Skip to main content

Hot Hadoop Trends in first half of 2013


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”

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 …