Skip to main content

Hot Hadoop Trends in first half of 2013

By Sachin Ghai

With 6 months of 2013 behind us, here is a look back at some of the key trends which emerged in Big Data and Hadoop ecosystem in first half of the year. The infographic below is available in poster size for printing and can be downloaded by clicking on the image.




1- MapReduce programs in more languages

With the wider buzz around Hadoop and MapReduce, programmers from various languages and platforms are incorporating MapReduce in the programming paradigm. Beyond Java, Scala, C/C++, there is wider interest in Python, Lua and other languages as well.
                               
2- Developer friendliness

Developers have cribbed quite a few things about this ecosystem. Windows non-compatibility was one of major rues. There has been some respite for them. SQL on Hadoop also gave them an excuse to not learn HQL, Pig and JAQL.

3- Social use cases emerge

Corporates have been using Hadoop for varied use cases including games, sentiment analysis. However, there has been emergence of better use cases like tracking human traffickers, bitcoin abusers, sewage management and other criminal intents.

4- VCs remain skeptical in rest of the world

While VCs remain upbeat about Hadoop and Big Data in USA, the rest of the world continues to show wait and watch before boarding the bus. There is wider skepticism regarding revenues which is probably characteristic in non-US VCs. The only show-cases yet are traditional analytics renamed as Big data firms or global subsidiaries of US start-ups.


5- OSS trend continues to dominate

While EMC may have bucked the trend for Open source, the fad and business model still holds good ground around OSS. Hortonworks leads the pack with contributions coming in from Linkedin, Netflix also in Apache fold. Cloudera, MapR and IBM also continued their OSS patronage.


6- Demand v/s supply

There is an oft-quoted gap in demand and supply of Hadoop professionals. Well, the truth extends a bit more. There is a gap between Big Data interest, PoC, actual deliveries and business users’ education. Many Hadoop professionals in the technology services world remain unallocated waiting for deals to be signed in 2nd  half of 2013- nothing akin to Java demand. The fancied product world holds another promise for another beta delivery.

7- Confused state of Analysts

From ‘trough of disillusionment’ to the most exciting technology wave of this decade, the research analysts have been slightly muddled up in the opinion. It is no secret that research firms continued to get tonnes of queries on Hadoop but still there is no unanimous view on the ecosystem.

8- Anything unstructured becomes Big Data

Reaping on the popularity of the ‘Big Data’ term and lack of clear, concise definition, everyone and anyone has jumped on the bandwagon. It is not a surprise that traditional analytics firm have rechristened their offerings- for the heck of it or rather, for the buck of it.

9- Big Data enters popular lexicon

Courtesy NSA, Big Data has entered the popular lexicon. Reporting on Hadoop and Big Data technologies is not restricted to the likes of Silicon Angle, InformationWeek now. It is now being reported by Salon et al. Don’t be surprised if Big Data is Time person of the year.

10- SQL, SQL everywhere; not a line of code to write

We have heard so much of this during this year that you almost feel amazed by the spike in interest in SQL on Hadoop. And as one portal reported, “The hot new technology in Big Data is decades old: SQL”
Labels:

Comments

Post a Comment

Popular posts from this blog

In-memory data model with Apache Gora

By Sachin Ghai
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…
4 comments
Read more

Data deduplication tactics with HDFS and MapReduce

By Desk Editor
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…
Post a Comment
Read more

Amazon DynamoDB datastore for Gora

By Sachin Ghai
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…
1 comment
Read more