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Why Spark has become indispensable for data scientists?

Published on 09 May 17
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Spark has completely changed the way big data is analyzed and monitored. It has added to the capabilities of enterprises to monitor, take decisions on the basis of data and execute them while data is yet to enter the database.
Apache Spark is the elixir of big data today. It has completely revolutionized the way big data are handled and monitored in the present times when speed is everything. The enterprises are falling on each other to adopt Spark analytics, which gives them, the convenience of analyzing the data in the data stream itself. Even before the data enters the database, it has not only been identified, monitored, analyzed and action initiated based on the information to effect the desired changes in the real time. Indeed, that is possible due to the speed of Spark which is 100 times faster than Hadoop and gives data scientists the liberty to merge several data streams into one and that too in distributed data environment.
The Spark is indeed the most active open source project in the big data which is now being generated much faster than a few years back. The biggest challenge for enterprises is that how to translate the big data analysis into actual profit. This is where spark chips in. The number of startups which are big data centric has increased phenomenally and they are all dependent upon Spark for stream analytics. The large firms are entering the field of big data analytics after the arrival of Spark on the scene. Indeed Spark is one of the most active open source project in big data. It has surpassed Hadoop and is ruling the big data stream analytics today.
Spark Streaming makes the batch processing redundant
The reason why Spark is so much in demand is that it has revolutionized the way the big data is monitored and analyzed. Earlier, when batch processing was in vogue, data were collected and processed which took a lot of time. Often night shifts were used to analyze the data generated on that day. But Spark completely changed the way data is analyzed. Thanks to Apache Spark steaming, data is analyzed on the go, in the real time. The data which were earlier useless before entering the database data is being monitored and manipulated to add to extra bucks to the accounts of the enterprises. Spark makes it possible for instance to change the prices of flight tickets based on the user preferences of airlines, days, timings etc.
Many data, scientists who were earlier working with Storm or Hadoop MapReduce are switching to Spark streaming as they are under pressure from the enterprises which are asking them to compete with their rivals in real time, reduce chances of fraud and suggest insights into the customer behavior. That is only possible if one opts for Apache Spark analytics.
Indeed, the of ease of use is one of the factors which has popularized Apache Spark. Its programming model is simple and much more versatile than Hadoop MapReduce for processing big data. However, there is still a big gap between the requirements of professionals conversant in the language supported by the Spark core API and an actual number of professionals proficient in them; this has indeed opened a huge opportunity to the individuals who are keen to make a career in the big data analysis.
Spark has completely changed the way big data is analyzed and monitored. It has added to the capabilities of enterprises to monitor, take decisions on the basis of data and execute them while data is yet to enter the database.

Apache Spark is the elixir of big data today. It has completely revolutionized the way big data are handled and monitored in the present times when speed is everything. The enterprises are falling on each other to adopt Spark analytics, which gives them, the convenience of analyzing the data in the data stream itself. Even before the data enters the database, it has not only been identified, monitored, analyzed and action initiated based on the information to effect the desired changes in the real time. Indeed, that is possible due to the speed of Spark which is 100 times faster than Hadoop and gives data scientists the liberty to merge several data streams into one and that too in distributed data environment.

The Spark is indeed the most active open source project in the big data which is now being generated much faster than a few years back. The biggest challenge for enterprises is that how to translate the big data analysis into actual profit. This is where spark chips in. The number of startups which are big data centric has increased phenomenally and they are all dependent upon Spark for stream analytics. The large firms are entering the field of big data analytics after the arrival of Spark on the scene. Indeed Spark is one of the most active open source project in big data. It has surpassed Hadoop and is ruling the big data stream analytics today.

Spark Streaming makes the batch processing redundant

The reason why Spark is so much in demand is that it has revolutionized the way the big data is monitored and analyzed. Earlier, when batch processing was in vogue, data were collected and processed which took a lot of time. Often night shifts were used to analyze the data generated on that day. But Spark completely changed the way data is analyzed. Thanks to Apache Spark steaming, data is analyzed on the go, in the real time. The data which were earlier useless before entering the database data is being monitored and manipulated to add to extra bucks to the accounts of the enterprises. Spark makes it possible for instance to change the prices of flight tickets based on the user preferences of airlines, days, timings etc.

Many data, scientists who were earlier working with Storm or Hadoop MapReduce are switching to Spark streaming as they are under pressure from the enterprises which are asking them to compete with their rivals in real time, reduce chances of fraud and suggest insights into the customer behavior. That is only possible if one opts for Apache Spark analytics.

Indeed, the of ease of use is one of the factors which has popularized Apache Spark. Its programming model is simple and much more versatile than Hadoop MapReduce for processing big data. However, there is still a big gap between the requirements of professionals conversant in the language supported by the Spark core API and an actual number of professionals proficient in them; this has indeed opened a huge opportunity to the individuals who are keen to make a career in the big data analysis.

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