Apache Spark offers more than eighty high-level operators so it is excessively easy to build parallel apps. You can use Spark interactively from Python, R, and Scala shells. Spark powers various libraries such as DataFrames, SQL, MLib for use with machine learning, Spark Streaming, and GraphX.
Spark is up to one hundred times faster than Hadoop when it comes to big data processing. This is because it’s engineered from the bottom up and exploits in memory computing and various other optimizations. Spark is quick when stored on disk too, currently holding the record for large-scale on-disk sorting.
You can easily integrate these various libraries within the same application. Spark can run on Mesos, Hadoop, in the cloud, or stand-alone. It is capable of accessing a wide range of data services including Cassandra, S3, HBase, and HDFS.
Ease of Use
Apache Spark can operate on big datasets thanks to its easy-to-use APIs. This includes a collection of more than one hundred operators which make it easy to transform data, and data frame APIs which let you manipulate semi-structured data.
Nestack offers a full range of Apache Spark services. Apache Spark is a quick, general engine for use with large-scale data processing. Spark runs programs up to one hundred times faster than Hadoop MapReduce in memory, or ten times faster when on disk. These applications can be easily and quickly written in Python, R, Scala, or Java.
Apache Spark is pre-packaged with higher-level libraries. This includes excellent support for streaming data, graph processing, SQL queries, and machine learning. The libraries give you better productivity and are easily combined to make for complex workflows.