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Important Use Cases In Spark-Towards Career In Apache Spark

The unpredicted & unconventional growth in the flow of data has created the urge to develop innovative technologies that can successful interpret with it.  Data ingestion, Data handling, Data recovery, Data Storage are among the major challenges created by Big Data. Today, we are going to concentrate on one of those mainstream enormous information advancements i.e., Apache Spark.

In this blog post, we will be discussing about different use cases based on the architecture where Spark is directly talking to a file system eg. HDFS:

  1. Random Access:

Random Access is a special condition & to explain it clearly let’s consider one example. Consider a case where there are billions of records and the client is keen on only one explicit record or another case where in some motion picture ticket booking application the user is keen towards viewing the transaction of ticket which he/she has booked for a particular show.

Will Spark work here? It unquestionably will. The solicitation will be put to Spark cluster for some ‘x’ record and it will initially stack every one of the records from the document framework and after that, it begins searching for that specific record requested by the user.

  1. Frequent Inserts:

Most of the experts use Spark for storing data in file systems as it clearly supports storing relatively large data sets without any critical issues. But the problem that arises here is that whenever the user inserts one record at a time using spark from that bulk of data Spark creates a new file for every insertion.

The perfect solution for this condition is to use particular bases that would support the inserts & to routinely compact your Spark SQL table files.

  1. Reporting:

Let us consider the case where there are ‘n’ number of queries coming from a certain ‘n’ number of clients of a website. So, in order to perform a query on a certain dataset using spark & then giving the files to HDFS to read may not accurately work. This process would ultimately slow down the system as all the queries gets queued up. The simple solution for this is to write out to a DB to handle the load.

  1. Searching For Content:

I guess everyone is well familiar with this is feature in Spark. The autocompletion feature in the search bar displays all the options by simply typing the first few letters there. The query for that would be something like:

sqlContext.sql(“select * from myTable where name like ‘%abc%’ “)

So, each time there’s a request on the Spark cluster for this, it will be a job and for each job, it will talk to the file system and load its content.

Get to know more about various concepts in the advanced Apache Spark technology by signing up for our expert’s driven program of Spark Training In Hyderabad.

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