Spark is a distributed open-source cluster-computing framework and includes an interface for programming a full suite of clusters with comprehensive fault tolerance and support for data parallelism. Spark can be used effectively to provide support for Java, Scala, Python and R programming and is suitable for SQL, streaming data, processing graphs and for machine learning.
‘Businesses currently using Spark including companies in industries like financial services and banks, telecommunications, gaming companies, large technology companies.’
Spark is included in most Hadoop distributions but it has become the more popular framework for processing big data, so it is a worthy choice for many types of industries and business functions dealing with a large data volume and a need for dependable performance.
Here are four important advantages of Spark that should be considered:
Speed and Performance – Apache Spark application development is popular, in part, because Spark performs tasks up to one hundred times faster than MapReduce when applied to multi-stage jobs. The Apache Spark DAG is designed with multiple stages for more efficient distribution.
Developer-Friendly Tools – Spark API is a developer-friendly tool that provides complex distribution with a simple method call so there is no need to deal with all the levels of detail. Apache offers popular language bindings for Python and R, Java and Scala, so application developers and data scientists can leverage the scalable tools and dependable performance and speed without a lot of digging and detail.
Libraries – Apache Spark bundles libraries to support machine learning and graph analysis, including a framework for machine learning to support feature extraction, transformations, and selections for any structured data set.
Structured Streaming – This may be the most important advantage of choosing Spark because the streaming market is exploding and Spark the Spark platform supports seamless streaming with cutting-edge methods for writing and maintaining streaming code.
Businesses currently using Spark include companies in industries like financial services and banks, telecommunications, gaming companies, large technology companies.
“Spark is included in most Hadoop distributions but it has become the more popular framework for processing big data, so it is a worthy choice for many types of industries and business functions dealing with a large data volume and a need for dependable performance.”
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