hive, and discovering and accessing these resources as analysts and application developers using HCatalog and Templeton.

• Series Part One: Avroizing the Enron Emails. In that post, we used Pig to extract, transform and load a MySQL database of the Enron emails to document format and serialize them in Avro.The Enron emails are available in Avro format here.
• Series Part Two: Mining Avros with Pig, Consuming Data with Hive.

…

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Weather Hurts

Catastrophic weather events like the historic 2011 floods in Pakistan or prolonged droughts in the horn of Africa make living conditions unspeakably harsh for tens of millions of families living in these affected areas.  In the US, the winter storms of 2009-2010 and 2010-2011 brought record-setting snowfall, forcing mighty metropolises into an icy standstill. Extreme weather can profoundly impact the human kind.

The effects of extreme weather can send terrible ripples throughout an entire community.  Unexpected cold snaps or overly hot summers can devastate crop yields and forcing producers to raise prices. When food prices rise, it becomes more difficult for some people to earn enough money to provide for their families, creating even larger problems for societies as a whole.

The central problem is the inability of current forecasting models to more accurately predict large-scale weather patterns.  Weathermen are good at predicting weather but poor at predicting climate. …

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Why genomics?

Big data. These are two words the world has been hearing a lot lately and it has been in relevance to a wide array of use cases in social media, government regulation, auto insurance, retail targeting, etc. The list goes on. However, a very important concept that should receive the same (if not more) recognition is the presence of big data in human genome research.

Three billion base pairs make up the DNA present in humans. It’s probably safe to say that such a massive amount of data should be organized in a useful way, especially if it presents the possibility of eliminating cancer. Cancer treatment has been around since its first documented case in Egypt (1500 BC) when humans began distinguishing between malignant and benign tumors by learning how to surgically remove them. It is intriguing and scientifically helpful to take a look at how far the world’s knowledge of cancer has progressed since that time and what kind of role big data (and its management and analysis) plays in the search for a cure.…

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