• Spark allows Integration with Hadoop and
files included in HDFS.
- It has an independent language (Scala) interpreter and hence comes with an interactive language shell.
- It consists of RDD’s (Resilient Distributed Datasets), that can be cached across computing nodes in a cluster.
- It supports multiple analytic tools that are used for interactive query analysis, real-time analysis and graph processing.
Additionally,
some of the salient features of Spark include:
- Lighting fast processing: When it comes to Big Data processing, speed always matters, and Spark runs Hadoop clusters way faster than others. Spark makes this possible by reducing the number of read/write operations to the disc. It stores this intermediate processing data in memory.
- Support for sophisticated analytics: In addition to simple “map” and “reduce” operations, Spark supports SQL queries, streaming data, and complex analytics such as machine learning and graph algorithms. This allows users to combine all these capabilities in a single workflow.
