Hadoop, named after a toy elephant that belonged to the child of one its inventors, is an open-source software framework. It is capable of storing colossal amounts of data and handling massive applications and jobs endlessly. Hadoop’s capabilities make it one of the most sought after data platforms for successful businesses all over the world.
Hadoop Benefits
Because it can store and quickly process any type of data, Hadoop is lightyears ahead of the game in the open-source world. Data is increasing and changing everyday due to social media inventions, new mobile devices, and technological advancements. Here are a few more benefits it exudes:
The Role of Hadoop in Big Data Analytics
Because Hadoop can handle enormous amounts of data of any kind, it has the capability to do analytical algorithms. It can help your business run smoother, discover new developments, and analyze advantages over your competitors. Web-based recommendation is derived from Hadoop. It is how Facebook suggests friends that you may know; how LinkedIn shows you jobs that may be of interest to you; and how eBay can predict which items you might want to bid on.
Although Hadoop is a free platform, the need for commercial distribution is growing. It will tackle any issues with the open source version of it such as the following:
Vendors Enjoying Growth by Commercially Distributing Hadoop
These are the leading Hadoop vendors who will contribute to its effectiveness in big data analytics over the next few years:
Hadoop is definitely a powerhouse in the open source world. It has capabilities that far exceed what the other data platforms can do. Its performance is increasing revenue, creating new jobs, and setting records.
What do you think about Hadoop and its distribution? Post a comment and let us know what you think.
Comment
Spark and Hadoop's MapReduce have complementary aspects. Spark is faster than Hadoop as long as data can stay in main memory which is a limitation. Considering main memory is much smaller than HDDs, this translates into a more expensive cluster as you will need more nodes for the same dataset size. This also makes your cluster more expensive as main memory is more expensive than HDDs.
A case where Spark is at a clear advantage over Hadoop's MapReduce is for a streaming data processing pipeline where you expect the newly acquired data to be fairly small in comparison to the whole and data does not need to stay in the "pipe". Another aspect is training models with Machine Learning algos which very often are highly iterative or at the very least need several passes over the data. Since the data can be cached, read overheads are likely affecting execution only once.
Hi Benjamin, I truly appreciate your comments. MapR is indeed a great solution and I will definitely consider including that in my future article. Thanks for your insight. Best, Sheza.
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