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Apache Spark has fast become the most popular analytics engine for big data and machine learning.Today’s most cutting-edge companies such as Apple, Netflix, Facebook, and Uber have deployed Spark at massive scale, processing petabytes of data to deliver innovations — from detecting fraudulent behavior to delivering personalized experiences in real-time — that are transforming every industry.
Behind these groundbreaking innovations are a small, but fast growing group of talented engineers, developers, and data scientists with deep knowledge of Apache Spark. Armed with expertise in Spark and related technologies like TensorFlow, you can change the trajectory of not only your business but also your career path.
Being able to analyse huge data sets is one of the most valuable technological skill is Apache Spark.
What is PySpark?
PySpark is a python API for spark released by Apache Spark community to support python with Spark. Using PySpark, one can easily integrate and work with RDD in python programming language too. There are numerous features that make PySpark such an amazing framework when it comes to working with huge datasets. Whether it is to perform computations on large data sets or to just analyze them, Data engineers are turning to this tool.
Key features of PySpark
Real time computations: Because of the in-memory processing in PySpark framework, it shows low latency
Polyglot: PySpark framework is compatible with various languages like Scala, Java, Python and R, which makes it one of the most preferable frameworks for processing huge datasets
Caching and disk persistence: PySpark framework provides powerful caching and very good disk persistence
Fast processing: PySpark framework is way faster than other traditional frameworks for big data processing
Works well with RDD: Python programming language is dynamically typed which helps when working with RDD.
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