Skip to main content

Hits and Miss among Hadoop use cases in 2012

2012 has been the year when Apache Hadoop hype reached its maximum peak till date. Along with the hype, came a lot of success stories around the adopted use cases. Let’s take a look at some of the Hits and Miss of Hadoop use cases.





Hits

1)      Seismic image processing

Chevron continued to demonstrate the huge technology initiatives if has taken with the help of Apache Hadoop. One of the major initiatives which gathered lime light (in 2012, though started earlier) was the processing of seismic data to detect presence of oil reservoirs.
source:: IBM Almaden

Under the sea subsurface information is collected by aggregating a large number of estimates from seismic waves. The process of creation of higher quality subsurface images has leveraged Apache Hadoop as a cheap and reliable technology option.


2)      Travel analytics:

Travel portals leveraged ApacheHadoop as one of the big consumers of this technology. With major projects undertaken in analytics and machine learning for online travel, the IT brains behind the portals leveraged the data crunching capabilities to the hilt.

Selective stories in online media combined with aggressive Hadoop skills hiring buzz continued to emphasize that Apache Hadoop is a definite hit with online travel.


Miss:


1)      Tsunami and Earthquake forecasting:

One of the grey areas where we would have liked Apache Hadoop to make a bigger impact was Tsunami and earthquake forecasting. We all realize the huge data sets that this problem relies on and how Apache Hadoop could be one of the prime candidates for processing. However, unfortunately, we failed to hear on any significant Apache Hadoop presence except for random interest sessions.

2)      Olympics problem:

The Olympics games at London was the biggest event in terms of Big Data generation through the year 2012. One of the big anxieties that was generated with Olympics was telecom network congestion. 80000 tweets per minute when Usain Bolt raced to glory and 118 million messages on San Weibo for opening ceremony – the data and statistics were just astounding.
But we failed to see an approach where Apache Hadoop could have been leveraged to predict network traffic or make provisioning arrangements during traffic peaks.

However, let’s not lose hope over here. Through the next few months, we will try to bring exclusive architecture approach for Tsunami forecasting and Olympics/Real life event mobile network provisioning. 

As we enter 2013, we see hope for many success stories and it will only be appropriate if hadoopsphere.com can contribute its little bit by demonstrating the power of IT for real life problems. So, stay tuned in 2013; keep mapping the real life problems while we all try to reduce their impacts.

Comments

Popular posts from this blog

Hadoop's 10 in LinkedIn's 10

LinkedIn, the pioneering professional social network has turned 10 years old. One of the hallmarks of its journey has been its technical accomplishments and significant contribution to open source, particularly in the last few years. Hadoop occupies a central place in its technical environment powering some of the most used features of desktop and mobile app. As LinkedIn enters the second decade of its existence, here is a look at 10 major projects and products powered by Hadoop in its data ecosystem.
1)      Voldemort:Arguably, the most famous export of LinkedIn engineering, Voldemort is a distributed key-value storage system. Named after an antagonist in Harry Potter series and influenced by Amazon’s Dynamo DB, the wizardry in this database extends to its self healing features. Available in HA configuration, its layered, pluggable architecture implementations are being used for both read and read-write use cases.
2)      Azkaban:A batch job scheduling system with a friendly UI, Azkab…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…