PySpark online training

PySpark is the Python API for Apache Spark, an open source, distributed computing
framework and set of libraries for real-time, large-scale data processing. If you are already
familiar with Python and libraries like Pandas, then PySpark is a good language to learn to
create more scalable analyses and pipelines.
Apache Spark is a computational engine that works with huge sets of data by processing
them in parallel and batch systems. Spark is written in Scala, and PySpark was released to
assist the collaboration of Spark and Python. Additionally provides an API for Spark, PySpark
enables you interface with Resilient Distributed Datasets (RDDs) by leveraging the Py4j
library.
PySpark offers PySpark Shell which links the Python API to the spark core and begin the
Spark context. Majority of data scientists and analytics experts today utilize Python because
of its rich library set. Integrating Python with Spark is an advantage to them.

Key features of PySpark:
 Real-time Computation
PySpark offers real-time computation on a large amount of data because it focuses
on in-memory processing. It shows the low latency.
 Support Multiple Language
PySpark framework is suited with various programming languages such as Scala,
Java, Python, and R. Its compatibility makes it the better frameworks for processing
huge datasets.
 Caching and disk constancy
PySpark framework offers powerful caching and good disk constancy.
 Swift Processing
PySpark provides us to attain a high data processing speed, which is
nearly 100 times faster in memory and 10 times faster on the disk.
 Works well with RDD
Python programming language is dynamically typed, which enables when working
with RDD.