Skip to main content

Top 5 Big Data and Hadoop Trends in 2014

As the year 2014 bid us goodbye, let’s uncover some of the key trends that dominated the big data and hadoop arena during the year. There were some key themes that came to the fore and considering big data is dominating the technology investments now, these trends are indicative of the path that the entire information technology world is taking.

(1)    Real time was the flavor of the year:

Much has been written about real time big data analytics or rather, the lack of it in traditional MapReduce world. There were able products in the form of Apache Tez, Cloudera Impala, IBM BigSQL, Apache Drill and Pivotal HAWQ that were unleashed in 2013. And as the adage goes, 2013 was history in 2014. Apache Spark took center stage and ensured that everyone talked about near-real-time at least. Apache Storm also got its associated lime light alongside and the rest in the streaming pack within industry. Real time big data is here and it is here to get better with each product’s release.

(2)    R&D came to the fore:

Not just Google, Microsoft, IBM, SAP and the likes, many other exciting labs are coming to the party and investing huge dollars in big data R&D. Machine learning is passe as the real interest shifts to deep learning. Backed by years of research interest in artificial intelligence, neural networks and more, the R&D in deep learning has found a new zest. Large industry players like AT&T Research as well as emerging companies like Impetus Technologies continued to invest in big data warehouse research and brought in senior executives from other companies (like IBM) to ensure they research it right and develop it hard.

(3)    The big boys kept struggling:

Big data has never been a ground that has been dominated by the big boys of IT. Rather the new kids on the block, Cloudera, Hortonworks and MapR have dominated the space and continued to do so in 2014. With a billion $ IPO under Hortonworks’ belt, things have never been so good for emerging product companies in the technology sector. These new companies are here to stay and give many sleepless nights to the sales execs of established product companies.

(4)    It’s a man’s world:

Strange but true – whether you visit a conference, seminar or development shop of big data, there have hardly been any women in the arena. Call it the invisible ceiling within big data industry but with an exception of Anjul Bhambhri, it is rare to see a woman dominating the scene. Even the proportion of women developers/architects/managers seems to be abysmally low – something we hope to see corrected in 2015 as more people take up Hadoop skills.

(5)    The shift in services world:


Ah, the cream of the revenue pie – professional services and consulting. Not just the product world, the services world has shown some interesting trends in emergence of new players. Big data services unlike traditional analytics is still dominated by specialized players rather than CMMi certified IT majors. By 2015 end, we should be able to see some new big names on the horizon. These companies do not have armies of certified professionals but rather have been establishing themselves by delivering successful big data specialized solutions from small experienced teams.


Comments

Popular posts from this blog

In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…

Amazon DynamoDB datastore for Gora

What was initially suggested during causal conversation at ApacheCon2011 in November 2011 as a “neat idea”, would soon become prime ground for Gora's first taste of participation within Google's Summer of Code program. Initially, the project, titled Amazon DynamoDB datastore for Gora, merely aimed to extend the Gora framework to Amazon DynamoDB. However, it seem became obvious that the issue would include much more than that simple vision.

The Gora 0.3 Toolbox We briefly digress to discuss some other noticeable additions to Gora in 0.3, namely: Modification of the Query interface: The Query interface was amended from Query<K, T> to Query<K, T extends Persistent> to be more precise and explicit for developers. Consequently all implementors and users of the Query interface can only pass object's of Persistent type. Logging improvements for data store mappings: A key aspect of using Gora well is the establishment and accurate definitio…

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…