Why is Hadoop dying?

Hadoop has been the definitive big data platform for some time. The name has practically been synonymous with the field. But while its ascent followed the trajectory of what was referred to as the ‘big data revolution’, Hadoop now seems to be in danger. The question is everywhere – is Hadoop dying out? And if it is, why is it? Is it because big data is no longer the buzzword it once was, or are there simply other ways of working with big data that have become more useful?

Hadoop was essential to the growth of big data

When Hadoop was open sourced in 2007, it opened the door to big data. It brought compute to data, as against bringing data to compute. Organisations had the opportunity to scale their data without having to worry too much about the cost. It obviously had initial hiccups with security, the complexity of querying and querying speeds, but all that was taken care off, in the long run. Still, although querying speeds remained quite a pain, however that wasn’t the real reason behind Hadoop dying (slowly).

As cloud grew, Hadoop started falling

One of the main reasons behind Hadoop’s decline in popularity was the growth of cloud. There cloud vendor market was pretty crowded, and each of them provided their own big data processing services. These services all basically did what Hadoop was doing. But they also did it in an even more efficient and hassle-free way. Customers didn’t have to think about administration, security or maintenance in the way they had to with Hadoop.

One person’s big data is another person’s small data

Well, this is clearly a fact. Several organisations that used big data technologies without really gauging the amount of data they actually would need to process, have suffered. Imagine sitting with 10TB Hadoop clusters when you don’t have that much data. The two biggest organisations that built products on Hadoop, Hortonworks and Cloudera, saw a decline in revenue in 2015, owing to their massive use of Hadoop. Customers weren’t pleased with nature of Hadoop’s limitations.

Apache Hadoop v Apache Spark

Hadoop processing is way behind in terms of processing speed. In 2014 Spark took the world by storm. I’m going to let you guess which line in the graph above might be Hadoop, and which might be Spark. Spark was a general purpose, easy to use platform that was built after studying the pitfalls of Hadoop. Spark was not bound to just the HDFS (Hadoop Distributed File System) which meant that it could leverage storage systems like Cassandra and MongoDB as well. Spark 2.3 was also able to run on Kubernetes; a big leap for containerized big data processing in the cloud. Spark also brings along GraphX, which allows developers to view data in the form of graphs. Some of the major areas Spark wins are Iterative Algorithms in Machine Learning, Interactive Data Mining and Data Processing, Stream processing, Sensor data processing, etc.

Machine Learning in Hadoop is not straightforward

Unlike MLlib in Spark, Machine Learning is not possible in Hadoop unless tied with a 3rd party library. Mahout used to be quite popular for doing ML on Hadoop, but its adoption has gone down in the past few years. Tools like RHadoop, a collection of 3 R packages, have grown for ML, but it still is nowhere comparable to the power of the modern day MLaaS offerings from cloud providers. All the more reason to move away from Hadoop, right? Maybe.

Hadoop is not only Hadoop

The general misconception is that Hadoop is quickly going to be extinct. On the contrary, the Hadoop family consists of YARN, HDFS, MapReduce, Hive, Hbase, Spark, Kudu, Impala, and 20 other products. While e folks may be moving away from Hadoop as their choice for big data processing, they will still be using Hadoop in some form or the other. As with Cloudera and Hortonworks, though the market has seen a downward trend, they’re in no way letting go of Hadoop anytime soon, although they have shifted part of their processing operations to Spark.

Is Hadoop dying? Perhaps not…

In the long run, it’s not completely accurate to say that Hadoop is dying. December last year brought with it Hadoop 3.0, which is supposed to be a much improved version of the framework. Some of the most noteworthy features are its improved shell script, more powerful YARN, improved fault tolerance with erasure coding, and many more. Although, that hasn’t caused any major spike in adoption, there are still users who will adopt Hadoop based on their use case, or simply use another alternative like Spark along with another framework from the Hadoop family. So, Hadoop’s not going away anytime soon.

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