Skip to main content

Making sense of Hype Cycle for Big Data



A few days back Gartner published Hype Cycle for Big Data,2012  and Hype Cycle for Cloud Computing,2012. We reviewed them and compared it with 2011 report and here are a few key new take-away from the 2012 Gartner reports.

The good news which had been expected by research followers is that Big Data has moved from ‘On the Rise’ to ‘At the Peak’.

However, the report goes on to add that Big Data will probably soon move into the Trough of Disillusionment in 2012 where it will mature in terms of offerings, solutions and technology maturity.  This was stated in its Hype Cycle for Cloud Computing, 2011 report also but seems Gartner is now more upbeat about technology players because it goes on to add “However, big data should spend very little time in the trough”.

The Hype Cycle for Cloud Computing, 2012 report also states that there will be attempts to combine MapReduce with Graph as well as natural-language processing and text analytics. Refer one of our articles on Facebook’s one such attempt among its many successful use cases.
The slight worrisome statement from the report is that “big data assets, such as images, video, sound and even three-dimensional object modeling, will also drive big data into the trough”. We know a lot of research is already happening on this and have in this site tried to cover one such architectural solution for Hadoop usage in video, sound search and modeling.

As in many other technology product life cycles, Gartner predicts that specialized technologies could become mainstream while newer technologies will emerge as the next major big data issue surfaces up.

In the Hype Cycle for Big Data, 2012, Gartner attributes one of the major reasons for increased focus on Big Data to “increased availability of scalable, elastic resources in the cloud have allowed organizations to begin big data projects without investing in infrastructure.”

It further details out 3 categories:
Entries that describe enabling technologies for big data
-          among the others, the one to note is column-store DBMS.
o        Refer our post on one such solution which uses column-store DBMS for higher performance

Entries that describe typical use cases for big data
-          among the other, the one catching attention is telematics

Entries that describe new information types, sources and roles
-          on expected lines, we know Data Scientist should feature here


For complete text, you may refer the 2012 reports at the links given above.  



Going a step further, we have applied Map Reduce on Hype Cycle for Big Data, 2012 report. We took all statements where Sample Vendors were suggested by Gartner in the technologies shown in Hype Cycle image at top. Our algorithm returned a count on how many times a vendor has been suggested in the report. The image below shows a graph generated by the data from MapReduce algorithm to arrive at a vendor mention count. (click on image to view full image).



: View full image on Pinterest




Comments

Popular posts from this blog

Low latency SQL querying on HBase

HBase has emerged as one of the most popular NoSQL database offering distributed, versioned, non-relational tables hosted on commodity hardware. However, with a large set of users coming from a relational SQL world, it made sense to bring the SQL back in this NoSQL. With Apache Phoenix, database professionals get a convenient way to query HBase through SQL in a fast and efficient manner. Continuing our discussion with James Taylor, the founder of Apache Phoenix, we focus on the functional aspects of Phoenix in this second part of interaction.
Although Apache Phoenix started off with distinct low latency advantage, have the other options like Hive/Impala (integrated with HBase) caught up in terms of performance?
No, these other tools such as Hive and Impala have not invested in improving performance against HBase data, so if anything, Phoenix's advantage has only gotten bigger as our performance improves.  See this link for comparison of Apache Phoenix with Apache Hive and Cloudera Im…

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…

Pricing models for Hadoop products

A look at the various pricing models adopted by the vendors in the Hadoop ecosystem. While the pricing models are evolving in this rapid and dynamic market, listed below are some of the major variations utilized by companies in the sphere.
1) Per Node:Among the most common model, the node based pricing mechanism utilizes customized rules for determining pricing per node. This may be as straight forward as pricing per name node and data node or could have complex variants of pricing based on number of core processors utilized by the nodes in the cluster or per user license in case of applications.
2) Per TB:The data based pricing mechanism charges customer for license cost per TB of data. This model usually accounts non replicated data for computation of cost.
3) Subscription Support cost only:In this model, the vendor prefers to give away software for free but charges the customer for subscription support on a specified number of nodes. The support timings and level of support further …