Now Spark became even more popular and much substance Spark has to validate this widespread interest and popularity.Now Spark became even more popular and much substance Spark has to validate this widespread interest and popularity.
Before 2015 commenced we have experienced the rise of Apache Spark. Focus and widespread interest of clients and developers on Apache Spark quickly left behind Apache Hadoop in popularity. Now Spark became even more popular. How much substance Spark has to validate this widespread interest and popularity, we will see here.
A 2015 survey by Databricks offers a clear view of Apache Spark industry as a whole. Widely distributed adoption of Spark has already surpassed all earlier data technologies. Fore than 90% of respondents in this survey considered performance as the most important factor for using Spark. Here are some takeaways from this survey.
MapReduce has always been considered as the canonical programming model for Big Data. But time consuming sequential handling of data of this model created the impetus for developing alternate models. In this respect Spark makes by far the best alternative addressing requirements like iterations and interactivity.
Spark can utilize Hadoop file system (HDFS) from Apache Foundation, Cloudera (CDH), Hortonworks (HDP) and other contributors. Though Spark does not require HDFS to function it can work with it nevertheless. This makes it more flexible to adopt with an array of file systems.
Spark can make use of YARN from Hadoop and this makes it a flexible engine to integrate with an array of advanced platforms including IBM Platform Symphony and YARN. As Spark can be deployed but cannot be monitored or managed fully, one does not need to build it from source but need to develop it from the existing cluster.
The machine learning (MLlib) ability and graph analytics API (GraphX) basically provide to support to SQL based queries and streaming applications. Moreover, by delivering a converged analytics platform, it allows writing own codes like Java, Scala or Python. These components ultimately lead to the creation of an analytics workflow.
Enhanced efficiency in usage of random as well as machine memory is a big advantage of Apache Spark. Spark by using in-memory data processing outperforms all other data processing engines by huge speed. By offering data abstraction models like Resilient Distributed Datasets (RDD) Spark ensures optimum performance and speed with highest fault tolerance. Offering compatibility with the Hadoop paradigm, RDDs can help partitioning and placing of data sets as part of Big Data infrastructure as well.
In an array of attributes Spark offers far better output compared to the other Big Data analytics engine Hadoop. From use of binary data and in respect of instances concerning in-memory HDFS Spark beats Hadoop when the disk space is low and memory is unavailable.