Skip to main content

Additional dimensions of Big Data - discussion revived once more


We have earlier as well heard of Big Data dimensions beyond the conventional 3 popularized by IBM and the akin: Volume, Variety and Velocity. The discussion is now revived once more after Mark Beyer, Gartner Research VP, addressed the gathering at Oracle Open World 2012 and brought his 12 dimensional framework for Big Data to the fore.

(Based on what has been heard, during the same session George Lumpkin, Vice President, Product Management also talked in grand Oracle fashion about Oracle’s In-Database Hadoop claims while the purists whispered their genuine queries on real time processing.

However, besides the hype was the informative talk from Mark who was co-hosting the session with George.)

To understand better what Mark said, let’s dust off a Gartner report from 2011 which says that Big Data is only the beginning of extreme information management.

According to Gartner’s research team comprising of Mark Beyer, Anne Lapkin, Nicholas Gall, Donald Feinberg, Valentin T. Sribar,  Big Data has more often than not been defined under various terms which include Real-time data, Shared data (data shared across apps), Linked data (inter-related data from various sources) and High-fidelity data (containing context, detail, relationships and identities of important business info).

Extreme information management, according to them, pertains to dealing with issues across a dozen different dimensions in three categories: quantification, access and quality assurance.

Quantification:
Volume of data
Velocity of data streams, access demands and record creation
Variety of data formats
Complexity of individual data types (standards, domain rules, storage formats for each asset type)

Access enablement and control:
Classification (sensitive/non-sensitive, private/public classifications and so on)
Contracts (agreements on who will share information and how)
Pervasiveness (how long does data remain active)
Technology-enablement (specifications for tools and technology)

Qualification and assurance:
Fidelity (ability or inability to confidently adapt an asset for wider use)

Linked data (data in combination and the uses related to this context)

Validation of data (ensuring data is valid per use case)
Perishability (longevity, aging of data while retaining its state and character)


In a nutshell, the Extreme Information Management framework definitely poses a unique opportunity to assess the key challenges faced in the Big Data explosion. CIOs must recognize the signs of these challenges while tapping data for a competitive advantage.  From a pure analyst perspective, we know there is no single tool that fits the bill per this framework.  Today it requires a huge environment to be in place to meet these needs and challenges posed by the Big Data world. We just hope as the ecosystem matures up, we can start putting more tick marks on each enterprise’ EA against this dimension list. 

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…