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Add Spark to Your Big Data Endeavour

Posted by analytixlabs on January 13th, 2017

There have been prophecies about the future of the big data industry since the beginning of the decade and we can pretty much rest assured that those prophecies are not going to be proved wrong. So every company that is planning to go for gold is resorting to big data analytics for a better map to venture with. Now that the industry has reached a billion mark and both the inherent big data units and external vendors are finding it hard to keep pace with themselves, tools like Apache Spark should be more than welcome. Spark introduces incredible speed to large scale data processing; speed and cost effectiveness that can really make a difference.

Spark v/s Hadoop

This is not a place for controversies but some comparisons are hard to avoid. News is Spark provides a data processing speed that is 100 times faster than that of Hadoop’s Mapreduce. Now this is some serious edge that Spark gives you. Spark big data training surely puts you among the supersonic group of analysts. Spark also requires fewer resources to function, which makes it way more cost effective. When it comes to the question cost effectiveness in data storage HDFS is the ultimate answer but in case of data processing Spark takes the crown away. The biggest advantage of Spark yet remains to be unveiled. It is the compatibility factor. It can work within a Hadoop environment just as Mapreduce does. It can also work with resource managers like YARN or Mesos, which Mapreduce cannot. Apart from this Spark also makes it easy to write programs. Its APIs support Scala, JAVA, Python as well as SQL. The ‘in-memory’ technology that Spark uses is really showing the advanced way of data processing.

Having said all that, one must admit that Hadoop is still the gold standard for industry usage for its reliability and the wide range of tools that it offers. Spark is still quite young and we can conjecture that with future developments it will be ready to rule the industry.

Spark and advanced analytics

Advanced analytics is something that all companies aspire to apply but few can. It is very hard to employ advanced analytics with traditional frameworks and yet harder to find people that can do that. Spark makes it simpler and easier with pre built machine learning libraries, streaming analytics engine and a graph processing engine.

Is Spark the future of Bid data analytics? 

This is one of the most asked questions that you find on the web about Spark. The speed, flexibility and cost effectiveness, Spark brings on the platter validates the question. But in an industry as fluid as the big data analytics, making an assumption is an effort likely to be wasted. The future itself will tell us its course. The advice to the analytics aspirants would be – get your excellent Spark Big data training done. Your future may or may not depend upon it; why take the risk? 

Also See: Big Data, Data Processing, Data Analytics, Cost Effectiveness, Spark, Data, Big

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