Features of PySpark

Python is very easy to learn and implement. It provides simple and comprehensive API. With Python, the readability of code, maintenance, and familiarity is far better. It features various options for data visualization, which is difficult using Scala or Java. PySpark is a general-purpose, in-memory, distributed processing engine that allows you to process data efficiently in a distributed fashion. Applications running on PySpark are 100x faster than traditional systems. Some key features are:-

  • Fault-tolerant
  • Immutable
  • Lazy evaluation
  • Cache persistence
  • Inbuild-optimization when using DataFrames
  • Supports ANSI SQL

You will get great benefits using PySpark for data ingestion pipelines. Using PySpark we can process data from Hadoop HDFS, AWS S3, and many file systems. PySpark also is used to process real-time data using Streaming and Kafka. Using PySpark streaming you can also stream files from the file system and also stream from the socket. PySpark natively has machine learning and graph libraries. It Distributed processing using parallelize and has In-memory computation. It can be used with many cluster managers (Spark, Yarn, Mesos e.t.c).

  • Real-time computations: Because of the in-memory processing in the PySpark framework, it shows low latency.
  • Polyglot: The PySpark framework is compatible with various languages such as Scala, Java, Python, and R, which makes it one of the most preferable frameworks for processing huge datasets.
  • Caching and disk persistence: This framework provides powerful caching and great disk persistence.
  • Fast processing: The PySpark framework is way faster than other traditional frameworks for Big Data processing.
  • Works well with RDDs: Python programming language is dynamically typed, which helps when working with RDDs.